"There are
two kinds of truth. There are superficial truths, the opposite of which are obviously
wrong. But there are also profound truths, whose opposites are equally right." --Niels Bohr
Key Points:
-
Individual agents
- Interpretation and action is based on mentalÂ
models
- Agents can have their own or shared mentalÂ
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 interactionÂ
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.
1. McKinsey Research
