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Diagram: Schemata

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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  • This is a list of Terms that Schemata is related to.

    Complex Systems are generated from the local interactions of multiple AGENTS. These agents are not actively striving to achieve any form of 'global' structure or pattern, but simply behave in their own self interests. Hence, we describe CAS dynamics as being generated from 'the bottom up' as opposed to 'the top down' as would be the case in traditional hierarchical organizations.

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  • This is a list of Urban Fields that Schemata is related to.

    Many cities around the world self-build without top-down control. What do these processes have in common with complexity?

    Cities around the world are growing without the capacity of top-down control. Informal urbanism is an example of bottom-up processes that shape the city. Can these processes be harnessed in ways that make them more effective and productive?
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    Across the globe we find spatial clusters of similar economic activity. How does complexity help us understand the path-dependent emergence of economic clusters?

    Evolutionary Economic Geography (EEG) tries to understand how economic agglomerations or clusters emerge from the bottom-up. This branch of economics draws significantly from principles of complexity and emergence, seeing the rise of particular regions as being path-dependent, and trying to understand the forces at work that drive change for economic agents - the firms that make up our economic environment.
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  • This is a list of Key Concepts that Schemata is related to.

    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?

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