bullet1 Overview

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

bullet2   CAS

Key Points:
  • Individual agents
  • Interpretation and action is based on mental  models
  • Agents can have their own or shared mental  models
  • Mental models can change; i.e., learning is possible
  • Interconnections among agents
  • Actions by one agent changes the context for  others
  • System behavior emerges from the interaction  among agents
  • The system can exhibit novel behavior
  • The system is non-linear; small inputs can lead to  major outcome swings
  • System behavior is fundamentally unpredictable at  the detail level
  • Broad-brush prediction of system behavior is  sometimes possible
  • Order is an inherent property of the system, it need  not be imposed

In a CAS, agents operate according to their own internal strategies or mental models (the technical term is "schemata"). In other words, each agent can have its own "rules" for how to respond to things in its environment; each agent can have its own interpretations of events. These rules and interpretations need not be explicit. They do not even need to be logical when viewed by another agent. These are clearly characteristics of humans in just about any social system.

Agents can share mental models, or be totally individualistic. Further, agents can change their mental models. Because agents can both change themselves and share mental models, a CAS can learn; it's behavior can adapt over time. Again, we clearly know that human organizations change over time and are capable of progress.
The behavior of a CAS emerges from the interaction among the agents. A CAS can, and usually does, exhibit novel behaviors. Because of the interaction, the behavior of the system is also non-linear; seemingly small changes can result in major swings in system behavior. If you reflect on this, you can probably recall many examples of these behaviors in human systems. We are usually surprised when they happen. However, when we learn to view systems through the lens of CAS, these behaviors become expected, not surprising.

Because of this novelty and non-linearity, the detailed behavior of a CAS is fundamentally unpredictable. It is not a question of better understandings of the agents, better models, or faster computing; you simply cannot reliably predict the detailed behavior of a CAS through analysis. You have to let the system run to see what happens. The implications of this are that we can never hope to predict the detailed behavior of a human system. While this seems obvious to say, note how often managers and leaders (we ourselves!) act as if we know or can be sure about how others should act in response to our actions.

Still, despite this lack of detailed predictability, it is often possible to make generally true, practically useful statements about the behavior of a CAS. For example, while we cannot predict the exact temperature in Atlanta at 4:49 pm on August 4, we can say that it is pretty likely that a traveler there will not need a heavy coat. This gives us some hope in human systems, we just need to be careful not to over-estimate our ability to predict what will happen. Over-estimation is the usual mistake that we all make; if you have ever been surprised by how something has turned out, you have fallen into the trap of over-estimation.

Stuart Kauffman, Ilya Prigogine, and others have shown that a CAS is inherently self-organizing. Order is an inherent property of the system; it does not need to be imposed from outside. Further, in a CAS, control is dispersed throughout the interactions among agents; a central controller is not needed. Yet, most of traditional management theory is about how to establish order and control through the actions of a few people at the top of an organizational hierarchy. This management instinct, one that we have all learned, may be the biggest factor holding back progress in our organizations.


bullet2   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.


     

bullet2 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



     

bullet2 Principles
{PRIVATE "TYPE=PICT;ALT=Edgeware- Principles"}
Conclusion  

Our existing principles of leadership and management in organizations are largely based on metaphors from science that are hundreds of years old. It is time that we realized that science itself has largely replaced these metaphors with more accurate descriptions of what really happens in the world. Science is replacing its old metaphors not because they are wrong, but because they only described simplistic situations that progress has now moved us well beyond. Similarly, our organizations today are not the simple machines they were envisioned to be in the Industrial Revolution that saw the birth of scientific management. Further, people today are no longer the compliant “cogs in the machine” that we once thought them to be. We have intuitively known these things for many years. Management innovations such as learning organizations, total quality, empowerment and so on were introduced to overcome the increasingly visible failures of the simple organization-as-machine metaphor. Still, as we have pointed out, the metaphor remains strong.

The emerging study of complex adaptive systems gives us a new lens through which we can now begin to see a new type of scientific management. This new scientific management resonates well with more modern, intuitive notions about what we must do to manage increasingly complex organizations today. More importantly, the new thinking in science provides a consistent framework to pull together these heretofore intuitive notions. Now, for example, advocates of open communications and empowerment can claim the same firmness of ground that advocates of structure and control have been claiming exclusively. Science can now say rather clearly that structure and control are great for simple, machine-like situations; but things such as open communication, diversity and so on are needed in complex adaptive systems – such as those in modern organizations. The new scientific management will, no doubt, revolutionize organizations in the coming decades much as the old scientific management changed the world in the early decades of this century.


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  • "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



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

     

     

       


    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.

     



     
  • 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


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

     

     

      Next | Previous | Return to Contents List


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    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



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

     


     

     

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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.

     



     
  • 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.





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    "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