Overview

» See document: http://www.cna.org/isaac/terrorism_and_cas.htm

• CAS
• The Box
• Roots of Complexity
• Principles
• Points o Interest
• Common Issues
• Planning
• Quality Mngmnt
• Adaptible Org
• Ed theory | complexity | technology

  CAS

  The Box

What's In the Box?!!??!

Definition

It operates in a non-linear way

That it is iterative (the output of one cycle  becomes the input of the next

Small variations in initial conditions lead to large  differences in outcomes

• Attractors
  The evolution of a nonlinear, dynamical, complex system can be marked by a series of phases, each of which constrains the behavior of the system to be in consonance with a reigning attractor(s). Such phases and their attractors can be likened to the stages of human development: infancy, childhood, adolescence, and so on. Each stage has its own characteristic set of behaviors, developmental tasks, cognitive patterns, emotional issues, and attitudes (although, of course, there is some variation among different people). Though a child may sometimes behave like an adult (and vice versa), the long term behavior is what falls under the sway of the attractor. Technically, in a dynamical system, an attractor is a pattern in phase or state space called a phase portrait to which values of variables settle into after transients die out. More generally, an attractor can be considered a circumscribed or constrained range in a system which seemingly underlies and "attracts" how a system is functioning within particular environmental (internal and external) conditions. The dynamics of the system as well as current conditions determine the system’s attractors. When attractors change, the behavior in the system changes because it is operating under a different set of governing principles. The change of attractors is called bifurcation, and is brought about from far-from-equilibrium conditions which can be considered as a change in parameter values toward a critical threshold.

  
  Types of Attractors:

  • Fixed Point Attractor:
  • An attractor which is a particular point in phase space, sometimes called an equilibrium point. As a point it represents a very limited range of possible behaviors in the system. For example, in a pendulum, the fixed point attractor represents the pendulum when the bob is at rest. This state of rest attracts the system because of gravity and friction. In an organization a fixed point attractor would be a metaphor for describing when the organization is "stuck" in a narrow range of possible actions.
  • Periodic (Limit Cycle) Attractor: An attractor which consists of a periodic movement back and forth between two or more values. The periodic attractor represents more possibilities for system behavior than the fixed point attractor. An example of a period two attractor is the oscillating movement of a metronome. In an organization, a periodic attractor might be when the general activity level oscillates from one extreme to another. Or, an example from psychiatry might be bi-polar disorder where a person’s mood shifts back and forth from elation to depression.
  • Strange Attractor:
  • An attractor of a chaotic system which is bound within a circumscribed region of phase space yet is aperiodic, meaning the exact behavior in the system never repeats. The structure of a strange attractor is fractal. A strange attractor can serve as a metaphor for creative activities in an organization in which innovation is possible yet there is a boundary to the activities determined by the core competencies of the organization as well as its resources and the environmental factors effecting the organization. A strange attractor portrays the characteristic of sensitive dependence on initial conditions (the Butterfly Effect) found in chaos.
  • Basins of Attraction:
  • If one imagines a complex system as a sink, then the attractor can be considered the drain at the bottom, and the basin of attraction is the sink’s basin. Technically, the set of all points in phase space that are attracted to an attractor. More generally, the initial conditions of a system which evolve into the range of behavior allowed by the attractor.
  • When a specific attractor(s) is operative in a system, the behavior of the system will be consonant with that attractor(s) meaning that a measurement of that behavior will be in the systems basin of attraction and thereby eventually converge to the attractor(s), no matter how unusual the conditions affecting the system are.
  
   
• FFE
  Far-from-equilibrium:

  
  The term used by the Prigogine School for those conditions leading to self- organization and the emergence of dissipative structures. Far-from-equilibrium conditions move the system away from its equilibrium state, activating the nonlinearity inherent in the system. Far-from-equilibrium conditions are another way of talking about the changes in the values of parameters leading-up to a bifurcation and the emergence of new attractor(s) in a dynamical system. Furthermore, to some extent, far-from-equilibrium conditions are similar to "edge of chaos" in cellular automata and random boolean networks.

  
   
• Universal Constants
  Adaptation:

  In the theory of Darwinian Evolution, adaptation is the ongoing process by which an organism becomes "fit" to a changing environment. Adaptation occurs when modifications of an organism prove helpful to the continuation of the species in a changed environment. These modifications result from both random mutations and recombination of genetic material (e.g., by means of sexual reproduction). In general, through the mechanism of natural selection, those modifications that aid in the survival of species survival are maintained. However, insights from the study of complex, adaptive systems are suggesting that natural selection operates on systems which already contain a great deal of order simply as a result of self- organizing processes following the internal dynamics of a system (Kauffman’s "order for free"). A fundamental characteristic of complex, adaptive systems is their capacity to adapt by changing the rules of interaction among their component agents. In that way, adaptation consists of "learning" new rules through accumulating new experiences.

  
   
• Adaptive Rules
   
• Anacoluthian Processes
  From the Greek "anacoluthon" (inconsistency in logic), a general term for system processes or methods facilitating self-organization and emergence. In these processes traditional procedures are followed while at the same time they are transgressed, thereby allowing the emergence of something radically new. An example of an anacoluthian process is the crossing-over of chromosomes from both parents in sexual reproduction. An example in a business or institution when people from diverse organizational functions are brought together in a project team, hopefully resulting in the emergence of an innovative organizational structure.
  
   
• Fitness Landscapes
  A "graphical" way to measure and explore the adaptive (fitness) value of different configurations of some elements in a system. Each configuration and its neighbor configurations (i.e., slight modifications of it) are graphed as lower or higher peaks on a landscape-like surface, i.e., high fitness is portrayed as mountainous-like peaks, and low fitness is depicted as lower peaks or valleys Such a display provides an indication of the degree to which various combinations add or detract from the system s survivability or sustainability. The use of fitness landscapes in understanding the behavior of complex, adaptive systems has been pioneered by Stuart Kauffman in his study of random boolean networks. An important implication from studying fitness landscapes is that there may be many local peaks or "okay" solutions instead of one, perfect, optimal solution. Thinking in terms of fitness landscapes can point to foolish adaptation, i.e., a downward trend on the slopes of the peaks. Moreover, studies of N/K models using fitness landscapes demonstrates that there is a decreasing rate of finding fitter adaptable configurations as one travels uphill on a fitness landscape. The use of fitness landscapes can be applied to gain insight into various organizational issues including which innovative organizational designs, processes, or strategies promise greater potential.
  
   
• S.I.C.
  Sensitive Dependence on Initial Conditions (SIC):

  The property of chaotic systems in which a small change in initial conditions can have a hugely disproportionate effect on outcome. SIC is popularly captured by the image of the Butterfly Effect. SIC makes chaotic systems largely unpredictable because measurements at initial conditions always will contain some amount of error, and SIC exponentially increases this error.

  
   
• Power Laws
  A type of mathematical pattern in which the frequency of an occurrence of a given size is inversely proportionate to some power (or exponent) of its size. For example, in the case of avalanches or earth quakes, large ones are fairly rare, smaller ones are much more frequent, and in between are cascades of different sizes and frequencies. Power laws define the distribution of catastrophic events in Self-organized Critical systems. According to Stuart Kauffman, systems at the "edge of chaos" will show a power law distribution, therefore, having this type of distribution can be evidence that systems are at this particularly "pregnant" phase.
  
   
• Containment
  Processes of self-organization and emergence occur within bounded regions, e.g., the container holding the Benard System so that the liquid is intact as it undergoes far-from- equilibrium conditions. In cellular automata the container is the electronic network itself which is "wrapped around" in that cells at the outskirts of the field are hooked back into the field. These boundaries or containers act to demarcate a system from its environment, and, thereby, maintain the identity of a system as it changes. Furthermore, boundaries channel the nonlinear processes at work during self-organization. In human systems, boundaries can refer to the actual physical plant, organizational policies, "rules" of interaction, and whatever serves to underlie an organization’s identity and that distinguishes an organization from its boundaries. Boundaries need to be both permeable in the sense that they allow exchange between a system and its environments as well as impermeable in so far as they circumscribe the identity of a system in contrast with its environments.
  
   
• SOC
  Self-organized Criticality (SOC):

  
  Formulated by the physicist Per Bak, a phenomena of sudden change in physical systems in which they evolve naturally to a critical state at which abrupt changes can occur. That is, when these systems are not in a critical state, i.e., they are characterized by instability, output follows from input in a linear fashion, but when in the critical state, systems characterized by self-organized criticality act like nonlinear amplifiers, similar to but not as extreme as the exponential increase in chaos due to sensitive dependence on initial conditions. That is, the nonlinear amplification in a self-organized, critical system follows a power law instead of an exponential law. SOC systems are self-organized in the sense that they reach a critical state on their own. Examples of such systems include avalanches, plate tectonics leading to earthquakes or stock market systems leading to crashes. Because SOC systems follow power laws, and because fractals also show a similar mathematical pattern then it may be the case that many naturally occurring fractals, such as tree growth, the structure of the lungs, and so on, may be generated by some form of self-organized criticality.

  
   

Roots of Complexity

The following list describes each of the scientific and mathematical disciplines displayed on the accompanying "whale" diagram. After each description are three items

1. Specific themes from complexity science running through or arising from each discipline;
2. The names of prominent researchers/theorists in each field (admittedly incomplete due to the many people who have made contributions in each field); and,
3. related terms from the glossary in this volume.

• Algorithmic Complexity | Evoltionary Bio
  Algorithmic Complexity Theory:

  One of the important sources of contemporary conceptions of what is complex about complex systems. Specifically, algorithmic complexity is a measure of complexity developed by the mathematician Gregory Chaitin based on earlier work in Information Theory founded by Claude Shannon and work on probability and information conducted by the by the Russian mathematicians Kolmogorov and Solomonoff. Algorithm complexity theory defines and measures complexity in terms of a computer algorithm (or computer program) which could generate the data coming from a particular complex system. In other words, the degree of a system's complexity is a matter of how large a computer program would be needed to generate a bit string derived from the system under question (sequence of 0's and 1's, or the binary code at the core of computer languages). Measures of complexity utilized in the study of Artificial Life and similar cellular automata have been heavily influenced by Algorithmic Complexity Theory.

  Themes: Definition and measure of complexity; relation of complexity to both randomness and order; recognition of the novelty of emergent structures; predictability and unpredictability of complex systems.

  Researchers/Theorists: Gregory Chaitin, Charles Bennett, Murray Gell-mann

  Glossary: Algorithm; Complexity (and Algorithmic Complexity); Logical Depth
  
  Artificial Intelligence (AI):

  The design of "smart" machines and robots which, obviously, have tremendous ramifications in our "Information Age." By exploring what intelligence means to humans in order to mimic it in machines, AI has been instrumental in the recent explosion of research in the cognitive processes of human beings. In addition, the development of intelligent machines has important implications for computational theory. AI has facilitated the search for basic structures of a complex system complex enough to be able to think. Consequently, AI has explored such themes as the hierarchical relationship of cognitive mechanisms, devices for simplifying or complexifying the dynamics of systems, and the elaboration of how interconnectivities effect the functioning of a complex system. Artificial Intelligence was partly spawned from earlier work in Cybernetics with servo-mechanisms, and has been influential in modern Computational Theory.

  Themes: How complex systems process information; insight into cognitive processes occurring within and between human beings; the role of hierarchy in complex systems

  Researchers/Theorists: Herbert Simon, Marvin Minsky, Roger Shank, Douglass Hofstadter, Danny Hillis. A significant and vociferous critique of some of AI's conclusions applied to human cognition has been the philosopher John Searle.

  Glossary: Complexity; Hierarchy
  
  Artificial Life:

  The study of the life-like patterns emerging in cellular automata and related electronic networks. Pioneered by the computer scientist Chris Langton, and experimented with extensively at the Santa Fe Institute. The study of Artificial Life is promising insights into natural processes leading to the build-up of structure in self-organizing, complex systems. It is closely allied with research into Random Boolean Networks (Stuart Kauffman) and Emergent Computational Theory.

  Themes: Computer simulations exhibiting self-organizing processes and emergent structures

  Researchers/Theorists: Chris Langton; Doyne Farmer; Norman Packard; Thomas Ray; William Sulis

  Glossary: Artificial Life; Cellular Automata; Boolean Networks; Emergence; Self-organization
  
  Autopoiesis:

  A theory concerning what accounts for the essence of a living organism as opposed to a nonliving entity. Developed by the Chilean scientists Humberto Maturana and Francisco Varela, the theory of autopoiesis suggests that a living organism can be understood as a circular, autocatalytic-like process having its own survival as its main goal. The phenomenon of self-organization has sometimes been understood in terms of autopoeisis. The theory's emphasis on the circular "closure" of the living organism can be seen as a "remedy" for the over emphasis on "openness" found in "open systems" theory. Theories of autopoeisis have been used in discussions of the emergent structures in Artificial Life and other cellular automata.

  Themes: How self-organizational processes require some kind of boundary or containment; the self-referential aspects of complex systems

  Researchers/Theorists: Humberto Maturana; Francisco Varela

  Glossary: Autopoiesis; Boundaries; Self-organization
  
  Boolean Networks:

  Electronic arrays developed by the medical researcher and evolutionary biologist Stuart Kauffman. These arrays are used to study self-organizing processes and the emergence of new, unexpected structures. The nodes in these arrays are connected to other nodes according to certain "boolean" or logical rules. Using the N/K Model of Boolean Networks yields insights into how manipulating the rules, the number of traits, and the number of inputs, leads to various self-organizing, emergent patterns. Of particular importance is the use of the construct of "fitness landscapes" which are graphical representations of the adaptive or fitness values of various modifications of genetic (and analogous) materials. The study of random, Boolean networks has provided important insights into how natural adaptive may occur, i.e., how innovations arise and the conditions needed to facilitate innovation.

  Themes: The dynamics of adaptation, innovation, and learning; understanding the emergence of order (Kauffman's "order for free") out of the nonlinear dynamics of the networks

  Researchers/Theorists: Stuart Kauffman; William Macready

  Glossary: N/K Model; Random Boolean Networks
  
  Catastrophe Theory:

  A mathematical theory in the field of topology formulated by the French mathematician Renee Thom. A catastrophe is a discontinuous change during the evolution of a system modeled by structural equations and topological folds. Catastrophes are governed by control parameters whose changes of values leads either to smooth transition at low values to abrupt changes at higher, critical values. Catastrophes indicate points of bifurcation in dynamical systems. Catastrophe theory provides critical insights into occurrences of abrupt change in complex systems.

  Themes: Insight into abrupt changes in complex systems

  Researchers/Theorists: Rene Thom; Christopher Zeeman; Stephen Guastello

  Glossary: Bifurcations; Catastrophes
  
  Chaos Theory:

  The study of dynamical systems characterized by sensitivity to initial conditions so that although the behavior is constrained within a particular range, the future behavior of the system is largely unpredictable. Unlike a random system which is also unpredictable, chaos is brought about by deterministic rules. Such systems are constituted by nonlinear, interactive, feedback types of relationships among the variables, components, or processes in the system. Chaos was first glimmered by the great French mathematician Henri Poincare a century ago. However, it wasn't until 1963 that the metereologist Edward Lorenz "discovered" chaos in data runs on a computer program he was using to model the dynamics of the weather. The term "chaos" was coined by the mathematicians Li and Yorke a decade later for a kind of aperiodic but bound behavior in mathematical systems of coupled differential equations. Chaos Theory has become an umbrella term for the study of many types of nonlinear, complex systems.

  Themes: How small changes can have a disproportionately large effect on a complex system; the role of attractors in understanding the behavior of complex systems; revising of the nature of the dichotomy between orderly and random

  Researchers/Theorists: Edward Lorenz; Jim Yorke; Ralph Abraham; Fred Abraham; Robert May; Doyne Farmer; Norman Packard; Robert Shaw; James Crutchfield

  Glossary: Attractors; Chaos; Sensitive Dependence on Initial Conditions
  
  Complex, Adaptive Systems Theory:

  The study of complex, nonlinear, interactive systems which have the ability to adapt to a changing environment. Such systems are characterized by the potential for self-organization and exist in a nonequilibrium environment. CAS's evolve by random mutation, self-organization, the transformation of their internal models of the environment, and natural selection. Examples include living organisms, the nervous system, the immune system, the economy, corporations, societies, and so on. The Santa Fe Institute is known as the major center in the world for the study of CAS's.

  Themes: How complex, nonlinear, interactive systems adapt to a changing environment along with other complex, adaptive systems in a co-evolutionary manner

  Researchers/Theorists: Murray Gell-mann, Brian Arthur, Chris Langton, Doyne Farmer, Norman Packard, Stuart Kauffman, John Holland, William Sulis

  Glossary: Adaptation; Complex, Adaptive Systems; Complexity

  Computational Theory:

  Research into the functioning, capabilities, and limitations of computers. Pioneered by the work of the remarkable English mathematician Alan Turing (who helped break the famous Enigma Code used by the Germans during WWII), and John von Neuman (the Hungarian born but US based mathematical prodigy), computational theory investigates such issues as the nature of algorithms, computer languages, and the applicability and usefulness of various types of computation to difficult problems in mathematics, the sciences, and other practical work with real world complex systems. A major research agenda of computational theory has been to delineate the nature of the complexity of various complex systems. Included in this is research into what defines a computable versus a noncomputable problem. Moreover, computational theory has provided us with the crucial distinction between hardware and software.

  Themes: Computability as a way of talking about the complexity of a system; a way of typing complex systems according to their ability to process information (whether in man-made computers or in the naturally-occurring systems like the brain, ecosystems, and the immune systems.

  Researchers/Theorists: Alan Turing, John von Neumann, Douglass Hofstadter, John Holland, Danny Hillis (and countless others as this has become a dominant scientific and mathematical field)

  Glossary: Church-Turing Thesis; Information; Turing Machines
  
  Condensed Matter and Solid-state Physics:

  That branch of physics having do with solid state or condensed matter exhibiting such phenomena as magnetism and modeled by such constructs as spin-glasses or how metal atoms can be modeled in terms of their electron "spins" having an influence like positive or negative feedback on neighboring atoms. The dynamics of "spin-glasses" have been influential in the formulation of Kauffman's N/K models used in his Random Boolean Networks and other complex, adaptive systems. Such models yield insight into the dynamics of interactive systems through the changing of connectivity rules and the exploration of the ensuing emergent phenomena.

  Themes: How to mode the behavior of interconnected systems in terms of coupling between components and various means for moving the system into and out of equilibrium states

  Researchers/Theorists: Philip Anderson; Daniel Stein; Richard Palmer; Bernard Derrida; Gerald Weishbuch

  Glossary: Coherence; Feedback; Parameters (Order); N/K Models
  
  Cybernetics:

  The study of control mechanisms such as thermostats, guided missile guidance systems, and other early "smart" machines. Pioneered by the mathematicians Norbert Wiener and John von Neumann, the term "cybernetics" comes from "cyber" or "steer" in Greek. Cybernetics is interested in how machines can be constructed to "steer" themselves such as in guided missiles. After World War II, Cybernetics was instrumental in the development of Artificial Intelligence and General Systems Theory. Cybernetics ideas spread to a host of other fields including physiology, neuroscience, operations research, various engineering disciplines and so on. Along the way, cybernetics became interested in machine learning and thus provided a foundation for Artificial Intelligence and was the first field where ideas of self-organization were conceived. Cybernetics has made much use of the concept of equilibrium and has conceived of self-organization in terms of the self-regulation of equilibrium-seeking systems. Furthermore, cybernetics posits the need for a "requisite variety" between the internal states of a system and the variation in its environment. In this way, cybernetics has laid important groundwork for the study of adaptation of complex systems to a complex environments.

  Themes: How systems can be understood in terms of the dynamics of negative and positive feedback; how systems can regulate their own behavior; adaptational, tranformative, and learning processes in complex systems

  Researchers/Theorists: Norbert Wiener, W. Ross Ashby, Heinz von Foerster, Arthur Burks, Gregory Bateson (applied to psychology and social systems), and Stafford Beer (applied to businesses)

  Glossary: Equilibrium; Feedback; Information
  
  Dynamical Systems Theory (and Nonlinear Dynamical Systems Theory)(NDS):

  The mathematical discipline which studies how systems evolve over time according to the dynamics of their equations. Emerging from classical mechanics, the study of differential equations, and topology, dynamical systems theory utilizes the constructs of nonlinearity, attractors, bifurcations, and phase (state) space to talk about transformations of system behavior. Dynamical systems are usually considered deterministic systems, although they can be influenced by random events. Much of the early work was done by Russian mathematicians who had a head start on the study of nonlinear dynamics. Dynamical Systems Theory has conceptualized many of the fundamental principles on which complexity sciences depend. It is the grandparent of chaos theory.

  A further development of dynamical systems theory bringing in research into systems modeled by nonlinear differential and difference equations. The mathematics of NDS were instrumental in the development of Chaos Theory, particularly the concepts of attractors, bifurcation, phase portraits, and measures of stability such as Lyapuonov Exponents. Prominent contemporary theorists include the mathematicians Steve Smale and Ralph Abraham.

  Themes: Transitions systems through different attractor regimes; how systems can be influenced by very small changes (fluctuations or perturbations)

  Researchers/Theorists: Henri Poincare; Steve Smale; Ralph Abraham; Leon Glass (biology); Ary Goldberger (medicine)

  Glossary: Attractors; Bifurcation; Catastrophes; Chaos; Initial Conditions; Stability
  
  Emergent Computation Theory:

  Research into the computational capacities of emergent structures in complex, self-organizing systems that can be used to measure the complexity of these structures. It recognizes emergent phenomena by their information processing capacity. That is, one can understand the emergent phenomena found in complex, adaptive systems by their their innate potential for processing information. Growing-out of work in Chaos Theory and Artificial Life, Emergent Computation Theory has postulated that a way to measure the complexity of a system is to ascertain what specific type of Turing Machine can be most effectively model of a complex system's time series measurements.

  Themes: Emergent structures as an intrinsic feature of complex systems to generate innovative structures

  Researchers/Theorists: James Crutchfield; Melanie Mitchell; James Hanson

  Glossary: Complexity; Information; Time Series; Turing Machines
  
  Evolutionary Biology:

  Biological theory of evolution originating in the work of Charles Darwin. It studies the process of evolution leading to the appearance and disappearance of species through the mechanisms of random mutation and natural selection of the fitter mutants. As such evolutionary biology has laid the foundation for our understanding of adaptation of living organisms to changes in their environment. These ideas of adaptation are providing a template in which to understand processes of adaptation in all complex, adaptive systems particularly the work of John Holland and Stuart Kauffman.

  Themes: How processes of adaptation can be understood as the result of random mutations, recombination of genotypes, and natural selection; the crucial role of the "edge of chaos" as a zone where adaptive experimentation may be at is optimum.

  Researchers/Theorists: Charles Darwin (and his followers); Later on Jacques Monod, Stephen Jay Gould, Richard Lewontin, Richard Dawkins, Stuart Kauffman

  Glossary: Adaptation; Edge of Chaos; Genetic Algorithms; N/K Model

  
  

  
   
• Evolutionary Systems Theory | Dynamics
  Evolutionary Systems Theory:

  Syntheses of evolutionary biology with congruent concepts from Cybernetics, General Systems Theory, and Dynamical Systems Theory, serves to integrate many fields in terms of principles of system development and transformation. The journal World Futures features articles on Evolutionary Systems Theory.

  Themes: General constructs from the theory of evolution applied across a great many complex systems; emphasis on evolutionary transformation

  Researchers/Theorists: Ervin Laszlo, Vilmos Csanyi, Rod Swenson, Sally Goerner

  Glossary: Bifurcation; Chaos; Complexity; Complex, Adaptive Systems
  
  Far-from-equilibrium Thermodynamics:

  The study of self-organization in physical systems founded by the Russian-born Belgian physical chemist Ilya Prigogine, winner of the Nobel Prize in chemistry. Self-organization has been studied from a thermodynamics perspective considering the relation between the build-up of structure seen in thermodynamics versus the supposed tendency of an increase of entropy (from the Second Law of Thermodynamics) to tear down form. Self-organizing, emergent patterns are termed "dissipative structures." Many of the ideas are revisions of earlier thermodynamic concepts applied to the build-up of organization in a physical systems.

  Themes: Self-organization understood as a process occurring in a nonlinear system at a far-from-equilibrium system; how complex systems can take advantage of random events in the build-up of new forms

  Researchers/Theorists: Ilya Prigogine; Gregoire Nicolis

  Glossary: Dissipative Structures; Equilibrium; Far-from-equilibrium; Self-organization
  
  Fractal Geometry:

  A geometrical pattern or set of points which is self-similar on different scales. The geometry of this pattern does not fall within the normal whole dimensions one, two, or three. Instead, a fractal is "in-between" one and two or two and three and so on dimensions. For example, the coast of England can be understood as a fractal, because as you observe from closer and closer points of view (i.e., changing the scale) it keeps showing a self-similar kind of irregularity. Fractal dimensionality is one way to measure the complexity of a dynamical system. Furthermore, strange attractors have a fractal dimensionality. Fractals have become popular through the amazing imagery of graphical depicted Mandelbrot or Julia Sets. The study of Fractal Geometry has been a great aide in discovering universal principles in complex systems, scaling phenomena being one of these. Moreover, fractals can represent power law distributions.

  Themes: Understanding aspects of complexity in terms of repeated irregularities on different scales; the benefits conferred on a system from having a fractal structure

  Researchers/Theorists: Benoit Mandelbrot; Michael Barnsley

  Glossary: Attractor; Chaos; Complexity; Fractal
  
  Game Theory:

  Originally developed by the great mathematician John von Neumann and the economist Oscar Morgenstern, Game Theory explores the various outcomes when interactive, semi-autonomous agents engage in either cooperative and noncooperative behavior. For example, in the famous Prisoner's Dilemma Game, two agents or "players" are arrested for armed robbery, and the different outcomes of their resulting cooperative or noncooperative strategies in the face of the district attorney's deal-making are assessed. Game Theory constructs are helpful in understanding the global effect of local "rules" (i.e., the various strategies used by the agents), and thereby, it is another complementary framework for understanding adaptation.

  Themes: Emergence of global patterns in complex systems according to the rules or strategies followed by interactive agents

  Researchers/Theorists: John von Neumann; Oscar Morgenstern; Bernardo Huberman; Natalie Glance; Robert Axelrod

  Glossary: Adaptation; Emergence
  
  General Systems Theory:

  Following from earlier work in Cybernetics, Information Theory, and Evolutionary Biology, General Systems Theory attempted to search for general principles of system across diverse scientific disciplines. As such, it provides a precursor to the similar search for general principles in Complex, Adaptive Systems Theory. Key ideas include negative feedback, stability, equilibrium-seeking, self-regulation, and "open systems" referring to the need for vital systems to be in active exchange with their environments.

  Themes: The search for general principles of the dynamics of living and other complex systems

  Researchers/Theorists: Ludwig von Bertalanffy

  Glossary: Adaptation; Equilibrium; Self-organization
  
  Genetic Algorithms:

  A type of computer program developed by the computer scientist John Holland whose strategy of arriving at solutions is based on principles taken from genetics. Basically, the genetic algorithm utilizes the mixing of genetic information in sexual reproduction, random mutations, and natural selection at arriving at solutions. The use and study of genetic algorithms has been instrumental in the development of a more general Complex, Adaptive Systems Theory.

  Themes: Inquiry into principles of learning and adaptation; designing evolving computer programs

  Researchers/Theorists: John Holland

  Glossary: Attractor; Chaos; Complexity; Fractal
  
  Information Theory:

  Formulated during and after World War II, Information Theory focussed on measurements of the amount of information a communications channel could contain. "Information" refers to the degree of variety versus redundancy capable of being transmitted electronically. Information Theory has been a keystone in the development of the study of self-organization and complexity as well as computational theory. Self-organization can be conceptualized in Information Theory in terms of the paradoxical nature of "noise" (or random fluctuations or perturbations) as either disorganizing or organizing. Complex systems can be understood as information-processing mechanisms. Information is now being used as a general concept linking all types of systems physical, social, computational.

  Themes: Information can be seen as the cognate in a social system as what energy is in a physical system; search for general principles of information across many types of systems

  Glossary: Information; Novelty; Redundancy

  Researchers/Theorists: Claude Shannon; Norbert Wiener; Henri Atlan (biology)
  
  Neural Nets:

  An outgrowth of Artificial Intelligence, Neural Nets are electronic automata used to for machine learning that are based on associative theories of human cognition. Using various algorithms, they are often programmed to learn how to recognize a pattern. Changing the rules of interaction between the "neurons" in the network can lead to interesting emergent behavior, so in that way, neural nets are another tool for investigating self-organization and emergence. Many believe neural nets are a better model of the way the living brain works than the operation of digital computers. The investigation of neural nets is providing a great many insights into emergent patterns in complex systems. Moreover, the study of neural net pattern recognition is providing insight into how the brain may function in its perception of patterns in the environment.

  Themes: Another example of complex systems composed of interacting semi-autonomous agents that can adapt and learn; insight into pattern recognition in complex system and the build-up of internal models

  Researchers/Theorists: J.J. Hopfield; T. J. Sejnowski

  Glossary: Adaptation; Genetic Algorithms; Neural Nets
  
  Self-organized Criticality (SOC):

  Research and theorizing about natural, abrupt changes formulated by the physicist Per Bak. Systems are viewed as evolving naturally, in a self-organizing manner, to a critical state at which abrupt changes can occur which abrupt changes can occur. Examples of such systems include plate tectonics leading to earthquakes, avalanches, sudden stock market dips or surges as well as crashes, and so on. By considering such systems "weakly chaotic" and exploring them in terms of power laws, Per Bak has contrasted them with "strongly chaotic" systems. Some of the themes of SOC have been incorporated by Stuart Kauffman into his ideas on the "edge of chaos."

  Themes: Understanding of abrupt, cascading or "avalanche" type of change in a complex system; another picture of systems being in a poised state or readiness of change; Information can be seen as the cognate in a social system as the energy in a physical system.

  Researchers/Theorists: Per Bak; Chao Tang; Kurt Wiesenfeld

  Glossary: Bifurcation; Chaos; Power Law; Self-organization; Stability
  
  Synergetics:

  The study of self-organizing systems initiated by the German physicist Hermann Haken, who did early research on the emergence of coherence in lasers and other emergent phenomena in physical systems. Synergetics emphasizes the exploration of order parameters which move the focus of studying complex systems from the lower level of components up to the level of the emergent structures. The term "Synergetics" has become roughly synonymous to complexity science in Europe and Russia.

  Themes: Understanding emergent phenomena in terms of the order parameters determining their coherent structure

  Researchers/Theorists: Hermann Haken

  Glossary: Coherence; Parameter (Order); Self-organization
  
  System Dynamics:

  Understanding the dynamics of complex systems in terms of a network of interlocking negative and positive feedback loops, e.g., how the functions of production, inventory, ordering, and shipping are interrelated. Diagramming complex systems with the visual aides of System Dynamics can help in indicating how changes will effect other parts or subsystems of the system. System Dynamics also provides practice in thinking systemically about systems, i.e., conceiving of the overall "holistic" interaction of components. System Dynamics has been influenced by Cybernetics and General Systems Theory, and more recently has included some elements of Dynamical Systems Theory and Complex, Adaptive Systems Theory, Synergetics, and Far-from-equilibrium Thermodynamics.

  Themes: Another way to conceptualize complex systems as interacting semi-autonomous units influencing one another through positive and negative feedback

  Researchers/Theorists: Jay Forrester; George Richardson; Peter Senge

  Glossary: Feedback

  
  

  
   

Principles

• "Nine Emerging & Connected Principles"
    

   

   

   Nine emerging and connected organizational and leadership principles.

  
  Some introductory thoughts

  
  Our study of the science of complex adaptive systems and our work with health care organizations in VHA has led us to propose some principles of management that are consistent with an understanding of organizations as CASs. In the spirit of the subject matter, there is nothing sacred or permanent about this list. However, these principles do begin to give us a new way of thinking about and approaching our roles as leaders in organizations.

  
  We are not the first to propose such a list. Our intent here is to capture practical principles that emerge from the science of complexity in language that resonates with management issues. Furthermore, astute readers will also observe that our list of principles, and CAS theory itself, has much in common with general systems thinking, the learning organization, total quality, empowerment, gestalt theory, organizational development and other approaches. It has much in common with these, but it is not any of these. CAS theory clarifies and pulls together many aspects of good thinking from the past. An understanding of CAS is an understanding of how things work in the real world. That others in the past have also understood these things and put them into various contextual frames should not be surprising. An understanding of CAS simply provides a broader, more fundamental, potentially unifying framework for these ideas.

   

    

  
  
  --------------------------------------------------------------------------------

  
  The Nine Principles:

   

  View your system through the lens of complexity.

  Build a good-enough vision

  When life is far from certain, lead with clockware and swarmware in tandem

  Tune your place to the edge

  Uncover and work with paradox and tension

  Go for multiple actions at the fringes, let direction arise

  Listen to the shadow system

  Grow complex systems by chunking

  Mix cooperation with competition

  
  
  --------------------------------------------------------------------------------

   

   

     

  
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  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Complexity Lens
    

   

   

     Nine emerging and connected organizational and leadership principles.

  
  Some introductory thoughts

  
  Our study of the science of complex adaptive systems and our work with health care organizations in VHA has led us to propose some principles of management that are consistent with an understanding of organizations as CASs. In the spirit of the subject matter, there is nothing sacred or permanent about this list. However, these principles do begin to give us a new way of thinking about and approaching our roles as leaders in organizations.

  
  We are not the first to propose such a list. Our intent here is to capture practical principles that emerge from the science of complexity in language that resonates with management issues. Furthermore, astute readers will also observe that our list of principles, and CAS theory itself, has much in common with general systems thinking, the learning organization, total quality, empowerment, gestalt theory, organizational development and other approaches. It has much in common with these, but it is not any of these. CAS theory clarifies and pulls together many aspects of good thinking from the past. An understanding of CAS is an understanding of how things work in the real world. That others in the past have also understood these things and put them into various contextual frames should not be surprising. An understanding of CAS simply provides a broader, more fundamental, potentially unifying framework for these ideas.

   

    

  
  
  --------------------------------------------------------------------------------

  
  The Nine Principles:

   

  View your system through the lens of complexity.

  Build a good-enough vision

  When life is far from certain, lead with clockware and swarmware in tandem

  Tune your place to the edge

  Uncover and work with paradox and tension

  Go for multiple actions at the fringes, let direction arise

  Listen to the shadow system

  Grow complex systems by chunking

  Mix cooperation with competition

  
  
  --------------------------------------------------------------------------------

   

   

     

  
  Next | Previous | Return to Contents List

  
  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Good Enough Vision
    

   

   

    Build a good-enough vision

  Provide minimum specifications, rather than trying to plan every little detail.

  
  
   

  
  Tales

  Emerges from fabric

  Worldwide complexity

  Make it or let it

  
  Bibliography

  Morgan:

  Images

  Stacey:

  Unknowable

  Waldrop:

  Trillion

  
  Aides

  Min specs

  Generative relationships

    Since the behavior of a CAS emerges from the interaction among the agents, and since the detailed behavior of the system is fundamentally unpredictable, it does little good to spend all the time that most organizations spend in detailed planning. Most organizational leaders have participated in very detailed planning, only to find that assumptions and inputs must be changed almost immediately after the plan is finalized. Complexity science suggests that we would be better off with minimum specifications and general senses of direction, and then allow appropriate autonomy for individuals to self-organize and adapt as time goes by. The science behind this principle traces it roots back to a computer simulation called “Boids,” developed in 1987 by Craig Reynolds. The simulation consists of a collection of autonomous agents – the boids – in a environment with obstacles. In addition to the basic laws of physics, each agent follows three simple rules: (1) try to maintain a minimum distance from all other boids and objects; (2) try to match speed with neighboring boids; and, (3) try to move toward the center of mass of the boids in your neighborhood. Remarkably, when the simulation is run, the boids exhibit the very lifelike behavior of flying in flocks around the objects on the screen. They flock, a complex behavior pattern, even though there is no rule explicitly telling them to do so. While this does not prove that birds actually use these simple rules, it does show that simple rules – minimum specifications – can lead to complex behaviors. These complex behaviors emerge from the interactions among agents, rather than being imposed upon the CAS by an outside agent or an explicit, detailed description.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "The principle of min specs [minimum specifications] suggests that managers should define no more than is absolutely necessary to launch a particular initiative or activity on its way. They have to avoid the role of ‘grand designer’ in favor of one that focuses on facilitation, orchestration and boundary management, creating ‘enabling conditions’ that allow a system to find its own form."

  -Morgan

  
  --------------------------------------------------------------------------------

   

  
  In contrast, we often over-specify things when designing or planning new activities in our organizations. This follows from the paradigm of “organization as a machine.” If you are designing a machine, you had better think of everything, because the machine cannot think for itself. Of course, in some cases, organizations do act enough like machines to justify selected use of this metaphor. For example, if you are having your gall bladder removed, you’d like the surgical team to operate as a precision machine; save that emerging, creative behavior for another time! Maximum specifications and the elimination of variation might be appropriate in such situations.

  
  
  
  Most of the time, however, organizations are not machine-like; they are complex adaptive systems. The key learning from the simulations is that in the case of a CAS, minimum specifications and purposeful variation are the way to go.

  
  
  
  This principle would suggest, for example, that intricate strategic plans be replaced by simple documents that describe the general direction the organization is pursuing and a few basic principles for how the organization should get there. The rest is left to the flexibility, adaptability and creativity of the system as the context continually changes. This, of course, is a frightening thought for leaders classically trained in the machine and military metaphors. But the key questions are: Are these traditional metaphors working for us today? Are we able to lay out detailed plans and then just do it with a guaranteed outcome? If not, do we really think that planning harder will be any better?

  
  
  
  The quintessential organizational example of this principle of good-enough vision and minimum specifications is the credit-card company, Visa International. Despite its $1 trillion annual sales volume and roughly half-billion clients, few people could tell you where it is headquartered or how it is governed. It’s founding chief executive officer, Dee Hock describes it as a nonstock, for-profit membership corporation in which members (typically, banks that issue the Visa cards) cooperate intensely “in a narrow band of activity essential to the success of the whole” (for example, the graphic layout of the card and common clearinghouse operations), while competing fiercely and innovatively in all else (including going after each other’s customers!). This blend of minimum specifications in the essential areas of cooperation, and complete freedom for creative energy in all else, has allowed Visa to grow 10,000 percent since 1970, despite the incredibly complex worldwide system of different currencies, customs, legal systems and the like. “It was beyond the power of reason to design an organization to deal with such complexity,” Hock explained. “The organization had to be based on biological concepts to evolve, in effect, to invent and organize itself.”

  
  
  
  
  --------------------------------------------------------------------------------

  
  "Managers therefore cannot form a vision of some future state toward which the business can be moved; the futures open to the system are too many, and the links between a future and the actions leading to it are too obscure. Chaotic dynamics lead us to see strategy as a direction into the future that emerges from what managers do. In chaotic conditions, strategy cannot be driven by pure intentions. Instead, it represents the unintentional creation of order out of chaos."

  -Stacey

  
  --------------------------------------------------------------------------------

   

   

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  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Clockware / Swarmware
    

   

   

    When Life Is Far From Certain, Lead With Clockware And Swarmware In Tandem.

  Balance data and intuition, planning and acting, safety and risk, giving due honor to each.

  
   

   

  
  Tales

  Wizards & CEOs

  Another way to think

  A complexity tool box

  Emerges from the fabric

  Make it or let it

  
  
  Bibliography

  Kelly:

  Control

  Stacey:

  Unknowable

  Zimmerman:

  Chaos

  
  
  Aides

  Stacey matrix

    “Clockware” is a term, coined by Kevin Kelly, that describes the management processes we all know that involve operating the core production processes of the organization in a manner that is rational, planned, standardized, repeatable, controlled and measured. In contrast, Kelly’s term “swarmware” refers to management processes that explore new possibilities through experimentation, trials, autonomy, freedom, intuition and working at the edge of knowledge and experience. Good-enough vision, minimum specifications and metaphor are examples of swarmware that we have already seen. The idea is to say just enough to paint a picture or describe the absolute boundaries, and then let the people in the CAS become active in trying whatever they think might work.

  
  
  --------------------------------------------------------------------------------

  "For jobs where supreme control is demanded, good old clockware is the way to go. Where supreme adaptability is required, out-of-control swarmware is what you want."

  –Kelly

  
  "Cohesive teams are needed for day-to-day issues. Spontaneous learning networks that have open conflict and dialogue are vital to handling strategic issues."

  –Stacey

  --------------------------------------------------------------------------------

   

  In an informed approach to complexity, it is not a question of saying that one is good and the other is bad. The issue is about finding an appropriate mix for a given situation. Where the world is certain and there is a high level of agreement among agents (for example, the need for consistent variable names and programming language syntax in a large software system, or the activities in the operating room during a routine surgery) clockware is appropriate. In a clockware situation, agents give up some of their freedom and mental models to accomplish something they have collectively agreed upon. The CAS displays less emergent, creative behavior, and begins to act more like a machine. There is nothing wrong with this.

  
  However, where the world is far from certainty and agreement (near the edge of chaos) swarmware is needed with its adaptability, openness to new learning and flexibility. Swarmware is also needed in situations for which the old clockware processes are no longer adequate for accomplishing the purpose, in situations for which the purpose has changed or in situations in which creativity is desirable for its own sake.

  
   

  
   

    Next | Previous | Return to Contents List

  
  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Tune To the Edge
    

   

   

    Tune your place to the edge.

  Foster the "right" degree of information flow, diversity and difference, connections inside and outside the organization, power differential and anxiety, instead of controlling information, forcing agreement, dealing separately with contentious groups, working systematically down all the layers of the hierarchy in sequence and seeking comfort.

  
  
   

  
  Tales

  Another way to think

  Power of information

  
  
  
  Bibliography

  Kauffman:

  At Home

  Waldrop:

  Complexity

  Stacey:

  Creativity

  Goldberger:

  Non-linear dynamics

  
  Aides

  Wicked Questions

  Metaphor

  Stacey matrix

  Generative relationships

  reflection

    Theoretical studies of complex adaptive systems suggest that creative self-organization occurs when there is just enough information flow, diversity, connectivity, power differential and anxiety among the agents. Too much of any of these can lead to chaotic system behavior; too little and the system remains stuck in a pattern of behavior.

  
  
  
  Again, we can look to biological sciences for a dramatic illustration of this principle. Dr. Ary Goldberger is a cardiac specialist at Harvard Medical School who has done much research in the role of complexity in physiologic systems such as the beat-to-beat record of a healthy heart. It shows an irregular, wrinkly appearance – not a smooth, regular tracing. Furthermore, when this tracing is magnified, there is even more wrinkly detail. This complex pattern of irregular fluctuations is a fractal. Surprisingly, if you were to view an equally detailed heart-rate tracing of a patient before cardiac arrest, you would probably not see more chaotic activity, as you might expect, but rather virtual consistency and regularity. Thus, predictable and regular activity can lead to a heart attack; unpredictability and fractal (chaotic-like) variability are associated with health and stability. (Note that this pattern can also be observed in other biological systems: in sleep, chaotic patterns have been shown to produce restful sleep and extreme regularity may indicate a coma; and in muscles, chaos indicates healthy functioning and stability indicates seizure or degenerative disease.)

  
  
  
  Of course, the trick in a human CAS lies in gauging the “right” amount of information flow, diversity, connectivity, power differential and anxiety among the agents. Since the predominant metaphors of organizational life are those of a machine and military operation, most organizations today have too little information flow and diversity, and too much power differential. The degree of connectivity and anxiety can go either way. This is a general observation that could of course be different in any specific context. If you are in a CAS, you will have your own mental model about such things, as will the other agents in the system.

  
  
  
  
  
  Since the detailed behavior of a CAS is fundamentally unpredictable, there is no way to arrive analytically at an answer for what amount of information flow, diversity, connections inside and outside the organization, power differential and anxiety among the agents is proper.

  
  
  
  You can have more- or less-correct intuitions, and some sense of general direction, but that’s inherently the best you can do. You’ll just have to try tuning up or down the various factors and reflect on what happens.

  
  
   

  
  Aides

  Reflection

  
    Reflection is, therefore, a key skill for anyone in a CAS. Good leaders in a CAS lead not by telling people what to do, but by being open to experimentation, followed by thoughtful and honest reflection on what happens.

  
  
  --------------------------------------------------------------------------------

  
  "At the ideal number of connections, the ideal amount of information flows between agents, and the system as a whole finds optimal solutions consistently … which in a rapidly changing environment allows the whole to persist."

  –Kauffman

  
  "Living systems are very close to the edge of chaos phase transitions where things are loose and fluid … Systems that are most adaptive are so loose they are a hairbreadth away from [being] out of control."

  –Waldrop

  
  "The emphasis on managing long-term specific outcomes is completely misplaced. They cannot be managed, but it is possible to influence control parameters...managers still need strategic plans; however, they relate not to outcomes and actions to achieve them, but to methods of affecting anxiety, power, difference, and connectivity."

  -Stacey

  
  --------------------------------------------------------------------------------

   

  
   

   

    Next | Previous | Return to Contents List

  
  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Paradox | Tension
    

   

   

    Tune your place to the edge.

  Foster the "right" degree of information flow, diversity and difference, connections inside and outside the organization, power differential and anxiety, instead of controlling information, forcing agreement, dealing separately with contentious groups, working systematically down all the layers of the hierarchy in sequence and seeking comfort.

  
  
   

  
  Tales

  Another way to think

  Power of information

  
  
  
  Bibliography

  Kauffman:

  At Home

  Waldrop:

  Complexity

  Stacey:

  Creativity

  Goldberger:

  Non-linear dynamics

  
  Aides

  Wicked Questions

  Metaphor

  Stacey matrix

  Generative relationships

  reflection

    Theoretical studies of complex adaptive systems suggest that creative self-organization occurs when there is just enough information flow, diversity, connectivity, power differential and anxiety among the agents. Too much of any of these can lead to chaotic system behavior; too little and the system remains stuck in a pattern of behavior.

  
  
  
  Again, we can look to biological sciences for a dramatic illustration of this principle. Dr. Ary Goldberger is a cardiac specialist at Harvard Medical School who has done much research in the role of complexity in physiologic systems such as the beat-to-beat record of a healthy heart. It shows an irregular, wrinkly appearance – not a smooth, regular tracing. Furthermore, when this tracing is magnified, there is even more wrinkly detail. This complex pattern of irregular fluctuations is a fractal. Surprisingly, if you were to view an equally detailed heart-rate tracing of a patient before cardiac arrest, you would probably not see more chaotic activity, as you might expect, but rather virtual consistency and regularity. Thus, predictable and regular activity can lead to a heart attack; unpredictability and fractal (chaotic-like) variability are associated with health and stability. (Note that this pattern can also be observed in other biological systems: in sleep, chaotic patterns have been shown to produce restful sleep and extreme regularity may indicate a coma; and in muscles, chaos indicates healthy functioning and stability indicates seizure or degenerative disease.)

  
  
  
  Of course, the trick in a human CAS lies in gauging the “right” amount of information flow, diversity, connectivity, power differential and anxiety among the agents. Since the predominant metaphors of organizational life are those of a machine and military operation, most organizations today have too little information flow and diversity, and too much power differential. The degree of connectivity and anxiety can go either way. This is a general observation that could of course be different in any specific context. If you are in a CAS, you will have your own mental model about such things, as will the other agents in the system.

  
  
  
  
  
  Since the detailed behavior of a CAS is fundamentally unpredictable, there is no way to arrive analytically at an answer for what amount of information flow, diversity, connections inside and outside the organization, power differential and anxiety among the agents is proper.

  
  
  
  You can have more- or less-correct intuitions, and some sense of general direction, but that’s inherently the best you can do. You’ll just have to try tuning up or down the various factors and reflect on what happens.

  
  
   

  
  Aides

  Reflection

  
    Reflection is, therefore, a key skill for anyone in a CAS. Good leaders in a CAS lead not by telling people what to do, but by being open to experimentation, followed by thoughtful and honest reflection on what happens.

  
  
  --------------------------------------------------------------------------------

  
  "At the ideal number of connections, the ideal amount of information flows between agents, and the system as a whole finds optimal solutions consistently … which in a rapidly changing environment allows the whole to persist."

  –Kauffman

  
  "Living systems are very close to the edge of chaos phase transitions where things are loose and fluid … Systems that are most adaptive are so loose they are a hairbreadth away from [being] out of control."

  –Waldrop

  
  "The emphasis on managing long-term specific outcomes is completely misplaced. They cannot be managed, but it is possible to influence control parameters...managers still need strategic plans; however, they relate not to outcomes and actions to achieve them, but to methods of affecting anxiety, power, difference, and connectivity."

  -Stacey

  
  --------------------------------------------------------------------------------

   

  
   

   

    Next | Previous | Return to Contents List

  
  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Multiple Actions
    

   

   

    Go for multiple actions at the fringes, let direction arise.

  You don’t have to be "sure" before you proceed with anything.

  
  
   

  
  Tales

  Worldwide complexity

  Learn as you go

  
  
  Bibliography

  Kelly:

  Control

  Stacey:

  Unknowable

  Nohira:

  Action

  Morgan:

  Images

  
  
  Aides

  Reflection

  Min specs

  Stacey matrix

    As we have already noted, in a CAS it does little good to plan the details. You can never know exactly what will happen until you do it. So, allowing the flexibility of multiple approaches is a very reasonable thing to do. Of course, such a flexible approach is unreasonable when we view the situation through the metaphor of a machine or military organization. A machine can work only one way, and an old-style military organization must follow procedures and regulations.

  
  
  
  The science that supports this principle of CAS behavior comes primarily from the study of gene pools in evolutionary biology. David Ackley points outs that, “Researchers have shown clearly and unequivocally how populations of organisms that are learning (that is, exploring their fitness possibilities by changing behavior) evolve faster than populations that are not learning.” We do not think it strains the metaphor here to suggest that our managerial instincts to drive for organizational consensus around a single option might be equivalent to inbreeding in a gene pool. And we all know the kinds of dysfunction that inbreeding in nature can spawn. We are personally struck by the fact that even though the words “organization” and “organism” have a common root, we have learned to think about them in such remarkably different ways.

  
  
  
  The fringes that we are referring to here are the issues that are far from the zone of certainty and agreement. Recall that we pointed out that it was not a question of the machine metaphor being wrong and the CAS metaphor being right, nor is it about throwing out clockware and replacing it with swarmware. Neither approach is inherently right or wrong; but either approach can be inappropriate and ineffective in a given context. The leadership skill lies in the intuition to know which approach is needed in the context one is in. The degree of certainty and agreement is a good guide.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "A healthy fringe speeds adaptation, increases resilience and almost always is the source of innovations."

  -Kelly

  
  
  --------------------------------------------------------------------------------

   

  However, when we do find ourselves in situations far from certainty and agreement, the management advice contained in this principle is to quit agonizing over it, quit trying to analyze it to certainty. Try several small experiments, reflect carefully on what happens and gradually shift time and attention toward those things that seem to be working the best (that is, let direction arise). These multiple actions at the fringes also serve the purpose of providing us with additional insights about the larger systems within which every system is inevitably buried.

  
  
  
  A concrete example of this principle is the health care organization that is trying to come up with a new financial incentive plan for physicians. There are many options, with success and failure stories for each one. Therefore, we are far from certainty and agreement. Rather than meeting endlessly over it trying to pick the right approach, experiment with several approaches. See what happens, see what seems to work and in what context. Over time, you may find a right way for you, or you may find several right ways.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "Successful experiments can go a long way in creating a foothold in a new reality. In particular, they offer important insights on the feedback loops and defensive routines that sustain a dominant attractor pattern and what can be done to help a new one to emerge."

  -Morgan

  
  
  --------------------------------------------------------------------------------

   

  
   

   

    Next | Previous | Return to Contents List

  
  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Shadow System
    

   

   

    Listen to the shadow system.

  That is, realize that informal relationships, gossip, rumor and hallway conversations contribute significantly to agents’ mental models and subsequent actions.

  
  
   

  
  Tales

  What we could be doing

  
  
  Bibliography

  Stacey:

  Creativity

  
  
  Aides

  Reflection

    Complexity theorist Ralph Stacey points out that every organization actually consists of two organizations: the legitimate and shadow systems. Everyone in an organization is part of both. The legitimate system consists of the formal hierarchy, rules and communications patterns in the organization. The shadow organization lies behind the scenes. It consists of hallway conversation, the grapevine, the rumor mill and the informal procedures for getting things done. Most traditional management theory either ignores the shadow system, or speaks of it as something leaders must battle against (as in, “overcome resistance to change” – it’s that military metaphor again).

  
  
  
  Stacey further points out that because the shadow system harbors such diversity of thought and approach, it is often the place where much of the creativity resides within an organization. While the legitimate system is often focused on procedures, routines and the like, the shadow system has few rules and constraints. The diversity, tension and paradox of these two organizations that coexist within one can be a great source of innovation if leaders could just learn to listen to, rather than battle against, the shadow.

  
  
  
  One health care executive entered the shadow system when he joined a group of doctors and nurses talking in the cafeteria one day. He was so fascinated by their discussion of improving the process for delivering anti-coagulants, he soon became part of this underground ad-hoc team. In doing so, he quietly sidestepped the difficult, formal process for approving quality improvement projects instituted by the hospital. The resulting work was so successful, it led to a close re-examination of the approval process that had been unintentionally discouraging such innovation.

  
  
  
   

  
  When we see our organizations as CASs, we realize that the shadow system is just a natural part of the larger system. It is simply more interconnections among agents, often stronger interconnections than those in the legitimate system. Leaders who lead from an understanding of CASs, will not have a need to discredit, agonize over, or combat the shadow systems in their organizations. Rather, they will recognize and listen to the shadow organization, using the interconnections it represents as another avenue for tuning information flow, diversity of opinion, anxiety, and power differential.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "When the legitimate and shadow system operate against each other, an organization is in the phase transition at the edge of chaos; it is only here that it is changeable, because it is only here that it is capable of double-loop learning …. When an organization is in this state, at least some of its members play by engaging in exploratory dialogue, utilizing analogies and metaphors, and employing self-reflection to develop new knowledge …. If this change is then amplified throughout the organization to become the dominant schema of the organization, potential innovation has occurred."

  –Stacey

  
  
  --------------------------------------------------------------------------------

   

   

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  All Components of Edgeware Principles Copyright © 2001, Curt

  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   
• Chunking
  
    Grow complex systems by chunking.

  
  Allow complex systems to emerge out of the links among simple systems that work well and are capable of operating independently.

  
  
   

  
  Tales

  What we could be doing

  
  
  Bibliography

  Waldrop:

  Complexity

  Holland:

  Hidden

  Kelly:

  Control

    Question: Who built the Internet?

  
  
  
  That’s an easy one. The answer, we all know, is no one. Not Bill Gates or any other computer genius. The Internet is our most visible and oft-cited example of emergent phenomena, an elegant case study of how a complicated and vastly diverse system can self-organize … in this case, almost overnight. On close examination, we see that the Internet evolved in chunks – like a set of building blocks – with components being integrated into the system only after they had been individually refined, proven and accepted by a collective, systemic jury.

  
  
  Complex systems are … well, complex. They are not easily understood or built in detail from the ground up. Chunking means that a good approach to building complex systems is to start small. Experiment to get pieces that work, and then link the pieces together. Of course, when you make the links, be aware that new interconnections may bring about unpredicted, emerging behaviors.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "Interesting, beguilingly complex behavior can emerge from collections of extremely simple components."

  –Waldrop

  
  "A scan of history shows that technical innovations almost always arise as a particular combination of well-known building blocks. "

  –Holland

  
  
  --------------------------------------------------------------------------------

   

  This principle is the basis upon which genetic evolution proceeds. Building blocks of organism functionality (for example, webbed feet on a bird) develop and are combined through crossover of genetic material with other bits of functionality (for example, an oversized bill more suitable for scooping fish out of the water) to form increasingly complex organisms (a pelican). The good-enough genetic combinations may survive and are then available as building blocks for future combinations. The UNIX computer operating system is another good example of an ever-evolving complex system that was built from chunks. The basic – and at the time it was introduced, revolutionary – principle behind the UNIX system is that software functions should be small, simple, standalone bits of code that do only one thing well, embedded in an environment that makes it easy for each such function to pass its output on to another function for further processing.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "The only way to make a complex system that works is to begin with a simple system that works. Attempts to instantly install highly complex organization … without growing it, inevitably lead to failure.To assemble a prairie takes time – even if you have all the pieces.Time is needed to let each part test itself against all the others. Complexity is created, then, by assembling it incrementally from simple modules that can operate independently. "

  -Kelly

  
  
  --------------------------------------------------------------------------------

   

  Applying this principle to teambuilding in a mid-sized organization, for example, would suggests that leaders should look for and support small natural teams. We might provide coaching and training for these teams. Then, when these teams are functioning well, look for ways to get the teams to work together and involve others. These new links may result in weird behavior; with a CAS, this is to be expected. The leaders should be open to doing some adapting of their own. Rather than insisting on pressing forward with the training, ground rules, or procedures that worked so well in the first teams, the leaders should understand that the interconnections among teams has resulted in a fundamentally new system that may need new approaches.

  
  
  
  Continual reflection and learning are key in building complex systems. You cannot reflect on anything until you do something. So start small, but do start.

  
   

   

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• Cooperation / Competition
    

   

   

    Mix cooperation with competition.

  It’s not one or the other.

  
  
   

  Bibliography

  Axelrod:

  Cooperation

  Waldrop:

  Trillion Dollar

  Goodwin:

  Leopard

    Nature competes. If you have ever glimpsed a lion stalking and devouring an elk on a PBS program before quickly changing the channel, you know this to be true.

  
  
  
  Nature cooperates, too. Observe members of an ant colony working together to produce intricate ant-mound societies.

  
  
  
  These dynamics are not mutually exclusive. Natural and biological systems display both cooperation and competition. And so can corporate, business and sociological systems.

  
  
  
  Perhaps no one has explored this paradox with more vigor – or success – than Dee Hock, former chief executive officer of Visa International. The corporation’s growth averages around 20 percent annually; it serves around a half-billion clients in more than 200 countries; sales volume is now passing $1 trillion.

  
  
  
  In the massive, sprawling Visa system, the cooperation-competition paradox is a fundamental part of the structure. Fierce competition occurs among member institutions and banks that issue Visa cards, set prices and develop services … all while going after each other’s customers. But these institutions must also cooperate: for the system to work, merchants and vendors must be able to accept any Visa card anywhere in the world, regardless of who issued the card. This mixture of cooperation and competition has allowed the system to grow globally, seemingly immune to traditional constraints of language, culture, currencies, politics or legal codes.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "We are used to thinking about competitions in which there is only one winner, competitions such as football or chess. But the world is rarely like that. In a vast range of situations, mutual cooperation can be better for both sides than mutual defection.The key to doing well lies not in overcoming others, but in eliciting their cooperation."

  –Waldrop

  
  "A scan of history shows that technical innovations almost always arise as a particular combination of well-known building blocks. "

  –Axelrod

  
  
  --------------------------------------------------------------------------------

   

  One popular expression of the competition-cooperation paradox is the “tit-for-tat” strategy. It came about when political scientist Robert Axelrod tested a variety of competitive strategies using computer simulations. Time and again, the simplest strategy of all took the prize in this complex contest: University of Toronto psychologist Anatol Rapport’s “Tit-for-Tat” program started out by cooperating on the first move, and then simply did exactly what the other program had done on the move before. The program was “nice” in the sense that it would never defect first. It was “tough” in the sense that it would punish uncooperative behavior by competing on the next move. It was “forgiving” in that it returned to cooperation once the other party demonstrated cooperation. And it was “clear” in the sense that it was very easy for the opposing programs to figure out exactly what it would do next. Thus, some have proposed the heuristic that “nice, tough, forgiving and clear guys finish first.”

  
  
  
  In his 1984 book, The Evolution of Cooperation, Robert Axelrod showed the profound nature of this simple strategy in its application to all sorts of complex adaptive systems – trench warfare in WW1, politics and even fungus growth on rocks. Commenting on this strategy, Waldrop said, “Consider the magical fact that competition can produce a very strong incentive for cooperation, as certain players forge alliances and symbiotic relationships with each other for mutual support. It happens at every level of, and in every kind of, complex adaptive system, from biology, to economics, to politics.”

  
  
  
  
  --------------------------------------------------------------------------------

  
  "It’s against the interests of either predator or prey to eliminate the enemy. That’s clearly irrational, yet that is clearly a force that drives nature."

  –Ehrlich

  
  
  --------------------------------------------------------------------------------

   

  A good leader would be one who knows how to, and prefers to, cooperate, but is also a skillful competitor when provoked to competition (that is, a nice, forgiving, tough and clear person). Note that this strategy rejects both extremes as a singular strategy. While much is said these days about the importance of being cooperative and positive-thinking in business dealings, the always-cooperative leader may find his or her proverbial lunch is being eaten by others. Similarly, while sports and warrior metaphors are also popular in some leadership circles, the always-competitive leader may find himself or herself on the outside looking in as alliances are formed.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "“[A] concept that is deeply ingrained in biology is competition.This is often described as the driving force of evolution … . However, there is as much cooperation in biology as there is competition. Mutualism and symbiosis, organisms living in a state of mutual dependency … are an equally universal feature of the biological realm.Why not argue that cooperation is the great source of innovation in evolution?”"

  –Goodwin

  
  
  --------------------------------------------------------------------------------

   

  
   

   

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  Lindberg, Complexity Management, VHA Inc. Permission to copy for educational

  purposes only. All other rights reserved.

   

  
  

  
   

Points o Interest

• Description
    

   

   

    Description of complex adaptive systems

  
  
  
  CAS have a number of linked attributes or properties. Because the attributes are all linked, it is impossible to identify the starting point for the list of attributes. Each attribute can be seen to be both a cause and effect of the other attributes. The attributes listed are all in stark contrast to the implicit assumptions underlying traditional management and Newtonian science.

  
  
  
  CAS are embedded or nested in other CAS. Each individual agent in a CAS is itself a CAS. In an ecosystem, a tree in a forest is a CAS and is also an agent in the CAS of the forest which is an agent in the larger ecosystem of the island and so forth. In health care, a doctor is a CAS and also an agent in the department which is a CAS and an agent in the hospital which is a CAS and an agent in health care which is a CAS and an agent in society. The agents co-evolve with the CAS of which they are a part. The cause and effect is mutual rather than one-way. In the health care system, we see how the system is co-evolving with the health care organizations and practitioners which make up the whole. The entire system is emerging from a dense pattern of interactions.

  
  
   

  Tales

  What we could be doing

  World wide complexity

  Make it or let it

  
  Bibliography

  Holland:

  Hidden Order

  Wilson:

  Naturalist

  
  
  Diffusion

  Styles

    Diversity is necessary for the sustainability of a CAS. Diversity is a source of information or novelty. As John Holland argues, the diversity of a CAS is the result of progressive adaptations. Diversity which is the result of adaptation also becomes the source of future adaptations. A decrease in diversity reduces the potential for future adaptations. It is for this reason that biologist E.O. Wilson argues that the rain forest is so critical to our planet. It has significantly more diversity - more potential for adaptation - than any other part of the planet. The planet needs this source of information and potential for long-term survival. In organizations, diversity is becoming seen as a key source of sustainability. Psychological profiles which identify individuals' dominant thinking styles have become popular management tools to ensure there is a sufficient level of diversity, at least in terms of thinking approaches, within teams in organizations. Diversity is seen as a key to innovation and long term viability.

  
  
  
  Many of us were taught that biological innovation was due in large part to genetic random mutations. When these random mutations fit the environment better than their predecessor they had a higher chance of being retained in the gene pool. Adaptation or innovation by random mutation of genes explains only a small fraction of the biological diversity we experience today. Crossover of genetic material is a million times more common than mutation in nature according to John Holland. In essence, crossover suggests a mixing together of the same building blocks or genetic material into different combinations. Understanding this can lead to profound insights about CAS. The concept of genetic algorithms is paradoxical in that building blocks, genes or other raw elements which are recombined in a wide variety of ways are the key to sustainability. Yet the process of manipulating these blocks only occurs when they are in relationship to each other. In genetic terms, this means the whole string on a chromosome. Holland argues that "evolution remembers combinations of building blocks that increase fitness." It is the relationship between the building blocks which is significant rather than the building blocks themselves. The focus is on the inter-relationships.

  
  
   

  
  Principles

  Chunking

    In organizational terms, this suggests that it is not the individual that is most critical but the relationships between individuals. We see this frequently in team sports. The team with the best individual players can lose to a team of poorer players. The second team cannot rely on one or two stars but instead has to focus on creating outcomes which are beyond the talents of any one individual. They create outcomes based on the interrelationships between the players. This is not to dismiss individual excellence. It does suggest that individual abilities is not a complete explanation of success or failure. In management terms, it shifts the attention to focus on the patterns of interrelationships and on the context of the issue, individual or group.

  
  
  
   

  
  Aides

  Min specs

  Generative relationships

    CAS have distributed control rather than centralized control. Rather than having a command center which directs all of the agents, control is distributed throughout the system. In a school of fish, there is no 'boss' which directs the other fishes' behavior. The independent agents (or fish) have the capacity to learn new strategies and adaptive techniques. The coherence of a CAS' behavior relates to the interrelationships between the agents. You cannot explain the outcomes or behavior of a CAS from a thorough understanding of all of the individual parts or agents. The school of fish reacts to a stimulus, for example the threat of a predator, faster than any individual fish can react. The school has capacities and attributes which are not explainable by the capacities and attributes of the individual agents. There is not one fish which is smarter than the others who is directing the school. If there was a smart 'boss' fish, this form of centralized control would result in a school of fish reacting at least as slow as the fastest fish could respond. Centralized control would slow down the school's capacity to react and adapt.

  
  
   

    

  --------------------------------------------------------------------------------

  
  "Some people really want to stop controlling, but are afraid. Everywhere things are changing, creating high degrees of uncertainty and anxiety. And the more anxious you are, the more in control you need to be. Making all this even worse, we've bought into the myth that leaders have all the answers. Managers who accept this myth have their levels of anxiety ratcheted up again. ...If complexity theory can begin freeing managers from this myth of control, I think you'll see people a whole lot more comfortable."

  Linda Rusch

  Vice President of Patient Care

  Hunterdon Medical Center

  New Jersey

  
  --------------------------------------------------------------------------------

   

  Distributed control means that the outcomes of a complex adaptive system emerge from a process of self-organization rather than being designed and controlled externally or by a centralized body. The emergence is a result of the patterns of interrelationships between the agents. Emergence suggests unpredictability - an inability to state precisely how a system will evolve.

  
  
  
  Rather than trying to predict the specific outcome of emergence, Stuart Kauffman suggests we think about fitness landscapes for CAS. A CAS or population of CAS are seen to be higher on the fitness landscape when they have learned better strategies to adapt and co-evolve with their environment. Being on a peak in a fitness landscape indicates greater success. However, the fitness landscape itself is not fixed - it is shifting and evolving. Hence a CAS needs to be continuously learning new strategies. The pattern one is trying to master is the adaptive walk or capacity of a CAS to move on fitness landscapes towards higher, more secure positions.

  
  
   

  
  Tales

  A Complex Way

  Emerges from the fabric

  
  Bibliography

  Kauffman:

  At Home

    The co-evolution of a CAS and its environment is difficult to map because it is non-linear. Linearity implies that the size of the change is correlated with the magnitude of the input to the system. A small input will have a small effect and a large input will have a large effect in a linear system. A CAS is a non-linear system. The size of the outcome may not be correlated to the size of the input. A large push to the system may not move it at all. In many non-linear systems, you cannot accurately predict the effect of the change by the size of the input to the system.

  
  
  
  Weather systems are often cited as examples of this phenomenon of nonlinearity. The butterfly effect, a term coined by meteorologist Edward Lorenz, is created, in part, by the huge number of non-linear interactions in weather. The butterfly effect suggests that sometimes a seemingly insignificant difference can make a huge impact. Lorenz found that in simulated weather forecasting, two almost identical simulations could result in radically different weather patterns. A very tiny change to the initial variables, metaphorically something as small as a butterfly flapping its wings, can radically alter the outcome. The weather system is very sensitive to the initial conditions or to its history.

  
  
  
  An example in an organizational setting of non-linearity is the huge effort put into a staff retreat or strategic planning exercise where everything stays the same after the 'big push'. In contrast, there are many examples of one small whisper of gossip - one small push - which creates a radical and rapid change in organizations.

  
  
  
  Non-linearity, distributed control and independent agents create conditions for perpetual novelty and innovation. CAS learn new strategies from experience. Their unique history helps shape the path they take. Newtonian science is ahistorical - the resting point or attractor of the system is independent of its history. This is the basis of neo-classical economics and is the antithesis of complexity.

  
  
   

  
  Aides

  Min specs

  
  
  Bibliography

  Arthur:

  Increasing Returns

  Lorenz:

  Chaos

  Waldrop:

  Complexity

    Complex adaptive systems are history dependent. They are shaped and influenced by where they have been. This may seem obvious and trivial. But much of our traditional science and management theory ignore this point. What is good in one context, makes sense in all contexts. Marketers talk about rolling out programs that were effective in one place and hence should be effective in all. In traditional neo-classical economics, there is an assumption of equifinality - it does not matter where the system has come from, it will head towards the equilibrium point. Outliers or minor differences in the starting point or history of the system are ignored. The outlier or difference from the normal pattern is assumed to be dampened and hence a 'blip' is not important. Brian Arthur's work in economics has radically altered this viewpoint. For example, he cites evidence of small differences fundamentally altering the shape of an industry. The differences are not always dampened but may indeed grow to reshape the whole. Lorenz referred to this in meteorology as sensitive dependence to initial conditions which was discussed earlier as the butterfly effect. In economics, in nature, in weather and in human organizations, we see many examples where understanding history is key to understanding the current position and potential movement of a CAS.

  
  
   

  
  Aides

  Wicked questions

    CAS are naturally drawn to attractors. In Newtonian science, an attractor can be the resting point for a pendulum. Unlike traditional attractors in Newtonian science which are a fixed point or repeated rhythm, the attractors for a CAS may be strange because they may have an overall shape and boundaries but one cannot predict exactly how or where the shape will form. They are formed in part by non-linear interactions. The attractor is a pattern or area that draws the energy of the system to it. It is a boundary of behavior for the system. The system will operate within this boundary, but at a local level - we cannot predict where the system will be within this overall attractor.

  
  
   

  
  Tales

  A complex way

  Emerges from the fabric

  
  
  Principles

  Tune to edge

    A dominant theme in the change management literature is how to overcome resistance to change. Using the concept of attractors, the idea of change is flipped to look at sources of attraction. In other words, to use the natural energy of the system rather than to fight against it. The non-linearity property of a CAS means that attractors may not be the biggest most obvious issues. Looking for the subtle attractors becomes a new challenge for managers.

  
  
  
  
  --------------------------------------------------------------------------------

  
  "In the past, when managers have tried to implement change, they'd find themselves wasting energy fighting off resistors who felt threatened.  Complexity science suggests that we can create small, non-threatening changes that attract people, instead of implementing large-scale change that excites resistence.  We work with the attractors"

  
  
  
  Mary Anne Keyes, R.N.

  Vice President, Patient Care

  Muhlenberg Regional Mediacal Center

  Plainfield, NJ

  
  --------------------------------------------------------------------------------

   CAS thrive in an area of bounded instability on the border or edge of chaos. In this region, there is not enough stability to have repetition or prediction, but not enough instability to create anarchy or to disperse the system. Life for a CAS is a dance on the border between death by equilibrium or death by dissipation. In organ- izational settings, this is a region of highly creative energy.

  
   

   

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  Schulich School of Business, York University, Toronto, Canada.

  Permission to copy for educational purposes only. All other rights reserved.

   

  
  

  
   
• Paradoxes
    

   

   

  
  Tales

  Another way to think

    Some of the paradoxes of complexity

  
  Complexity science is highly paradoxical. As you study the world through a complexity lens you will be continually confronted with 'both-and' rather than 'either-or' thinking. The paradoxes of complexity are that both sides of many apparent contradictions are true.

  
  
   

  Aides

  Stacey matrix

  Wicked questions

  Min specs

  
  Principles

  Paradox

    The first of these paradoxes is that the systemic nature of a CAS implies interdependence yet each of the elements which are interdependent are able to act independently. Interdependence and independence co-exist.

  
  
  
  Another paradox in complexity is that simple patterns of interaction can create huge numbers of potential outcomes. Simplicity leads to complexity.  CAS operate in a context that is frequently unpredictable; not merely unknown but unknowable. Yet it is the agents' propensity to predict based on schema of local conditions that allow them to act in an apparently coherent manner.

  
  
   

  
  Bibliography

  Hurst/

  Zimmerman:

  Ecocyle

    Complexity science is the study of living systems but living systems die.  As a metaphor associated with life, it needs to encompass all aspects of the life cycle. Death is part of this cycle. The traditional management literature's depiction of the life cycle begins at birth and ends at decline. Complexity also includes the study of death and renewal.

  
  
   

  
  Bibliography

  Morgan:

  Images

  
  Aides

  Metaphor

  Reflection

    Complexity is a metaphor

  A recent article in a popular magazine argued that we needed to distinguish between complexity researchers who were using the 'theory' from those who were using the 'metaphor'. What that statement missed is that all science is metaphor, as Gareth Morgan argues. It is metaphor which shapes our logic and perspective. Metaphor influences the questions we ask and hence the answers we find. A powerful metaphor becomes deeply rooted in our ways of understanding and is often implicit rather than explicit. In biological terms, a metaphor is the schema by which we make sense of our situation.

  
  
   

  
  Bibliography

  Morgan:

  Images

  
  
  Aides

  Metaphor

  Reflection

  
  
  Principles

  Complexity lens

    

  --------------------------------------------------------------------------------

  
  "As a physician, I learned to think from a biological perspective. When I went into management, traditional organizational theory seemed artificial, foreign to my experience. So when I started studying complexity through the VHA project, I was stunned. Here was a way of thinking about organizations hat compared them to living things. That makes sense to me, intuitively."

  Richard Weinberg, MD

  Vice President,

  Network Development

  Atlantic Health System

  Passaic, New Jersey

  
  --------------------------------------------------------------------------------

   

  Complexity science presents a contrast to the dominant scientific and organizational metaphor and thereby challenges us to see what other questions we can ask about the systems we are studying or living within. The metaphor of systems as mechanical or 'machines' has shaped our studies in physics, biology, economics, medicine and organizations. Complexity is about reframing our understanding of many systems by using a metaphor associated with life and living systems rather than machines or mechanical systems. Viewing the world through a complexity lens means understanding the world from biological concepts.

   

    

  
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  Schulich School of Business, York University, Toronto, Canada.

  Permission to copy for educational purposes only. All other rights reserved

   

  
  

  
   

Common Issues

• Introduction: Planning in the New World of Complex Systems
  

  		{PRIVATE "TYPE=PICT;ALT=2-apphead.gif (6644 bytes)"}
  Tales {HYPERLINK "main_tales5.html"} A CEO's conversion {HYPERLINK "main_tales6.html"} Managing a living thing {HYPERLINK "main_tales10.html"} New ways of working {HYPERLINK "main_tales19.html"} Make it or let it {HYPERLINK "main_tales12.html"} Worldwide complexity {HYPERLINK "main_tales18.html"} Learn as you go		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems Jeffrey Goldstein, Ph.D. School of Management and Business Adelphi University Garden City, NY 11530 "If you want to make God laugh, tell Him your plans" -Ida Davis, late grandmother of the author
  		Introduction: Planning in the New World of Complex Systems. Planning is considered a crucial responsibility for the leaders of organizations. The common wisdom has it that the higher-up in the hierarchy a leader is, the greater the time span is supposed to be covered by planning. Thus, a CEO is expected to be involved in planning that focuses on several years, even many years, ahead. The sequence of planning typically goes like this: accurate forecasting; establishing a vision; planning for the vision; articulating the vision; implementing the plan; measuring the progress being made to achieve the vision; and, correcting the course if necessary. But, what assumptions underlie this conception of planning and do they remain as pertinent as they once did in the face of the strange new world of complex and nonlinear systems within which leaders must lead? Consider the etymology of the word "plan": it comes from the Latin "planus" meaning flat, as in our words "plane" (a flat surface) and "plain" (the Plains). A "plan" is a projection or map of a three dimensional object (e.g., an airplane) onto a two dimension flat surface (e.g., the airplane s blueprint). The plan then offers a way to both survey all at once a dauntingly large or complicated object as well as a means to peer into the future by looking ahead on the plan s flat surface. But the flatness and static quality of the plan neglect not only the spatial third dimension but the temporal dimension of a system's evolution over time as well. This neglect is not a problem if the plan is of a simple, linear system. But, what recent research into complexity is showing is that our businesses or institutions are not simple or linear, they are better thought of as complex and nonlinear. As a result, the leadership role of planning needs to be rethought in the light of complexity research. I propose in this article to reconceive planning in the light of contemporary research in the complexity sciences by sketching out three revised roles for planners in the complex, nonlinear, and nonequilibrium world in which our businesses and institutions exist:
  Aides {HYPERLINK "main_aides4.html"} Metaphor		Planners as Nonlinear and Complex Map-makers Planners as Nonlinear and Complex Explorers Planners as Nonlinear and Complex Tricksters   These three roles are interrelated in the sense that the planner as Trickster first needs to have Explored the new terrain of the nonlinear and complex world which, in turn, demands that appropriate maps have been made of this new geography. So, first we'll look at the new maps, then how to explore the new geography, and finally, how to proceed within this new geography following these new maps.
  		{HYPERLINK "main_app-b.html"} Next  | {HYPERLINK "main_app.html"} Previous  | {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction {HYPERLINK "main_app.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   Copyright © 2001 VHA Inc. Permission to copy for educational purposes only.

  
  
  

  
   
• Planning and The Geography of Predictability
  

  		{PRIVATE "TYPE=PICT;ALT=2-apphead.gif (6644 bytes)"}
  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  		Planning and The Geography of Predictability Traditionally, successful planning was supposed to rest on two interrelated achievements: accurate prediction of the future combined with an implementation strategy carefully tailored to these predictions. For instance, Ackerman (1982) claimed that successful organizational change resulted not only from an "impact analysis" of how the planned change will specifically effect the organization's functions, people, and management systems, but the ability of planners to predict, ahead of time, at what pace this change will proceed! And, Zeira and Avedisian (1990) proposed a planning procedure based so primarily on the accuracy of the initial forecast that success was supposed to altogether hinge on the initial assessment of the current status of the organization. {PRIVATE "TYPE=PICT;ALT=v_4.gif (34064 bytes)"} Figure 1: Planning as Cartography
  		Indeed, this linkage of planning with accurate prediction runs deep in our classical scientific and philosophical heritage. For example, Isaac Newton believed he had managed by means of his calculus to unfailingly predict the future state of a system - all that was needed was an accurate measurement of initial conditions and the appropriate equations of motion (Ekeland, 1988). Linking effective planning to this ideal of predictability indeed sounds like a commendable endeavor for an organization. The only problem is that Newton s promise of predictability was for a world mainly conceived as linear, simple, and stable, whereas complexity research is revealing a world composed of systems that are nonlinear, complex, and unstable. In such a new world, Newton's type of predictability can no longer reign supreme. Of course, this is true not only from a mathematical or physical point of view, for who can, in our tumultuous and unstable healthcare environment, seriously entertain the belief that predicting the future is possible anymore (outside of trivial considerations of current trends)? Instead of a stable environment, instability is the name of the game: shifts in the workforce; the unexpected rise of resistant bacteria and apparently new viruses; changes in healthcare financing and insurance; unexpected shifts in governmental regulations; the unprecedented rise and fall of for-profit ventures; technological innovations; demographic shifts in the marketplace; and on and on. Furthermore, are prediction and accurate anticipation really what's so crucial for organizational change efforts? Dyers (1985), for example, in his studies of the planned change of corporate cultures, points out that in many cases significant changes were not planned, but were, instead, precipitated by unanticipated financial shifts, crises, illnesses, and even deaths of leaders. And, Westley (1990) found that unexpected changes, spontaneously accompanying planned change efforts, often had more lasting influence on an organization than the original plans themselves.
  		{HYPERLINK "main_app-c.html"} Next  | {HYPERLINK "main_app-a.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction {HYPERLINK "main_app.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• A Limit to Unpredictability
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  		A Limit to Unpredictability Amidst all this talk about unpredictability, however, an important point needs to be underscored. To be sure in the wake of complexity research, there has been a great deal of brouhaha surrounding the newly discovered unpredictability of complex systems which has been having a major impact on how we are now thinking about our businesses and institutions. Some organizational theorists have even gone so far as to claim that such unpredictability obviates entirely the role of planning and visioning (a chief buzzword of leadership in the 1980's and early 90's). What's the point of planning if the future is totally uncertain? All it can be is to serve as a temporary illusion, something nice to strive for but a striving that is ultimately in vain. To be sure, complex systems are unpredictable in ways not previously considered. But it is simply not true that they are not predicable at all. Instead, the world of complex, nonlinear, and nonequilibrium (or far-from-equilibrium) systems is a strange brew of anticipated and surprising events, continuous and emergent phenomena, and stable and unstable features. To say they are totally unpredictable is as simplistic as to say they are as predictable as they were once thought to be. Rethinking the role of planning called-for by the recognition of organizations as complex systems demands then not only a sufficient grasp of what makes them unpredictable, it equally requires those involved in corporate planning to understand in what ways this unpredictability is itself limited. Complex organizations are indeed predictable but in ways not previously considered. Therefore, a nonlinear and complex world requires a nonlinear and complex map, and, accordingly, leaders as planners must practice a new style of cartography (see the geography of the new nonlinear and complex world in Figure 1 above).
  		{HYPERLINK "main_app-d.html"} Next  | {HYPERLINK "main_app-b.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction {HYPERLINK "main_app.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "main_app-b.html"} {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for Educational purposes only.

  
  
  

  
   
• Regions of Nonlinear Amplification: Loss of Information and Unpredictability
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  Bibliography {HYPERLINK "main_bib2.html"} Lorenz: Chaos		Regions of Nonlinear Amplification: Loss of Information and Unpredictability Chaos As is now well appreciated, one of the cornerstones of the complexity revolution concerns nonlinearity. According to the physicist J. Bruce West (1985), the success of linear reasoning formed the backbone of scientific models well into the mid-twentieth century. This linear perspective assumed a one- way, non-reciprocal type of causality, a proportion between input and output, a negligible environmental influence on a system, and that systems would evolve predictably (as on the flat surface of the plan that was mentioned above). On the other hand, discoveries of nonlinearity have radically challenged each of these assumptions. We can see this by taking a look at one of the most startling types of newly discovered nonlinearity, i.e., chaos.
  		Chaos presents one of the most startling demonstrations of unpredictability in complex systems. Since chaotic systems show a degree of unpredictability more extensive than that found generally in complex systems, the recognition of bounds even to the unpredictability in chaotic systems will be applicable even more so to complex systems in general. The unpredictability of chaotic systems is the result of their property of sensitive dependence on initial conditions (SIC) which exponentially magnifies small differences or changes in initial conditions. This is the so-called Butterfly Effect where the tiny air currents produced by a butterfly flapping its wings in, say, Sierra Leone, can be hugely amplified leading to a thunderstorm weeks later in Brazil. If such a tiny event as a butterfly flapping its wings could have such a huge impact on a system, and the number of such tiny events happening in a large complex system is so enormous, then the predictability of future states in a chaotic system must be impossible. Indeed, mathematical theorems have proven that the unpredictability of a chaotic system will always exceed capacity of the fastest computer predicting future states of a chaotic system by calculations based on initial conditions (Ford, 1989). A way to understand chaos' characteristic of SIC is to first consider what initial conditions are and how they are measured. An initial condition is simply the current state of a system when it is being assessed or measured. Measurements of the initial conditions of the weather, for example, may include air temperature at sea level, air temperature at higher elevations, wind speed, humidity, and so on. Of course, any measurement at some initial point in time will strive to be as precise and accurate as possible. On a graph, this hoped-for, ideal precision of measurement of initial conditions would be captured by a clearly distinct point (see Figure 1 in Appendix B). But the fact is that every measurement of the initial conditions of any system will contain some degree of imprecision or inaccuracy because the measurers are fallible, the measuring instruments are fallible and the measurement accuracy will always be limited. For example, measurements of air temperature at sea level will only go as far as some specific decimal point: Fahrenheit 75.0093 degrees. The instrument just cannot go any further. But this means that the measurement when displayed on a graph will never be an exact point, but will instead always occupy a region around a point, this region being equivalent to the amount of inaccuracy of the measurement (again, see Figure 1 in Appendix A).
  		Unpredictability as the The Nonlinear Expansion of Ignorance Because there can never be a perfectly accurate measurement or assessment of a system's initial condition, there will always be something about the system that, at the time it is measured, remains unknown, in other words, a degree of ignorance or missing information about the system (Ford, 1989). Now the fact that we will always remain ignorant to some degree about a system does not present a problem for predictability in a linear system. The reason is that a linear system does not expand the amount of ignorance we have at any initial condition but simply keeps this ignorance approximately the same. That is, a small magnitude of ignorance or missing information to start-out with will merely stay the same because such systems are not sensitive to initial conditions. In other words they do not amplify the initial imprecision. The linearity of the system guarantees that the amount of what we don't know about the system will remain pretty much the same. However, in a strongly nonlinear system such as found in chaos, the ignorance or missing information associated with imprecision of measurement or assessment will be "blown-up" by the system and to such a degree that our ignorance of the system will always exceeds our ability to predict future states of the system. Chaotic systems, therefore, are intractably unpredictable, at least as far as future states of the system are concerned (See Appendix A Figure 2). In chaotic systems, we become more and more ignorant as we project the current state into the future. That is, our projection of the future will have to be extremely general and imprecise. Consequently, trying to predict the future state of a chaotic system based on measurements of the initial condition is largely an exercise in futility. All it can yield is a very large and murky space of possibilities for future states of the system. From the point of view of a planner trying to prognosticate the future, each future state of an organization becomes farther and farther removed from the predictions based on the initial conditions. The point is not simply the obvious fact that we can't know everything, instead, it is that chaos exponentially amplifies every small lack of information at our disposal. In such systems, there can be no exact solution, no short cut to tell ahead of time a future state - you just have to watch as the system evolves. According to the computer scientist Ed Fredkin: "There is no way to knowing the answer to some question [a nonlinear one] any faster than what's going on...(even God) cannot know the answer to the question any faster than doing it" (quoted in Wright, 1990, p. 68).
  		Whereas the assumption of linearity in traditional planning presents a picture of system evolution as if it were proceeding on a flat plane where there is a proportionality between input and output with no surprises ahead, in nonlinear amplification like in chaos, a small input is magnified into a very large output. This suggests that nonlinearity deforms the surface so much that our line of vision is obscured. In regard to a business or institution characterized by some degree of strong nonlinearity, any initial assessment will not be of much help in forecasting future states of the system. This holds true for assessments of the environment as well. No matter how sophisticated the tools for measuring or assessing environmental variables, if the environment is characterized by strong nonlinearities, the future will remain opaque. But all this talk about the expansion of our ignorance and the ensuing unpredictability in strongly nonlinear systems is not the whole truth being revealed in complexity research. Indeed, there are regions on the nonlinear and complex geography that are indeed unpredictable, but the good news is that the more we learn about nonlinear systems the more we know about limits to regions of unpredictability. Let's turn to some of the ways nonlinear, complex systems are proving to be predictable after all.
  		{HYPERLINK "main_app-e.html"} Next  | {HYPERLINK "main_app-c.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction {HYPERLINK "main_app.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Decreasing Our Ignorance in Nonlinear Systems:
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  		Decreasing Our Ignorance in Nonlinear Systems: Recognizing the Identity of an Organization The unpredictability found in nonlinear, complex systems has a seldom discussed property that can actually lead to a decrease of our ignorance of them. Exploiting this property on the part of leader/planners can help facilitate their shift from thinking of planning as a linear to a nonlinear activity. Instead of being linear prognosticators, leader/planners can be facilitators of a greater recognition of an organization's identity, i.e., its core competencies, strengths and limitations, and unique perspective on the goods or services it makes or delivers. This property has to do with approaching the ongoing measurement of a chaotic or complex system in terms of a gain in experimental information (Abraham & Shaw, 1984; Shaw, 1981). This gain in experimental information, or in other words, decrease in ignorance, derives from an ongoing comparison of current measurements with ones conducted in the past, i.e., each new assessment of initial conditions is compared with previous assessments of past initial conditions. The gain in experimental information comes about by continually remeasuring the system - we conduct the same assessment at a later time (see Appendix A, Figure 3). We then compare the new measurement at the new time with what believed to be the future state of the system based on projections from our previous measurement at the initial time. But remember that our projection into the future based on the initial measurement had to be extremely general and unable to pinpoint future states of the system since SIC in the chaotic system "blew-up" the small ignorance or missing information we had at the initial measurement. But notice that the ignorance of our new measurement or its missing information has not yet "blown-up" and is, therefore, much more precise than the projection based on the past measurements. This means that the new measurement has decreased the ignorance expressed in our earlier projection into the future. That is, we know more about the system at this current time than was available at the earlier time when we projected into the future. We can then take this current decrease in ignorance or gain in experimental information flowing it backwards to the earlier imprecision, ignorance, or missing information (see Appendix A, Figure 4). This backward flow, in turn, shrinks the earlier imprecision, the degree of our earlier ignorance about the future by increasing the amount of the amount of information available to the system even at the earlier time. What's going on here is that by an ongoing measurement process and the comparison of these ongoing measurements with earlier ones, the system is yielding more knowledge or information about itself, no matter how much the nonlinear amplification in the system is making future states unpredictable. In such a way, a system, its observers and planners, can know more about itself, in terms of where it was before than it could have possibly known at the earlier time. Accordingly, a more precise knowledge of where it is now, i.e., its identity, yields potential greater knowledge of where it is heading. A planner by conducting ongoing present assessments and comparing them with earlier projections of the future gains information and decreases ignorance about what the system really is at its core, i.e., its core competencies (what specific operations, tendencies, propensities, and directions, practices, and skills form the essential identity and capacity of the organization). This shifts the role of planning, though, into a process of map-making, comparing temporal regions of a company's evolution to engender greater knowledge of the geography of an organization's identity. This role for planning is different than merely searching for trends since the focus is not on looking for trends occurring now and continuing into the future as much as it is in gaining information about where the organization was, and then continues to be, and will continue to be into the future. The planner here makes maps that connect past and present in feedback loops of information, opening up vistas into the future. It may be that this gain of information on a chaotic attractor is one of the bases for the "intuitive" insights that leaders use to guide a business or institution into the uncharted regions of the future. This gain of information about an organization s identity is related to another feature of predictability of nonlinear systems to which we now turn.
  		{HYPERLINK "main_app-f.html"} Next  | {HYPERLINK "main_app-d.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Recognizing the Identity of an Organization
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  Bibliography {HYPERLINK "main_bib2.html"} Goldstein: Unshackled		Attractors: Nonlinear Geographies with Unpredictable States but Predictable Structures One of the most fascinating findings of complexity theory is that the evolution of nonlinear, complex systems are marked by a series of phases, each of which is under the governance of an attractor(s) dominating the system at that time. These attractors, arising out of the internal nonlinear dynamics plus the influence of environmental factors on the system, act to permit and constrain the range of possible behaviors in the system. Moreover, when attractors change, behavior in the system concomitantly changes as well because it is now operating under the different set of governing rules represented by the newly emergent attractor(s). In fact, it is often possible to determine a great deal about the behavior in a complex systems through an exploration of the qualitative properties of its attractors even when the specific equations modeling the dynamics of the system haven't been solved (Glass & Mackey, 1988). Since an attractor represents the "shape" of a nonlinear system, a "shape" determining its behavior, knowledge of these "shapes" provides some degree of ability to predict the system's behavior. This is the case even for chaotic systems which, as we saw above, are marked by the the presence of sensitive dependence on initial conditions rendering the future states of such systems unpredictable. Chaotic systems have chaotic attractors whose "shape" determines the possible behaviors in the system; see Figure 2. {PRIVATE "TYPE=PICT;ALT=v_10.gif (50178 bytes)"} We can see from this figure that even aperiodic and unpredictable chaos has attractors. If chaos were a totally random system, time series data (i.e., measurements of the system at discrete time steps) would simply completely fill out the coordinate plane within which the attractor is graphed. Instead we see a particular structure that delimits the coordinate plane. This structure is the chaotic attractor which acts as an enduring geometric shape (in phase space) for the system. The presence of this attractor is a structure within which future states of the system must fall. In other words, not anything goes concerning the future evolution of a chaotic system - it must stay within its structure (Goertzel, 1993). So whereas the particular future states of the system may be unpredictable, the fact that they will fall within the attractor is definitely predictable. In this case, the attractor acts as a system's structure that remains the same or is predictable, whereas specific points on the attractor represent the system's states which are unpredictable.
  Bibliography {HYPERLINK "main_bib2.html"} Goertzel: Evolving Mind		For example, consider the weather: particular states of the weather would be the temperature or humidity at any particular time ("Today, October 29 is sunny, 54 degrees, with a humidity of 53% with a Southwest wind at 10 mph"). But then there is the climate (Mid-Atlantic, Autumn) which acts as a structure within which particular states of the weather are constrained. In a Mid-Atlantic region during late October, one can predict with a fair amount of certainty that the day-time temperature will be between 45 and 62. (Of course this all becomes more complicated due to the fact that climates change as well Ñ but because climates don't change as fast as the state of the weather, they remain good candidates for predicting the range within which future weather states will occur.) In other words, the climate as structure acts as an attractor for the states of the weather. The nonlinear dynamical psychologist Fred Abraham (1991) has termed this structural predictability of complex systems "insensitivity to initial conditions" to contrast it with the sensitive dependence on initial conditions causing future states to be so unpredictability. Because chaos is aperiodic, each new state of the system will be novel, not an exact repeat of a previous state. Indeed, deprivation in prediction turns out to be one of the preconditions of novelty in complex systems. Yet, even though novelty and uncertainty are being generated in complex, nonlinear systems, simultaneously, order and redundancy are also being maintained because of the bounded and patterned arena of the chaotic attractor acting as a structure ordering the apparently random.
  Bibliography {HYPERLINK "main_bib2.html"} Morgan: Images Principles {HYPERLINK "main_prin6.html"} Paradox		This understanding of attractor as predictable structure can be related to what the complexity influenced planning theorist Mike McMaster (1996) has said about foresight into the structure of the future because the future is currently manifested in the structure of the present. Instead of emphasizing prediction per se, McMaster argues for foresight based on an understanding of the unfolding patterns in an organization. Again, this is similar to the earlier point about how comparing present with past assessments can aid leaders in discovering more and more about an organization's identity and using these discoveries to facilitate a greater unfolding of this identity. Planners can enable greater insight into an organization's "identity" by exploiting the idea of attractors as predictable structures. Also relevant here is Gareth Morgan's (1997) point about areas of paradox in an organization being precisely the points where insights into a system's behavior may be accessible. Structure has paradoxical regions (e.g., how chaotic attractors show tendencies toward both divergence and convergence, i.e., the so-called "stretching and folding" of chaos.) Moreover, related to a point made above, if structural predictability can be used to characterize chaotic systems with their extreme form of nonlinear amplified unpredictability, then, structural predictability is even more employable when it comes to systems characterized by a lesser degree of nonlinearity.
  		{HYPERLINK "main_app-g.html"} Next  | {HYPERLINK "main_app-e.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "main_app-e.html"}{HYPERLINK "main_apptoc.html"} {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Attractors: Nonlinear Geographies with
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  Bibliography {HYPERLINK "main_bib2.html"} Goldstein: Unshackled		Attractors: Nonlinear Geographies with Unpredictable States but Predictable Structures One of the most fascinating findings of complexity theory is that the evolution of nonlinear, complex systems are marked by a series of phases, each of which is under the governance of an attractor(s) dominating the system at that time. These attractors, arising out of the internal nonlinear dynamics plus the influence of environmental factors on the system, act to permit and constrain the range of possible behaviors in the system. Moreover, when attractors change, behavior in the system concomitantly changes as well because it is now operating under the different set of governing rules represented by the newly emergent attractor(s). In fact, it is often possible to determine a great deal about the behavior in a complex systems through an exploration of the qualitative properties of its attractors even when the specific equations modeling the dynamics of the system haven't been solved (Glass & Mackey, 1988). Since an attractor represents the "shape" of a nonlinear system, a "shape" determining its behavior, knowledge of these "shapes" provides some degree of ability to predict the system's behavior. This is the case even for chaotic systems which, as we saw above, are marked by the the presence of sensitive dependence on initial conditions rendering the future states of such systems unpredictable. Chaotic systems have chaotic attractors whose "shape" determines the possible behaviors in the system; see Figure 2. {PRIVATE "TYPE=PICT;ALT=v_10.gif (50178 bytes)"} We can see from this figure that even aperiodic and unpredictable chaos has attractors. If chaos were a totally random system, time series data (i.e., measurements of the system at discrete time steps) would simply completely fill out the coordinate plane within which the attractor is graphed. Instead we see a particular structure that delimits the coordinate plane. This structure is the chaotic attractor which acts as an enduring geometric shape (in phase space) for the system. The presence of this attractor is a structure within which future states of the system must fall. In other words, not anything goes concerning the future evolution of a chaotic system - it must stay within its structure (Goertzel, 1993). So whereas the particular future states of the system may be unpredictable, the fact that they will fall within the attractor is definitely predictable. In this case, the attractor acts as a system's structure that remains the same or is predictable, whereas specific points on the attractor represent the system's states which are unpredictable.
  Bibliography {HYPERLINK "main_bib2.html"} Goertzel: Evolving Mind		For example, consider the weather: particular states of the weather would be the temperature or humidity at any particular time ("Today, October 29 is sunny, 54 degrees, with a humidity of 53% with a Southwest wind at 10 mph"). But then there is the climate (Mid-Atlantic, Autumn) which acts as a structure within which particular states of the weather are constrained. In a Mid-Atlantic region during late October, one can predict with a fair amount of certainty that the day-time temperature will be between 45 and 62. (Of course this all becomes more complicated due to the fact that climates change as well Ñ but because climates don't change as fast as the state of the weather, they remain good candidates for predicting the range within which future weather states will occur.) In other words, the climate as structure acts as an attractor for the states of the weather. The nonlinear dynamical psychologist Fred Abraham (1991) has termed this structural predictability of complex systems "insensitivity to initial conditions" to contrast it with the sensitive dependence on initial conditions causing future states to be so unpredictability. Because chaos is aperiodic, each new state of the system will be novel, not an exact repeat of a previous state. Indeed, deprivation in prediction turns out to be one of the preconditions of novelty in complex systems. Yet, even though novelty and uncertainty are being generated in complex, nonlinear systems, simultaneously, order and redundancy are also being maintained because of the bounded and patterned arena of the chaotic attractor acting as a structure ordering the apparently random.
  Bibliography {HYPERLINK "main_bib2.html"} Morgan: Images Principles {HYPERLINK "main_prin6.html"} Paradox		This understanding of attractor as predictable structure can be related to what the complexity influenced planning theorist Mike McMaster (1996) has said about foresight into the structure of the future because the future is currently manifested in the structure of the present. Instead of emphasizing prediction per se, McMaster argues for foresight based on an understanding of the unfolding patterns in an organization. Again, this is similar to the earlier point about how comparing present with past assessments can aid leaders in discovering more and more about an organization's identity and using these discoveries to facilitate a greater unfolding of this identity. Planners can enable greater insight into an organization's "identity" by exploiting the idea of attractors as predictable structures. Also relevant here is Gareth Morgan's (1997) point about areas of paradox in an organization being precisely the points where insights into a system's behavior may be accessible. Structure has paradoxical regions (e.g., how chaotic attractors show tendencies toward both divergence and convergence, i.e., the so-called "stretching and folding" of chaos.) Moreover, related to a point made above, if structural predictability can be used to characterize chaotic systems with their extreme form of nonlinear amplified unpredictability, then, structural predictability is even more employable when it comes to systems characterized by a lesser degree of nonlinearity.
  		{HYPERLINK "main_app-g.html"} Next  | {HYPERLINK "main_app-e.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "main_app-e.html"}{HYPERLINK "main_apptoc.html"} {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Unpredictable States but Predictable Structures
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  Bibliography {HYPERLINK "main_bib2.html"} Goldstein: Unshackled		Attractors: Nonlinear Geographies with Unpredictable States but Predictable Structures One of the most fascinating findings of complexity theory is that the evolution of nonlinear, complex systems are marked by a series of phases, each of which is under the governance of an attractor(s) dominating the system at that time. These attractors, arising out of the internal nonlinear dynamics plus the influence of environmental factors on the system, act to permit and constrain the range of possible behaviors in the system. Moreover, when attractors change, behavior in the system concomitantly changes as well because it is now operating under the different set of governing rules represented by the newly emergent attractor(s). In fact, it is often possible to determine a great deal about the behavior in a complex systems through an exploration of the qualitative properties of its attractors even when the specific equations modeling the dynamics of the system haven't been solved (Glass & Mackey, 1988). Since an attractor represents the "shape" of a nonlinear system, a "shape" determining its behavior, knowledge of these "shapes" provides some degree of ability to predict the system's behavior. This is the case even for chaotic systems which, as we saw above, are marked by the the presence of sensitive dependence on initial conditions rendering the future states of such systems unpredictable. Chaotic systems have chaotic attractors whose "shape" determines the possible behaviors in the system; see Figure 2. {PRIVATE "TYPE=PICT;ALT=v_10.gif (50178 bytes)"} We can see from this figure that even aperiodic and unpredictable chaos has attractors. If chaos were a totally random system, time series data (i.e., measurements of the system at discrete time steps) would simply completely fill out the coordinate plane within which the attractor is graphed. Instead we see a particular structure that delimits the coordinate plane. This structure is the chaotic attractor which acts as an enduring geometric shape (in phase space) for the system. The presence of this attractor is a structure within which future states of the system must fall. In other words, not anything goes concerning the future evolution of a chaotic system - it must stay within its structure (Goertzel, 1993). So whereas the particular future states of the system may be unpredictable, the fact that they will fall within the attractor is definitely predictable. In this case, the attractor acts as a system's structure that remains the same or is predictable, whereas specific points on the attractor represent the system's states which are unpredictable.
  Bibliography {HYPERLINK "main_bib2.html"} Goertzel: Evolving Mind		For example, consider the weather: particular states of the weather would be the temperature or humidity at any particular time ("Today, October 29 is sunny, 54 degrees, with a humidity of 53% with a Southwest wind at 10 mph"). But then there is the climate (Mid-Atlantic, Autumn) which acts as a structure within which particular states of the weather are constrained. In a Mid-Atlantic region during late October, one can predict with a fair amount of certainty that the day-time temperature will be between 45 and 62. (Of course this all becomes more complicated due to the fact that climates change as well Ñ but because climates don't change as fast as the state of the weather, they remain good candidates for predicting the range within which future weather states will occur.) In other words, the climate as structure acts as an attractor for the states of the weather. The nonlinear dynamical psychologist Fred Abraham (1991) has termed this structural predictability of complex systems "insensitivity to initial conditions" to contrast it with the sensitive dependence on initial conditions causing future states to be so unpredictability. Because chaos is aperiodic, each new state of the system will be novel, not an exact repeat of a previous state. Indeed, deprivation in prediction turns out to be one of the preconditions of novelty in complex systems. Yet, even though novelty and uncertainty are being generated in complex, nonlinear systems, simultaneously, order and redundancy are also being maintained because of the bounded and patterned arena of the chaotic attractor acting as a structure ordering the apparently random.
  Bibliography {HYPERLINK "main_bib2.html"} Morgan: Images Principles {HYPERLINK "main_prin6.html"} Paradox		This understanding of attractor as predictable structure can be related to what the complexity influenced planning theorist Mike McMaster (1996) has said about foresight into the structure of the future because the future is currently manifested in the structure of the present. Instead of emphasizing prediction per se, McMaster argues for foresight based on an understanding of the unfolding patterns in an organization. Again, this is similar to the earlier point about how comparing present with past assessments can aid leaders in discovering more and more about an organization's identity and using these discoveries to facilitate a greater unfolding of this identity. Planners can enable greater insight into an organization's "identity" by exploiting the idea of attractors as predictable structures. Also relevant here is Gareth Morgan's (1997) point about areas of paradox in an organization being precisely the points where insights into a system's behavior may be accessible. Structure has paradoxical regions (e.g., how chaotic attractors show tendencies toward both divergence and convergence, i.e., the so-called "stretching and folding" of chaos.) Moreover, related to a point made above, if structural predictability can be used to characterize chaotic systems with their extreme form of nonlinear amplified unpredictability, then, structural predictability is even more employable when it comes to systems characterized by a lesser degree of nonlinearity.
  		{HYPERLINK "main_app-g.html"} Next  | {HYPERLINK "main_app-e.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "main_app-e.html"}{HYPERLINK "main_apptoc.html"} {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Charting the Strange Realm of Nonlinear Resonance
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  		Charting the Strange Realm of Nonlinear Resonance As we have seen, nonlinearity and complexity can lead not only to greater unpredictability in a system, paradoxically, they can also yield predictable behaviors. One reason for this strange blend is the way components and subsystems of complex systems become coupled with one another in feedback types of relationships. Sometimes this coupling leads to the kind of nonlinear amplification seen, e.g., in chaotic systems, and other times nonlinear coupling can produce phases of more stability, and hence, greater predictability. Therefore, planners as cartographers of the complex world need to be familiar with various kinds of regions of nonlinear predictability. Consider, for example, the curious behavior that takes place when pendulum-driven clocks are hung on a wall already containing similar clocks: the new clocks become in-phase with the clocks already hanging there, i.e., the periodic swings of the pendulums lock-into the same frequency. As a result, before a clock is hung on the wall, if the phase of the clocks already hung is known, then one can predict the eventual phase of the new clock Ñ it will be the same as the clocks already hanging. This phenomena of frequency-locking called "entrainment" is one of the strange features of complex systems. A similar frequency-locking phenomenon can be seen in the case of large- scale weather patterns such as the now notorious El Nino, the seemingly erratic warming of the equatorial surface waters extending west into the Pacific Ocean off the coast of South America, a phenomenon now known to deleteriously effect global weather patterns. This year El Nino has been blamed for Hurricane Linda, the most powerful Eastern Pacific Hurricane on record. The name "El Nino" comes from the Spanish for "the Christ Child" because this weather pattern has tended to occur around Christmas time. El Nino is a very nonlinear complex system due to the pervasive feedback loops between oceanic phenomena (e.g., water temperature both on the surface as well as deeper as well as current speeds and extension) interacting with atmospheric phenomena (e.g., air circulation and temperatures) ( Jin, F.F., Neelin, J.D., & Ghil, M., 1994; and, Tziperman, Stone, Cane, & Jarosh, 1994). The nonlinearity of El Nino is even heightened when the seasonal cycle is added to the picture (See Appendix B). Yet, instead of this additional nonlinearity making the system more unpredictable, it can, under some conditions, actually serve to make El Nino more predictable through engendering both a new kind of stability in the system, i.e., the way the El Nino cycle can become entrained (like the pendulum-clocks above) with the seasonal cycle, as well as putting the mathematics of the El Nino nonlinearity within the known dynamics of the so- called routes to chaos. The type of emergent stability of entertainment or frequency-locking can lead to greater predictability since the system can be temporarily "stuck" at these particular phases. Through the on-going intensified exploration of such nonlinear phenomena, the predictability of complex systems will only increase. Again this is an area of nonlinear dynamics which leader/planners will need to know how to get around in. Improvement in predictability, though, doesn't translate into prophetic powers. Just this year, El Nino popped up unexpectedly. Moreover, these remarks on the sophisticated mathematical patterns of El Nino are not offered here as a suggestion that organizational planners should become mathematicians. Rather, the point is that not all hope for prediction is lost when it comes to nonlinear, complex systems and that organizational planners will need to recognize that nonlinearity will prove to be more and more navigable, but in a way that will defy common sense derived from outdated models of organizations as linear systems.
  		{HYPERLINK "main_app-h.html"} Next  | {HYPERLINK "main_app-f.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction {HYPERLINK "main_app.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   Copyright © 2001, {HYPERLINK "http://www.plexusinstitute.com"} Plexus Institute . Permission to copy for educational purposes only.

  
  
  

  
   

Planning

• Fitness Landscapes: Exploring the Possibility Space of Adaptations
  

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  		Map-makers, Explorers, and Tricksters: New Roles for Planning and Prediction in Nonlinear, Complex Systems
  Bibliography {HYPERLINK "main_bib2.html"} Goldstein: Unshackled		Attractors: Nonlinear Geographies with Unpredictable States but Predictable Structures One of the most fascinating findings of complexity theory is that the evolution of nonlinear, complex systems are marked by a series of phases, each of which is under the governance of an attractor(s) dominating the system at that time. These attractors, arising out of the internal nonlinear dynamics plus the influence of environmental factors on the system, act to permit and constrain the range of possible behaviors in the system. Moreover, when attractors change, behavior in the system concomitantly changes as well because it is now operating under the different set of governing rules represented by the newly emergent attractor(s). In fact, it is often possible to determine a great deal about the behavior in a complex systems through an exploration of the qualitative properties of its attractors even when the specific equations modeling the dynamics of the system haven't been solved (Glass & Mackey, 1988). Since an attractor represents the "shape" of a nonlinear system, a "shape" determining its behavior, knowledge of these "shapes" provides some degree of ability to predict the system's behavior. This is the case even for chaotic systems which, as we saw above, are marked by the the presence of sensitive dependence on initial conditions rendering the future states of such systems unpredictable. Chaotic systems have chaotic attractors whose "shape" determines the possible behaviors in the system; see Figure 2. {PRIVATE "TYPE=PICT;ALT=v_10.gif (50178 bytes)"} We can see from this figure that even aperiodic and unpredictable chaos has attractors. If chaos were a totally random system, time series data (i.e., measurements of the system at discrete time steps) would simply completely fill out the coordinate plane within which the attractor is graphed. Instead we see a particular structure that delimits the coordinate plane. This structure is the chaotic attractor which acts as an enduring geometric shape (in phase space) for the system. The presence of this attractor is a structure within which future states of the system must fall. In other words, not anything goes concerning the future evolution of a chaotic system - it must stay within its structure (Goertzel, 1993). So whereas the particular future states of the system may be unpredictable, the fact that they will fall within the attractor is definitely predictable. In this case, the attractor acts as a system's structure that remains the same or is predictable, whereas specific points on the attractor represent the system's states which are unpredictable.
  Bibliography {HYPERLINK "main_bib2.html"} Goertzel: Evolving Mind		For example, consider the weather: particular states of the weather would be the temperature or humidity at any particular time ("Today, October 29 is sunny, 54 degrees, with a humidity of 53% with a Southwest wind at 10 mph"). But then there is the climate (Mid-Atlantic, Autumn) which acts as a structure within which particular states of the weather are constrained. In a Mid-Atlantic region during late October, one can predict with a fair amount of certainty that the day-time temperature will be between 45 and 62. (Of course this all becomes more complicated due to the fact that climates change as well Ñ but because climates don't change as fast as the state of the weather, they remain good candidates for predicting the range within which future weather states will occur.) In other words, the climate as structure acts as an attractor for the states of the weather. The nonlinear dynamical psychologist Fred Abraham (1991) has termed this structural predictability of complex systems "insensitivity to initial conditions" to contrast it with the sensitive dependence on initial conditions causing future states to be so unpredictability. Because chaos is aperiodic, each new state of the system will be novel, not an exact repeat of a previous state. Indeed, deprivation in prediction turns out to be one of the preconditions of novelty in complex systems. Yet, even though novelty and uncertainty are being generated in complex, nonlinear systems, simultaneously, order and redundancy are also being maintained because of the bounded and patterned arena of the chaotic attractor acting as a structure ordering the apparently random.
  Bibliography {HYPERLINK "main_bib2.html"} Morgan: Images Principles {HYPERLINK "main_prin6.html"} Paradox		This understanding of attractor as predictable structure can be related to what the complexity influenced planning theorist Mike McMaster (1996) has said about foresight into the structure of the future because the future is currently manifested in the structure of the present. Instead of emphasizing prediction per se, McMaster argues for foresight based on an understanding of the unfolding patterns in an organization. Again, this is similar to the earlier point about how comparing present with past assessments can aid leaders in discovering more and more about an organization's identity and using these discoveries to facilitate a greater unfolding of this identity. Planners can enable greater insight into an organization's "identity" by exploiting the idea of attractors as predictable structures. Also relevant here is Gareth Morgan's (1997) point about areas of paradox in an organization being precisely the points where insights into a system's behavior may be accessible. Structure has paradoxical regions (e.g., how chaotic attractors show tendencies toward both divergence and convergence, i.e., the so-called "stretching and folding" of chaos.) Moreover, related to a point made above, if structural predictability can be used to characterize chaotic systems with their extreme form of nonlinear amplified unpredictability, then, structural predictability is even more employable when it comes to systems characterized by a lesser degree of nonlinearity.
  		{HYPERLINK "main_app-g.html"} Next  | {HYPERLINK "main_app-e.html"} Previous {HYPERLINK "main_app.html" \l "contents"} New Roles for Planning and Prediction Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "main_app-e.html"}{HYPERLINK "main_apptoc.html"} {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Exploring Fitness Landscapes Using the N/K Model
  

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  		Planners as Nonlinear and Complex Explorers
  		Exploring Fitness Landscapes Using the N/K Model Yet, adaptation need not take place on a purely random landscape. To envision nonrandom fitness landscapes whose contours reflect the underlying nonlinear and complex dynamics among the components in a system or ecosystem, Kauffman has developed a N/K model of adaptation. In this N/K model, N = number of traits (such as bowed or straight legs, webbed or separate toes, long or short feet) and K = the number of inputs from other genes (which is a measure of the dependence of traits on one another, i.e., the nonlinear coupling or feedback among the traits). Kauffman adds this K parameter since the contribution of a single trait to adaptability may depends on other traits (e.g., the contribution of bowed legs to adaptive fitness may simultaneously involve whether the feet are long or short e.g., if N=3 and K=2, the genome has three genes each of which is effected by two others). Using this model, one can alter K as if twisting a control knob and observe what happens as the landscape deforms. As K increases, the more interconnected the traits or modifications are, so there are more conflicting constraints and, thereby, the landscape becomes more rugged with more local peaks. Unlike a landscape with one large mountain representing a very high value of adaptiveness, in this more rugged landscape, there are a large number of modest compromise solutions rather than a perfect one. In organizational planning, an analogy can be found in the Boston Consulting Group (BCG) portfolio analysis of products or business units. In the BCG portfolio grid, business units or products are grouped into four sectors which are really another way of talking about their adaptive value: stars; cash cows; dogs; and question marks. All four may represent compromise solutions, even stars and cash cows because it is undecidable from the grid alone whether the star or cash cow represents a high optimal peak or is trapped at a local peak. Most planners get stuck at that point, whereas the nonlinear fitness landscapes promises a way to envision the adaptive value of even currently highly productive products or business units. Adaptation becomes more difficult as K increases to its maximum value, N-1, where every gene affecting every other so the fitness landscape becomes completely random. In such a random fitness landscape, an adapting organism gets trapped at very low peaks, and the rate of improvement slows; thus, adaptation to highest peak becomes virtually impossible. This can be seen in biological as well as technological evolution since they are processes that attempt to optimize systems riddled with conflicting constraints (Kauffman and Macready, 1995). In such a situation, foolish adaptation, i.e., moving down a fitness slope, may be paradoxically advantageous since it frees up those modifications trapped on lower valued short peaks. (We will come back to this idea of foolish adaptation later to see how planners may be able to exploit it.) In a moderate degree of ruggedness, the highest peaks can be scaled from the greatest number of initial positions, so an adaptive walk is more likely to climb to a high peak than a low one (i.e., the basins of attraction for the high peak as attractors are larger than for lower peaks).
  		{HYPERLINK "main_app-j.html"} Next  | {HYPERLINK "main_app-h.html"} Previous {HYPERLINK "main_apptoc2.html"} Planners as Nonlinear and Complex Explorers Contents   | {HYPERLINK "main_apptoc.html"} Applications Contents {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Planning as Adaptive Exploration of Organizational Strategies
  

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  		Planners As Nonlinear and Complex Explorers
  		Planning as Adaptive Exploration of Organizational Strategies Organizations evolve on correlated but rugged landscapes (Kauffman, 1995). Maguire (1997) understands the choice of a specific strategy, e.g., its choice of which products or services to make or offer, as correlated with a specific fitness landscape. For example, an increase in the heterogeneity of the market is equivalent to an increase in "ruggedness" on the landscape, which, in turn, means an increase in the complexity of the strategy as a design problem. The point is to envision strategy in terms of how the various combinations of organizational processes and structures which make up a strategy add to or diminish the adaptive value of specific strategies. But notice here that planning is not so much prediction, as exploration of possible scenarios. In this sense planning can be reconceptualized as exploratory searches through the "space" of modifications of a strategy. Here, the use of fitness landscapes can be applied to gain insight into which innovative organizational designs, processes, or strategies promise greater potential.
  Bibliography {HYPERLINK "main_bib2.html"} Maguire: Strategy as design		Maguire has provided a kind of grid which suggests the quality, quantity and foolishness of different exploration strategies. For example, how constrained or coupled is the environment (an organizational analogue to the N/K Model). He can use this grid to classify the appropriateness of a particular business strategy, e.g., Mintzberg's (1988) strategy of quality differentiation is a relatively local search on a short distance while design differentiation strategy is a farther away search in the adaptation landscape. Furthermore, Maguire has identified exploration or search parameters: exploration rate (search activity per unit time, number of sample units per unit time); exploration distance (search distance across landscape); and exploration direction (which variables on a string to flip; or, constraining the search to a specific direction). In the new nonlinear and complex geography of organizations, therefore, leaders as planners face a two-fold challenge: drawing useful maps of the new terrain and exploring this new terrain through the encouragement of strategies that tend toward higher fit. However, designing strategies with better fit does not always consist of climbing straight-up adaptive hills. Sometimes random searches are what's called-for, sometimes what is required is the seemingly foolish move of going down a hill, and there are still other seemingly counterproductive practices. Thus, in the nonlinear geography of complex systems, the planner also needs a bag of unusual tricks, so now we turn to the role of planner as Trickster.
  		{HYPERLINK "main_app-k.html"} Next  | {HYPERLINK "main_app-i.html"} Previous {HYPERLINK "main_apptoc2.html"} Planners as Nonlinear and Complex Explorers Contents   | {HYPERLINK "main_apptoc.html"} Applications Contents   Copyright © 2001, {HYPERLINK "http://www.plexusinstitute.com"} Plexus Institute . Permission to copy for educational purposes only.

  
  

  
  

  
   
• Planners as Nonlinear and Complex Tricksters
  

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  		Planners as Nonlinear and Complex Explorers
  		Planners as Nonlinear and Complex Tricksters So far we have examined planning in complex systems in terms of both map-making and exploration of the new nonlinear geography. Both of these planning roles assume it is a rational process, consciously utilizing new constructs to better map and explore the new terrain that is being revealed. But the new geography emerging from complexity research is, in many respects, so unlike the predictable, linear, simple, and equilibrium-based world of classical science, that rationality itself is in need of revision. The point being made here is not a call to act irrationally, but, instead, it is to place attention on how reason itself has been shaped to conform to the linearity and simplicity of the classical world. In an environment that is, in important respects, unpredictable, unstable, and vulnerable to random events, then the rationality of planning must include new outlooks and practices congruent with the new world being discovered. Here, the appropriate image for planners may not so much be the rational designer as that mischievous figure from mythology: the Trickster. Found in diverse cultures throughout the world, the Trickster breaks taboos and flouts, traditional mores and norms, constantly investigating, improvising, and devising new ways (Harding, 1963). The pranks of Trickster figures are legendary and surprisingly similar to the characteristics of complex, nonlinear systems: unpredictable; bizarre; disproportionate; random; mixing-thing up; stirring the pot; upsetting the apple-cart. These qualities are certainly a long way from the image of planning as precise forecasting, conscious design, and careful implementation of strategy. Yet, it may be that it is these tricks of the Trickster that organizations desperately need to navigate through these tumultuous times. In this section, we will be looking at planning according to three Trickster roles: "Noise Makers" "Foolish Trekkers" "Odd Matchmakers"
  		{HYPERLINK "main_app-l.html"} Next  | {HYPERLINK "main_app-j.html"} Previous {HYPERLINK "main_apptoc2.html"} Planners as Nonlinear and Complex Explorers Contents   | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Planners as "Noise Makers"
  

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  		Planners as Nonlinear and Complex Explorers
  		Planners as "Noise Makers" The Utilization of Random Events in Complex Systems Besides its crucial role in adaptation, randomness has been understood as a powerful source of the new structures (e.g., dissipative structures) emerging during the process of self-organization (Nicolis, 1989). Examples of such emergent structures are the hexagonal cells arising in the Benard liquid when a critical temperature is reached, or the life-like patterns emerging in cellular automata and random boolean networks. Random events are unpredictable, unplanned occurrences that a system, under a far-from- equilibrium condition or unstable state (i.e., near bifurcation), will notice, respond to, and amplify as a major component of the new emergent structures. For example, the hexagonal convection cells emerging in the Benard system are partially the result of the amplification of random currents in the liquid so that the specific directionality of the emerging convection cells is unpredictable. According to Prigogine and Nicolis (1989), nothing in the experimental set-up permits a prediction beforehand of the state that will eventually ensue: "Only chance, in the form of the particular perturbation that may have prevailed at the moment of the experiment, will decide..." (p. 14). {PRIVATE "TYPE=PICT;ALT=v_18.gif (6870 bytes)"} It is crucial to note that chance elements only become an important factor when the system is unstable, because that is when the nonlinear dynamics in the system have the capacity for amplifying the effect of a chance occurrence. A stable system will dampen random movements away from the prevailing attractor, whereas, in an unstable system random events can kick the system away from its attractor - see Figure 5 where stability and instability are portrayed as a ball trapped inside a bowl or perching precariously on top of an overturned bowl. The planner as Trickster would act to first turn the bowl inside out by challenging the assumptions of the current attractor, and, then, stand on top of the peaked bowl on the right to facilitate the influence of random events in pushing the system away from its current attractor.
  Aides {HYPERLINK "main_aides5.html"} Wicked questions {HYPERLINK "main_aides6.html"} Generative relationships Principles {HYPERLINK "main_prin5.html"} Tune to edge Principles {HYPERLINK "main_prin2.html"} Complexity Lens		If a system is open to the effect of random events to the point where it can undergo modification of key aspects of its processes and structures, then the system may be able be more adaptive to the environment as it changes. Indeed, random-inspired reorganizations may represent an evolutionary response of the system to changes in the environment but only if the system is in vital contact with its environments (Allen, 1988). This vital contact is what enables the system to try out its new modifications in the changed environment. Moreover, Allen and McGlade (1985) state that in order to learn about the world around them, it may be the random departures of systems from norm-seeking, average behavior which are decisive. Nicolis (1989) has evidence that permanent and rigid structures or processes in a system which is interacting with an unpredictable environment will bring the system to a less than optimal condition. Whereas, a system which has a high rate of unpredictable explorations (i.e., influenceable by random occurrences of its unpredictable environment) can develop temporary structures or processes suitable for any occasion that may arise. Furthermore, chaos and complexity, according to the physicist Robert Shaw, turn out to be a generators par excellence of information which can be understood as a potent mixture of randomness and redundancy (Shaw, 1981). Shaw interprets the source of this new information as a matter of the transfer of information from a micro-to a macro-scale. The chaotic attractor magnifies the random occurrences on the microscale upwards into novel information available to the system on a macroscale. According to the physicist Joseph Ford (1989): "chaos is dynamics freed from the shackles of order and predictability. It permits systems to randomly explore their every dynamical possibility" (p. 354). In fact, randomness permits the emergence of real novelty in a complex system because by its very nature a random event is unpredictable and not the result of a pre-set plan (for then it wouldn't be random). Consequently, randomness seems to be a necessary component at some stage in the process of organization innovation. For if innovations are truly novel they must be unpredictable and what better source of unpredictability is there besides randomness? Similarly, in an interesting parallel, it has been repeatedly pointed out that unplanned events (i.e., random) have often played a crucial role in scientific discoveries (Austin, 1977). Examples are numerous: the discovery of penicillin, radioactivity, Teflon, and so on. Perhaps, the process of scientific discovery can be understood along the lines of self-organizing systems. In both cases, that of organizational innovation and scientific discovery, randomness can function serendipitously in the formation of new, possibly more adaptive modifications of pre-existing patterns. But of course, the organization or the scientist must be open to and ready to make use of the random event. As Pasteur once said, "Chance favors the prepared mind" Ñ therefore, the organization must be primed to take advantage of the random event, and such priming is one of the roles of the leader/planner as a Trickster. Tricksters help make a system unstable in order for innovation to emerge. This is certainly a far cry from the traditional role of leaders as organization stabilizers. Certainly, there is a time for stability, but there is also a time for instability, and when organizations find themselves in an unpredictable environment, it is likely a time for instability and here is where the Trickster can play a major role.
  Bibliography {HYPERLINK "main_bib2.html"} Kauffman: At Home {HYPERLINK "main_bib2.html"} Goldstein: Unshackled		Planning and Serendipitous "Noise" Making Random events in organizations are what Ciborra (et. al., 1984) call organizational "noise", i.e., phenomena occurring in or around the organization that are usually ignored and whose effects are presumed to be restrained by organizational control mechanisms. But, in unstable conditions "organizational noise" may assume a critical role in the evolution of the system through nonlinear amplification and self-organizational processes (Goldstein, 1994). But, of course, because emergent patterns result from random effects, they cannot be predicted, nor can it be established ahead of time just what particular "organizational noise" will have a transformative rather than disorganizing effect. The role of planners, then, could be that of facilitating an organization's experimentation with noise. Figuratively speaking, planners would be acting like Trickster-inspired "noise makers" (e.g., children and adults on New Year's Eve making a lot of noise, the louder and more cacophonous, the better). This means that leader/planners as Tricksters would aid an organization in exploring its "noisy" elements, events that spontaneously depart from the norm, and instead of the normal attempt to dampen the effects of such noisy elements, actually amplify these effects.
  Principles {HYPERLINK "main_prin6.html"} Paradox		But this means that planners would simultaneously need to facilitate those unstable conditions that allow noise to have an impact. Again, this is a Trickster role in upsetting the apple cart. The author of this article (Goldstein, 1994) has discussed such methods for generating instability under the term, borrowed from Prigogine, far- from-equilibrium conditions. Examples of such Trickster noise-making would included methods that highlight the differing ideas and attitudes existing among people in a work group (not generating conflict but admitting it is there and utilizing its tremendous energy), or that challenge currently held deep beliefs about what an organization is and how it should function, or that upset the apple-cart by the facilitation of what seem absurd or foolish activities (again see Goldstein, 1994, Chapter 10).
  Aides {HYPERLINK "main_aides5.html"} Wicked questions {HYPERLINK "main_aides4.html"} Metaphor		Along the same lines, following Shaw's lead about the transfer of information from micro- to macro-scales, planners can expedite processes in a business for magnifying the creative endeavors of its individual members and incorporating these creative ideas and actions into the macro-scale of how the organization does its business. Included in such Trickster tricks is also the technique of Wicked Questions suggested by the organizational complexity researcher Brenda Zimmerman (see Zimmerman in this volume).
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• Planners as Foolish Trekkers
  

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  		Planners as Nonlinear and Complex Explorers
  Bibliography {HYPERLINK "main_bib2.html"} Kauffman: At home {HYPERLINK "main_bib2.html"} Goldstien: Unshackled		Planners as Foolish Trekkers In his N/K Models, Kauffman identified situations in fitness landscapes where low values of the K parameter (representing coupling among traits) lead to adaptive modifications getting trapped in local minima and thereby never arriving at peaks with adequate fitness. This is analogous to organizations or work groups getting stuck in equilibrium attractors which Goldstein (1994) blames on the presence of self-fulfilling prophecies which link organizational attitudes, expectations, behaviors, and results in vicious circles. For example, a self-fulfilling prophecy may link an organization's sense of identity and its market with actions congruent with those "prophecies" and which lead to results which confirm the original expectation. Self-fulfilling prophecies, though, can trap the organization or work group on very suboptimal short peaks. To free adaptive processes from their entrapment in local peaks, Kauffman has suggested a certain amount of "foolish adaptation" or "going the wrong way" referring to going down instead of up peaks. That is, to get to a peak with a higher adaptive value, first there must be a descent from a lower peak. As Maguire puts it, an escape from suboptimal peaks opens up the possibility of a uphill path to higher fitness peaks (Maguire, p. 13). Kauffman points to simulated annealing in models in condensed matter physics which is a kind of thermal bath which loosens up this kind of entrapment process. Again, the analogy is to far-from-equilibrium conditions in organizations which serve to interrupt those self-fulfilling prophecies which trap organizational functioning in suboptimal routines.
  Principles {HYPERLINK "main_prin5.html"} Tune to the edge		Hence, organizational planning can include the encouragement of a type of foolish adaptive walks. Here, planners in their Trickster role would facilitate a work group to "go the wrong way", do things unexpected and out of the ordinary even though these activities seem to be counterproductive to achieving the organization's goals. From a linear and simple perspective, this sounds like sheer idiocy, even dangerous to an organization's success. Yet, "going the wrong way" is precisely what creativity specialists often call-for. For example, participants in creativity seminars are often encouraged to go on excursions away from, even in opposite directions, to what they think they should be doing (Gordon, 1961). These foolish excursions or treks tend to loosen the grip of familiar and comfortable walks in creativity space.
  Principles {HYPERLINK "main_prin7.html"} Multiple actions		In terms of organizational planning, such foolish treks could consist of conducing meetings where, instead of good ideas, foolish notions for strategies could be entertained. (This after all is what "brainstorming" is supposed to facilitate but often doesn't because of strong pressure for group conformity). But foolish notions need not only be entertained in fantasy, planners as Tricksters need to try out some of these foolish directions. Again, because we don't have a God's Eye view of the future, complex systems need to experiment a great deal, and sometimes, with modifications of existing practices that at first sight seem foolish.
  		{HYPERLINK "main_app-n.html"} Next  | {HYPERLINK "main_app-l.html"} Previous {HYPERLINK "main_apptoc2.html"} Planners as Nonlinear and Complex Explorers Contents   | {HYPERLINK "main_apptoc.html"} Applications Contents   {HYPERLINK "http://www.plexusinstitute.com"} Copyright © 2001, Plexus Institute  Permission to copy for educational purposes only.

  
  
  

  
   
• Planners as Odd Matchmakers
  

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  		Planners as Nonlinear and Complex Explorers
  Bibliography {HYPERLINK "main_bib2.html"} Holland: Hidden {HYPERLINK "main_bib2.html"} Kauffman: At home		Planners as Odd Matchmakers Recombination One way that biological organisms explore their adaptive "space" is through sexual reproduction where recombinations of parental genetic material afford the opportunity for modifications that may prove more fit for the species. The computer scientist and complexity pioneer John Holland (1992) has created adaptive computer programs called genetic algorithms based on sexual reproduction as a paradigm of "crossing-over" or the mixing of genetic material (which become bit strings in his programs). The programs evolve by both sexual-like recombination as well as through random mutations; each new modification that is closer to the solution is given a heavier weight. Then the program over many generations converges to a solution. The computer scientists Gerhardt Bruderer and Martin Maiers along with the complexity management consultant and theorist Glenda Eoyang (Maiers and Eoyang, 1997) have been designing a genetic algorithm as a decision support tool for managers. This program can easily be modified for decision-making in planning as well. But again, such a usage is dependent on planners revising their view of what their main roles are to be. Recombination also comes up in Kauffman s N/K model. For Kauffman, sexual mating or reproduction allows a kind of "God's Eye" peek at the peaks (Kauffman, 1995). The genetic recombinations that result from sex between organisms at different locations on a landscape allows the adapting "population" to "look at" the regions between the parental genotypes. In this way recombination allows the adapting population to make use of large scale features of the landscapes to find high peaks. In fact, Kauffman found in his N/K landscapes that populations using mutation and recombination as well as selection improve far more rapidly than those using only mutation and selection (Kauffman, 1995). If the fitness landscape looks like the Alps, then the peaks carry mutual information about where to find high peaks: they are nearby! Moreover, if parents are high up on the peaks, then the kids will have a greater chance to start out higher. Yet, recombination can actually be harmful on a totally random landscape since if parents are at local peaks, recombination can lead progeny to be "dropped off" in a place with lower fitness. Furthermore, when a search is merely random with no clues about upward trends, the only way to find the highest pinnacle is to search the whole space. Recombination is going on in organizations in an unprecedented manner with the accelerating pace of mergers and acquisitions. Previously competitive organizations are now joined and the frequent issue concerns how these previously separate, even hostile entities can possible work together. Whereas the traditional approach might be to impose a new structure or plan or working procedures on the newly merged system, an approach informed by genetic algorithms or the N/K model would see this recombination and the potential conflict it might engender as a great opportunity for the emergence of new organizational practices and directions (see Goldstein, "Leadership and Emergence" in the File Cabinet section). Then the intervention would not be to dampen differences of opinion but to highlight them, amplify them and allow a more adaptive organizational structure to emerge as a result of the merger.
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• Matchmaking for Strange Couples
  Matchmaking for Strange Couples  

  Organizational planners, then, might conceive of themselves as organizational "matchmakers" bringing together diverse organizational "genetic material" and mixing it up and seeing what ensues. But these should be strange matches, bringing together what was previously thought of as incompatible elements or components or subsystems. For example, bringing together janitorial staff with product designers, customers with suppliers, finance executives with secretaries on nursing floors. In fact, the more seemingly incompatible the elements, the more they probably need to be brought together. The emphasis, here, of course, is on experimentation and the allowance of emergent patterns that are unanticipated and have unexpected outcomes. Again, one cannot know the correct solution ahead of time, so one needs to work with whatever emerges through recombination. Such organizational matchmaking links to Lane's and Maxfield's (1996) idea of generative relationships which are connections among people which generate new organizational forms, directions and strategies. A generative relationship is based on heterogeneity, it leads to greater action possibilities, it promotes the sharing of information (hence, the flow of innovation), and it sets up the conditions for more novel relationships. Organization leader/planners as Tricksters make and engender more matches and thus build into a feedback cycle of expanding networks. This is emergence and self-organization at its best.

  
  

  
   
• Conclusion: From Planning the Future to Preparing for the Future
  

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  		Planners as Nonlinear and Complex Explorers
  		Conclusion: From Planning the Future to Preparing for the Future Unlimited possibilities for a company are, of course, not possible. Possibilities for strategies are limited by the past history of the organization, by the constraints of the marketplace, and by the identity of the organization, i.e., its set of core competencies. Complexity sciences can provide better maps for organizational strategy design that follow these constraints than traditional organizational tools or constructs. We live in a complex, interdependent world where the business and institutional environment is undergoing unprecedented change, even turbulence. Whereas planners whose main function was to accurately predict the future had some reason to congratulate themselves when organizations were in a more stable environment; today the whole claim of linear predictability is being seriously undermined. Therefore, the role of leader/planners must shift to take advantage of what we are learning about the dynamics of complex, interactive, nonlinear, nonequilibrium systems. This shift includes transforming planning into: A set of better means for organizations to get to know who they are, what they do well, and what their innate tendencies are. Planning becomes preparation for the future through greater insight into what one does better right now. A set of processes for facilitating organizational experimentation through using whatever happens, anticipated or unanticipated. This makes planning into a way to prepare for or adapt to, not predict the future. A set of tools taken and modified from nonlinear mathematics and sciences to help an organization navigate through the newly discovered, intriguing terrain opened up by the exploration of complex systems. Whereas, at first sight, it might have appeared that the unpredictability of complex systems foredoomed all attempts at planning, there are important ways with which the nonlinear dynamical accounting for this unpredictability can be exploited for a revised conception of organizational leading/planning.
  		{HYPERLINK "main_app-q.html"} Next  | {HYPERLINK "main_app-o.html"} Previous {HYPERLINK "main_apptoc2.html"} Planners as Nonlinear and Complex Explorers Contents   | {HYPERLINK "main_apptoc.html"} Applications Contents   Copyright © 2001, Brenda Jane Zimmerman and Curt Lindberg. Permission to copy for educational purposes only. All other rights reserved. Excerpt from "Stories of the Emergence of Complexity Science in US Health Care" - paper to be published in a book edited by Eve Mitleton-Kelly of the London School of Economics.

  
  
  

  
   
• Appendix A: Chaos and Measurement
   

Quality Mngmnt

• Enabling Natural, Adaptive Improvement.
   
• The Complex Patient
   
• Diffusion of Innovation and Best Practice
  

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  		2. Diffusion of Innovation and Best Practice The issue… There are many things we know... lower left of Stacey... Examples: early ambulation after hip surgery, early extubation after heart surgery, aspirin after MI.... In a large organization or health system, these are done in some places well and in other places not at all. Replication of improvement is an important issue. In general, people are confused by the often failure to replicate a logically sound improvement idea. Complexity theory could provide important sense-making and strategy-aides. The question we should be seeking to answer for the leaders out there struggling with replication of improvement is: What should I do different from what I am currently doing? Caution… There is a need for thoughtfulness in deciding what is needed in a situation. Yes, taking an area of certainty and driving it home is the problem. But also yes, creating conditions for adaptation and creativity is the problem. Complexity Concepts: Fitness Landscapes, Coupling, Context… Kauffman's "rugged fitness landscapes" may help us understand what is happening here... In a smooth landscape, optimization of whole system is easy... On rugged landscape, optimization of whole system is hard... On smooth landscapes, all can see the optimum point and all roads lead there... On rugged landscapes, one can be trapped on a low "optimum" where any change leads to degradation and where it is hard to see that there are even higher optimum points elsewhere. Tight coupling between practices and the context of those practices (organizational environment factors) leads to rugged landscapes. So, we need to understand how strongly coupled the "practice" is to the "context" before we simply try to replicate it. If the practice is tightly coupled to a particular context, then it will be harder to replicate into another context. Everett Rogers talks about this in his book Diffusion of Innovation. There he observes that groups often need to "rediscover" the innovation. This has the effect of breaking the coupling with the distant context and creating useful coupling to the local context. A social system that facilitates rediscovery by valuing diversity, collaboration, risk taking, etc. might be more able to accept good practices initially discovered elsewhere. (Another reference is a paper by Mark Granovetter, "The Strength of Weak Ties." Glenda Eoyang's book contains a chapter on coupling.) For successful emergence, a complex system requires loose coupling in order to consider new ideas and tight coupling to engage them locally. Both the describer of the "best practice" and the receiver of the practice need to be conscious of this need to continuously play with coupling. (Jim Howard pointed out that Grey Elrodt did a study of diffusion of clinical pathways within VHA and found this loose-tight coupling phenomenon.) Rogers also points out that innovation often begins in the 2% "first adopters." These folks are often not listened to in their home organizations (loose coupling). The next 10% of "adopters" are key. They are respected in their organizations (tighter coupling) but they will also listen to the odd-ball early adopters. The idea of weak and tight coupling might also be related to the concept of legitimate and shadow organizations. Tight coupling in the legitimate and weaker coupling in the shadow. Diffusion of innovation might be easier in organizations where there is some degree of harmony among these aspects of the organization. Plsek post … "My question is: How would you know if a practice was tightly coupled to its context? What would you be able to observe that would indicate to you that simply replicating the idea back at your place was not going to be a simple matter? (The answers to these questions could be a great contribution to the "arts" of benchmarking and rapid cycle improvement based on change concepts.)" Levine post … I think the answer depends on a better understanding of the human relationships within the organization under review. It seems to me that process as a whole as well as the component parts of the process have differing levels of dependence on individuals. If we had a way to "measure" these individual relationships, we would have some sense of how well prepared another institution was to incorporate the benchmark institution's process. Dooley post… Remember Shortell's important recent research re hospitals and TQM: midstream and late adopters (70% of the total) of TQM do so because they think they must in order to remain "legitimate"; the adoption process has little to do with perceived or expected benefit. Therefore, organizational readiness or context--while I admit that it *should* matter--is largely ignored by organizational decision-makers. Complexity Concepts: Tune the CAS… Recall one of the "emerging principles of complexity:" *Tune* your place to the edge by *tuning* info flow, diversity, and so on. Tuning implies that sometimes you need more info, diversity, etc and sometimes you really do need less. Great opportunity to overlap with traditional QI approaches around the PDSA cycle of learning. Tune the info, diversity, etc *up* and reflect on what happens, tune it *down* and reflect. Continuously learning about the CAS. Tom Petzinger's Forklift Company example illustrates the use of tuned up info flow and tuned down power differential. They made information available as opposed to saying thou shalt do it this way. Netscape is getting improvement in its browser by sharing information about its source code with a diverse and increasingly connected collection of programmers on the internet. Complexity Concepts: Stacey Matrix… The Stacey matrix is a wonderful map for plotting issues. The questions are: Where are we on this diagram? What direction do we wish the system to go now (towards more adaptation or towards more certainty)? And what actions on my part, as a member of the CAS, are appropriate? Don't settle down forever in the lower left of Stacey. Yes, we might be certain and agree *now* about something, but we should also from time to time purposefully pull up into the mid zone and see if some adaptation is appropriate. A useful bit of language to remind us about this is the phrase: "current best practice for the time being." CAS theory gives us guidance on how to "play" the system on the Stacey diagram. To move toward the mid-zone: provide more information, stress divergence and diversity of thought, loosen the couplings, and so on. Vice versa to move the other direction. A key piece of information we need for adaptation is our true position on the Stacey diagram. Plsek post... I wish that we had a "stacey-o-meter" that we could dip into a CAS and read where it truly is. One practical way to do this would be to simply have some dialogue where everyone gets a chance to put a dot on the diagram where they think the system is on the particular issue under discussion. This would provide feedback to the system and provide a grounding for further discussion about options (maybe multiple options). Such simple reflection might have helped the Xerox situation described by Steve. Such simple reflection might keep many organizational "leaders" from wasting lots of time forcing clockware OR swarmware onto a situation when one or the other really isn't appropriate. What would our organizations be like if *everyone* in the organization understood and routinely used this one simple aide from complexity thinking? A potentially nice demonstration project… Teach the Stacey diagram to everyone in the organization... use it for extensively for reflection... see if the organizations capacity for successful action and adaptation is improved. Complexity Concepts: Holland's Framework… Another potentially rich set of ideas for understanding diffusion of good practice comes from John Holland's book Hidden Order. See Steve Larned 3/9/98 post for more information about how tagging, agents, internal models, aggregation, catalyzation, non-linearity, flows, and diversity might help us think through the issues around diffusion. Thoughts About Demonstration Projects… A natural place to demonstrate new thinking based on the complexity sciences would be in the midst of current efforts around EBM, clinical paths, guidelines, etc. that people are naturally finding frustrating. This current frustration would make for a natural resonance with new thinking from complexity. Does the current trend toward consolidation into larger organizations in healthcare make diffusion of innovation easier or harder?… (Post from Mark Levine) Consolidation in and of itself does add to the coupling challenge. I think it is imperative to remember that consolidation causes disruption to former relationships while at the same time causing opportunity to develop new relationships. Focusing on currently existing generative relationships and actively working to maintain those relationships, it would seem to me, is the key. Encouraging a new identity and structure in the consolidated organization that fosters development of new generative relationships is the other key. (An interesting article about medical systems can be found in the Feb 15, 1998 Annals of Internal Medicine Nolan, T Understanding Medical Systems p. 293.) Post from Al Herzog... Paul, in theory, these consolidations should make the diffusion of best practice into other organizations-from those with the best practice for `X`, much easier. In practice, this has not happened; certainly not in my system, and, as I listen to and read people like Brent James, neither in his. All of that provides some comfort, but ultimately much frustration. I think much of this has to do with trust--the level or degree of it between/among different people but there may be other, perhaps even stronger reasons. I struggle with this one but am so far without a good explanation. Open questions... Can we use complexity principles and Aides (eg. tune to the edge, coupling, fitness landscapes, shadow organizations, and so on) to build on and supplement existing thinking about diffusion of improvement? Can these additional complexity-inspired approaches help us even more as we move toward consolidation and ever-larger organizations in healthcare? Thoughts About Demonstration Projects… Need to do more thinking about this. Ultimate Goal of This Line of Thinking… Better diffusion of improvement knowledge, evidence, best practice, and results of benchmarking studies within today's healthcare organizations. What can leaders do that is different from what they are currently doing (which is leading today to lots of frustrations)?
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• Enabling Natural, Adaptive Improvement
  

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  		3. Enabling Natural, Adaptive Improvement   Issues and Open Questions… Complexity sciences suggest that evolution and adaptation toward a generally improving state are natural behaviors in CAS. In the context of typical quality improvement efforts, this observation leads to several questions: What information do we need to make effective adaptability decisions? What information do we need in order to learn from these decisions? Would some improvements happen more naturally if we decreased the amount of information available? Do the organizational structures that we have put in place hoping to enable improvement (for example, quality councils), actually just get in the way of adaptive improvement? Can we demonstrate innovative, complexity-inspired approaches to improvement? These questions have been posed, but there has not yet been any substantial discussion of them. This goes to the heart of the relationship between complexity and the Deming/Shewhart PDSA cycle. Notes About Where We Might Focus Under This Topic… We really have done very little development on this thread. This thread could be merged with the diffusion of innovation and best practice thread. It is also important to note that VHA has just formed a new group to address the topic of building more adaptive organizations, so we could defer to that group for more development on this issue. On the other hand, the use of structures like quality councils and formal committees/teams is so pervasive in QI efforts that if complexity has some new insight to offer, we should take advantage of that to break new ground here. The discussion that we had on-line based on the Tom Petzinger case about the Infection Control effort might serve as a good base for further development on this line. Complexity Concepts That Might Help… What information do we need to make effective adaptability decisions and to learn from those decisions? The information needed is not likely to be apparent to those involved -- so, avoid converging too early. A big issue here involves "tuning" the information flow. In many cases, we see that there is too much information; or, better said, too much data and too little information. How can CAS ideas help us separate out information amid the data chatter. John Holland's language in Hidden Order (tagging, agents, internal models, aggregation, catalyzation, non-linearity, flows, and so on) might be helpful here. Also, understanding coupling, diversity, and Kaufmann's fitness landscapes. Thoughts About Demonstration Projects… Need to do more thinking about this. Ultimate Goal of This Line of Thinking… More rapid, naturally-occurring, efficient, and effective quality improvement efforts in healthcare organizations. Getting back to the notion that continuous improvement is really a fundamental professional ethic in healthcare.
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• Metaphor and Leadership
  

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  		3. Enabling Natural, Adaptive Improvement   Issues and Open Questions… Complexity sciences suggest that evolution and adaptation toward a generally improving state are natural behaviors in CAS. In the context of typical quality improvement efforts, this observation leads to several questions: What information do we need to make effective adaptability decisions? What information do we need in order to learn from these decisions? Would some improvements happen more naturally if we decreased the amount of information available? Do the organizational structures that we have put in place hoping to enable improvement (for example, quality councils), actually just get in the way of adaptive improvement? Can we demonstrate innovative, complexity-inspired approaches to improvement? These questions have been posed, but there has not yet been any substantial discussion of them. This goes to the heart of the relationship between complexity and the Deming/Shewhart PDSA cycle. Notes About Where We Might Focus Under This Topic… We really have done very little development on this thread. This thread could be merged with the diffusion of innovation and best practice thread. It is also important to note that VHA has just formed a new group to address the topic of building more adaptive organizations, so we could defer to that group for more development on this issue. On the other hand, the use of structures like quality councils and formal committees/teams is so pervasive in QI efforts that if complexity has some new insight to offer, we should take advantage of that to break new ground here. The discussion that we had on-line based on the Tom Petzinger case about the Infection Control effort might serve as a good base for further development on this line. Complexity Concepts That Might Help… What information do we need to make effective adaptability decisions and to learn from those decisions? The information needed is not likely to be apparent to those involved -- so, avoid converging too early. A big issue here involves "tuning" the information flow. In many cases, we see that there is too much information; or, better said, too much data and too little information. How can CAS ideas help us separate out information amid the data chatter. John Holland's language in Hidden Order (tagging, agents, internal models, aggregation, catalyzation, non-linearity, flows, and so on) might be helpful here. Also, understanding coupling, diversity, and Kaufmann's fitness landscapes. Thoughts About Demonstration Projects… Need to do more thinking about this. Ultimate Goal of This Line of Thinking… More rapid, naturally-occurring, efficient, and effective quality improvement efforts in healthcare organizations. Getting back to the notion that continuous improvement is really a fundamental professional ethic in healthcare.
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• Collaborative Learning
   

Adaptible Org

• Attractors & Culture
  

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  		Attractors & Culture One of the most exciting ideas in complexity theory is the concept of attractors, which completely reframes the phenomena of resistance to change. This concept suggests that the dynamics of a complex system are always following attractors. Nothing is resisting anything, instead, behavior and ideas and attitudes are following the attractors. If that is the case, then the issue is not how to overcome resistance but how to work on the level underlying, or creating, the attractors (complexity suggests that "simple rules" create attractors). (you may know you are working at this level when you seem to be going with the "natural" energy in the system. It also suggests loosening up the bound nature of the existing attractors and self-fulfilling prophecies by fostering far-from-equilibrium conditions. This contrasts with the traditional approach to overcoming resistance - applying more and more pressure (you may know when you are doing this when your frustration and impatience are rising), which then sets up a compensating feedback which pushes back against this pressure. The group observed that the hold of the status quo can be very strong. In looking at some specific organizations the group commented on the power of the "do nothing" and "not good enough" attractors. The group asked - What makes the status quo attractive? What conditions make it more desirable than anything else? Another observation was that resistance to change seems to be much more a result of how management goes about implementing change than anything else. Managers create resistance. Organizational culture seems to be a powerful determining factor of what the attractor patterns are (in a sense similar to how the climate has a role in determining the attractors for states of the weather). Awareness of culture, and its specific nature, is the starting point. One member of the group noted "We all look at the world through our own glasses. A great sense of freedom attends the recognition that you are wearing glasses at all." If you are not aware of something you cannot facilitate change or appreciate the need for change. A notion proposed by a member of the group was that attractors were a set of ideas in an organization that have value. These ideas influence people’s values and behaviors, which in turn create certain dynamics in an organization. And, it is from dynamic interactions that emergence comes. In this case, from people’s behaviors and values, the kinds of relationships they form, that a culture emerges, which in turn influences people’s behaviors – the feedback loop. Which led to the insight that you can’t change culture, the emergent property, at the macro level, but you can influence what emerges by changing the micro level - people’s behaviors and beliefs. In other words, you don’t change a culture, you create conditions so people can change. And the micro and macro change at different paces. People’s behaviors can change quickly at times, but it takes the culture longer to change. Since the interactions between agents who mutually affect one another is the source of emergence, then relationships are the bottom line. The attractor in complexity thinking is valuing relationships in and of themselves. In the organizations that value relationships, a sense of community emerges. In this context, people spoke of being more able to adapt, better able to deal with ambiguity and uncertainty, willing to be more open-ended and flexible – because there was a web of support. This in turn made the organization more adaptable. The group also briefly explored the use of stories and narratives, notably those which are honest and authentic, as a means of fostering healthy relationships in organizations and in helping people in the organization understand the organization and its culture. Worth pursuing - How to uncover, understand and work with the simples rules underlying current attractors.
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• Emergence
  

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  		Emergence The concepts developed in this portion of the digest came primarily from the experiences in facilitating emergence shared, and then explored, by group members. Before delving into the resulting insights a cautionary note should be made. It has to do with the belief in many organizations that the answers can always be found at the top, ultimately from the chief executive officer. Here is how it is described in one organization – "Our administrative team has for so long looked to the CEO for direction, and approval that they defer all strategic decisions to him. They are so busy with operational issues that they almost never step back and ask some fundamental questions. Like why? What? To what end? Does this make any sense? It then becomes their expectation of him and this of himself. Indeed a self-reinforcing process." Techniques and approaches uncovered and used by members of the group that they believe contribute to constructive emergence are as follows: Increase the flow of information in the system (i.e. an email discussion throughout the organization about organizational values). Operationalize the regular use of fundamental questioning (i.e. use of "wicked questions"). Keep size of work groups, teams, organizational units/subunits relatively small - in line with the number of relationships most people can handle. Some research, based upon the brain’s capacity and clan size in hunter-gatherer societies, suggests up to 12 for teams and no more than 150 for organizational units. Pay attention to our own 15% sphere of responsibility and influence, pretty soon others move. Remember that you are "of" the system, not apart from the system. Traditionally, strategy, change and adaptation literature in management implicitly suggests that we can (1) understand the system by observing it and then (2) intervene in the system by injecting something into it. The problem with this notion is it suggests that we can somehow be apart from the very system we are interacting with. What we need to do is give up the idea of being an "outsider" and go into the much more uncomfortable area of seeing yourself as a participant   Pay attention to the role of redundancy. One member of the group observed - "One the big differences I have observed in our culture as compared to others relates to redundancy. Knowledge and skill in our place is shared openly and widely so if there's something you want to try or know it is very easy. In a very politicized, command and control environment information and skills are carefully guarded to ensure longevity or security or something."   Help CEOs (and others too) find a "safe place" to explore the new, sometimes personally challenging concepts of complexity. Appreciate the strategy of sometimes of "mulling stuff over and letting the deciding hang" – waiting for emergent solutions to occur as opposed to forcing a decision that may be a poor one.   Engage the organization through action, using complexity-inspired approaches on difficult organizational challenges. This enables people to learn from direct experience and appreciate how concepts from complexity can trigger fresh thinking and more workable solutions. An example of this approach came from one group member – "We have done a large group program which puts a cross section of the system, not just the leaders, in a room at the same time and has the group work on a wicked question, choose several appealing courses of action, try them out quickly, all in few hours. The approach provided a bridge for people to gain some experience with complexity principles while still feeling somewhat safe." Complexity principles beneath this approach were tension and paradox, diversity, edge of chaos, multiple actions.   In the group’s work on emergence some time and attention was devoted to helping members deal with real issues members they were facing that related to the general topics of adaptability and emergence. This effort led to some concluding observations, which added support to the point in the previous section on attractors and culture about the central role of genuine, open, and caring relationships in fostering an adaptable, creative culture. Here is a sample of actual postings. "I think it is great the way you support each other ... and in a way that is consistent with the theories we are exploring. You look for the patterns in each other’s work that reveal the hidden potentials in you or your work places. I am so glad that people of your caliber and spirit are devoting your efforts to health care." "Thank you. I have been in need of some energy and renewed optimism. I need to return to the 15%, and "helping to create conditions in which the crops grow" and to stop trying to push a boulder uphill." This experience in the group also buttressed quite a number of the points made above – learning by work on real issues, creating conditions for emergence - openness, information flow, sufficient safety, diversity of experiences… Worth pursuing – How can leaders learn to distinguish seeds of emergence from mere "serendipitous novelty." If so, how? What do leaders need to do in order to set-up conditions that tend toward more constructive than destructive outcomes? This issue was raised but not explored in any depth. Worth pursuing – How to provide a safe way for some CEOs and other leaders to come to terms with the expectation they have of themselves, and the organization has of them, that they must have the best, right answers.
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• Personal "Edge" Experiences
  

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  		Personal "EDGE" Experiences One of the issues raised above was helping leaders become aware and more comfortable with a different kind of control, where direction and creative new approaches emerge from a healthy system, instead of plans being imposed on the system by leaders. To explore this issue members of the group explored what if felt like to be operating at the edge, in the creative far-from-equilibrium space. A clear, consistent pattern was evident - paradoxical emotions. Here is what people said. "it was some mix of excitement, curiosity, anxiety - that we have all experienced - and which some folks take as a sign that "intriguing possibilities ahead." "To be able at any moment to sacrifice what we are for what we could become." "I've lost it, now over the edge, but my intuition is telling me to push ahead, keep working this stuff." "I had the strangest feeling of absolute calm that I was doing the right thing and yet real anxiety and fear that I was awfully close to the edge of a cliff." "I'm pulling my hair out, chewing my nails, staying up at nights, but still feeling that things are good and getting better."   Helping others appreciate what it actually feels like to be in this territory and appreciate the fact that it is a good place to be if change and creativity are needed was view were viewed as essential in helping leaders let go of traditional means of control. Relating this concept to personal experiences of significant growth and change was viewed as an effective means of fostering understanding of this principle. Worth pursuing - What enables people to work at the edge? Is it past experience, support from others, knowing the theory…?
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• Vision
  

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  		Vision In the group’s consideration of this topic a number of issues were explored - foresight versus insight vision (insight into the current state) and the efficiency/inefficiency of a "good enough" vision. In organizations we have traditionally been concerned about foresight, or a vision of the future. One of the paradoxes in complexity is that when a system is far-from-equilibrium it is adaptable but unpredictable. What makes it adaptable though is the increased capacity for "sight", or understanding of the current context. Vision in a complexity context becomes something like a belief in the underlying order of the process, requiring a rethinking of vision from seeing to believing. A member of the group expressed it this way - "It seems to me I've given up the hope of having foresight. The most I have is the ongoing confidence that the people I work with, and me with them, will choose the best place to put our foot down next as we wander around the wilderness of organizational living… Making the best of each place requires a kind of collective consciousness that I feel more than see. Also in this conversation was an examination of the concept of "good enough" vision and whether its use was inefficient. "Good enough" recognizes that you cannot have a clear and explicit vision for the future in an inherently unpredictable system. The best you can do is "good enough" and then to get moving, acting, and watching for patterns and direction to emerge. Reference was made to the similarity of this conception of vision to the "semi-coherent strategy" advanced by Brown and Eisenhardt in their new book Competing on the Edge. The conclusion of the discussion was that following the "good enough" vision approach was, in the long-run as efficient as one could expect to be in uncertain times. And definitely more efficient than wasting time trying to predict the unknowable.
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• Measures of Adaptability
  

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  		Measures of Adaptability There was some exploration of how an organization could measure its adaptability. A difficulty encountered was gaining a clear and tangible grasp of adaptability per se, suggesting therefore that it be measured indirectly – through the factors underlying and fostering organizational adaptability Some candidate measures of adaptability were: Connectivity – it was suggested that increased connectivity among agents inside a system and between a system and its environment leads to a greater potential for adaptability because of increasing information flow. That is, the more connections, the more frequent the connections occur, the richer the history of connections, the broader the range of connections, the more information flows across the connections, the higher the quality (however defined) of the connections, the more enduring they are, then the more adaptable the system will be. "Maps" of connectivity among individuals, work groups, departments, and with external systems on could be drawn with some kind of weighting scheme indicating the various parameters of connectivity. The point here is not the same as measuring the number of connections but rather the richness and characteristics of the connections. Reference was made to Lane & Maxfield’s construct of generative relationships and the dimensions of action opportunities, aligned direction, difference, and the ongoing nature of the relationships.   Transforming Feedback Loops – (from Eoyang) – a connection between two or more persons that serves to transform each party. Several parameters of feedback loops: length – distance of time of information exchange; width – how many different types of information can travel across the loop at the same time.   Departures From The Norm – suggesting that some degree of ongoing "instability" or "far-from-equilibrium" variability seems to be more conducive for innovation.   Innovation Initiatives – is there some way to measure the number of spontaneously emerging projects and initiatives, and not just the new ideas, but their implementation as well?   Management Non-Interventions – the notion here is the concept of distributed control, including the degree of authority vested in a job and how much of an organization’s authority is distributed rather than concentrated at higher levels. It was suggested that we consider measuring how many times managers don’t intervene or refuse to make a decision.   Wicked Questioning – how can we measure the degree and frequency of challenges to the equilibrium? Possibilities - number of challenging statements, impact, who voiced them, who listened, who acted upon, what was changed as a result, is there a reward for such challenges.   This discussion triggered questions about whether measurement is the right concept. Some said that complexity is more about qualities than quantities. The idea of indicators was broached, as it is less laden with negative and mechanistic connotations and implicitly suggests the value of reflecting on the observable.
  		{HYPERLINK "main_app-jj.html"} Next  | {HYPERLINK "main_app-hh.html"} Previous  | {HYPERLINK "main_apptoc4.html" \l "contents"} EdgeAdapt Contents  | {HYPERLINK "main_apptoc.html"} Applications {HYPERLINK "main_apptoc.html"} Contents   Copyright © 2000, VHA Inc. Permission to copy for educational purposes only.

  
  
  

  
   
• What Works
  

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  		Measures of Adaptability There was some exploration of how an organization could measure its adaptability. A difficulty encountered was gaining a clear and tangible grasp of adaptability per se, suggesting therefore that it be measured indirectly – through the factors underlying and fostering organizational adaptability Some candidate measures of adaptability were: Connectivity – it was suggested that increased connectivity among agents inside a system and between a system and its environment leads to a greater potential for adaptability because of increasing information flow. That is, the more connections, the more frequent the connections occur, the richer the history of connections, the broader the range of connections, the more information flows across the connections, the higher the quality (however defined) of the connections, the more enduring they are, then the more adaptable the system will be. "Maps" of connectivity among individuals, work groups, departments, and with external systems on could be drawn with some kind of weighting scheme indicating the various parameters of connectivity. The point here is not the same as measuring the number of connections but rather the richness and characteristics of the connections. Reference was made to Lane & Maxfield’s construct of generative relationships and the dimensions of action opportunities, aligned direction, difference, and the ongoing nature of the relationships.   Transforming Feedback Loops – (from Eoyang) – a connection between two or more persons that serves to transform each party. Several parameters of feedback loops: length – distance of time of information exchange; width – how many different types of information can travel across the loop at the same time.   Departures From The Norm – suggesting that some degree of ongoing "instability" or "far-from-equilibrium" variability seems to be more conducive for innovation.   Innovation Initiatives – is there some way to measure the number of spontaneously emerging projects and initiatives, and not just the new ideas, but their implementation as well?   Management Non-Interventions – the notion here is the concept of distributed control, including the degree of authority vested in a job and how much of an organization’s authority is distributed rather than concentrated at higher levels. It was suggested that we consider measuring how many times managers don’t intervene or refuse to make a decision.   Wicked Questioning – how can we measure the degree and frequency of challenges to the equilibrium? Possibilities - number of challenging statements, impact, who voiced them, who listened, who acted upon, what was changed as a result, is there a reward for such challenges.   This discussion triggered questions about whether measurement is the right concept. Some said that complexity is more about qualities than quantities. The idea of indicators was broached, as it is less laden with negative and mechanistic connotations and implicitly suggests the value of reflecting on the observable.
  		{HYPERLINK "main_app-jj.html"} Next  | {HYPERLINK "main_app-hh.html"} Previous  | {HYPERLINK "main_apptoc4.html" \l "contents"} EdgeAdapt Contents  | {HYPERLINK "main_apptoc.html"} Applications {HYPERLINK "main_apptoc.html"} Contents   Copyright © 2000, VHA Inc. Permission to copy for educational purposes only.

  
  
  

  
   
• Wondering About
  

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  		Wondering About Here is the place where EdgeAdapters listed complexity concepts they would like to learn more about and use more effectively. Uncovering and working with paradox and tension seems to be a set of skills many of us lack. Prigogine, in his book The End of Certainty, makes a very interesting analysis which indicates that if indeed stable dynamical systems without instabilities (tensions and paradoxes) were the key driving forces in our world, humans would not exist. So it seems that it isn't just managers who are lacking in skills in this arena or uncomfortable with this concept. Western philosophy, religion, science and management have all suffered from the belief that instability is bad rather than the life force. But students of life (ie. doctors and nurses) intuitively know this already. So maybe talking to them about their observations in health and illness is a key means for accessing what we already"know" - that stability is not an evolutionary force or a creative energy. How to make decisions on which organizational "seedlings" to allow grow or to weed out. The trick is when to make the decision - waiting too long may allow a bad idea to take root, but acting too fast may kill off a promising new hybrid. Should we not have more confidence that if the underlying purposes and values of the organization are firmly rooted these decisions will be made environmentally, rather than having some controlling authority. Even though this sounds good, what if the and values need to evolve because they are killing off great new emergent advances? Worth pursuing - The point made above that physicians and nurses "know" about the value of instability for life. Is tapping into this one way to stimulate the creation of our critical mass?
  		{HYPERLINK "main_app-ll.html"} Next  | {HYPERLINK "main_app-jj.html"} Previous  | {HYPERLINK "main_apptoc4.html" \l "contents"} EdgeAdapt {HYPERLINK "main_apptoc4.html" \l "contents"} Contents  | {HYPERLINK "main_apptoc.html"} Applications Contents   Copyright © 2000, VHA Inc. Permission to copy for educational purposes only.

  
  
  

  
   

Ed theory | complexity | technology

» See document: http://carbon.cudenver.edu/~mryder/itc_data/theory.html