Complex Adaptive Systems
Complex Adaptive Systems (CAS) are a way to operationalise the complexity paradigm.
What is the complexity paradigm
The complexity paradigm looks at systems as fluid evolving things. It attempts to build fuzzy, multivalent, mult-level and multi-disciplinary representations of reality.
As systems are fluid and evolving, control and order is understood as emergent rather than predetermined. Systems transition naturally. This is the concept of a CAS. Examples would be weather, economies, ecologies, social, organisations, cultures...
What is a CAS
The basic element of a CAS is agents. Agents are semi-autonomous units that seek to maximise fitness by evolving over time.
Agents scan their enviornment and develop schema. These are mental templates that define how reality is interpreted and what appropriate responses to stimuli are.
Schema are made up of smaller schema. When an observation doesn't fit, either the observation can change or the agent can purposfully alter the schema. Mutation of schema can also happen randomly. When schema change it generally has the effect of making agents:
- More robust (can perform in light of increasing variety)
- More reliable (can perform more predictably)
- More capable (can perform in wider conditions)
Less fit agents are more likely to change schema. Optimisation of local fitness allows differentiation and novelty (think market competition), global optimisation enhances the CAS coherence as a system and induces long term memory.
Schema define how agents interact with other agents. Actions involve exchange of information or resources. Flows can be non-linear. Flows can have multiplier effects based on the nature of interconnectedness..
Last update: 07-03-2022 08:12