Diagram: Schemata


CAS Systems develop order or pattern ‘for free’: this means that order arises as a result of independent agent behaviors, without need for other inputs.

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Emerging Schemata

In some systems agent rules are fixed: the agent has no ability to change its operational schema and rules are static. In others, agents learn over time, with the results of past rule enactments serving as a source of input, or data, which can then be used to generate new rules. Gradually, agents reorganize their rule protocols in order to better align with their environmental inputs.

Another way to conceptualize such adjusting 'rules' is with the notion of 'schemata'. Murray Gell-Mann, one of the pioneers of complexity thinking, positioned  schemata as the kinds of 'models' agents form of their environment. Agents enact different kinds of rules, with each rule being a kind of proposition about how the agent can best 'fit' within the environmental context. When the context pushes back - responding or not responding well to a particular rule set, the agents are forced to revise their schemata or model (with a resulting revision of rules). Each rule is therefore a kind of fitness proposition: a 'schemata' of how to operate within a given context, and these schemata are themselves evolving.

The difference between 'rules' and 'schemata' seems to imply a more active, cognitive model of context being generated.  Agents operating via simple rules have no need of a 'model' of their context: they simply require feedback on whether or not a rule achieved a goal, and then a rule about how and when to change rules.

But the notion of schema implies a more active formulating about the context of operation - a kind of sleuthing around the environment, where rules are not simply blind variations, but are subject to an emerging proposition about the nature of a context. As such, schemata (or 'schema' as they are sometimes called), alludes to competing models of the context that an agent believe itself to be operating within, and how the shifting models sets then effect the nature of the subsequent rule sets.

Gell-Mann writes:

"each schema provides, in its own way, some combination of description, prediction and (where behavior is concerned) prescriptions for action"

provided that they are responding to a context that contains a meaningful 'difference', and provide that  there are a sufficient number of them, and provided that their rule regimes generate correlation amongst them, the system


Cite this page:

Wohl, S. (2019, 6 November). Schemata. Retrieved from https://kapalicarsi.wittmeyer.io/definition/emergence-of-fit-behaviorsprotocols

Schemata was updated November 6th, 2019.

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Santa Fe Institute; Fitness Landscape

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    Complex Adaptive Systems become more 'fit' over time. Depending on the system, Fitness can take many forms,  but all involve states that achieve more while expending less energy.

    What do we mean when we speak of Fitness? For ants, fitness might be discovering a source of food that is abundant and easy to reach. For a city, fitness might be moving the maximum number of people in the minimum amount of time. But fitness criteria can also vary - what might be fit for one agent isn't necessarily fit for another. For example, what makes a hotel room 'fit'? Is it location, or price, or cleanliness, or amenities, or all of the above?  For different people, these various factors or parameters have different 'weights'. For a backpacker traveling through Europe, maybe the price is the only thing worth worrying about, whereas for a wealthy business person it may not factor in at all.

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