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Schemata

Encyclopedias may be thought of as examples of schemata. They provide a structured and coherent representation to a certain domain of interest.

Encyclopedias may be thought of as examples of schemata. They provide a structured and coherent representation to a certain domain of interest.

A schema is a conceptual pattern or structure that organizes categories of information and the relationships among them.1 The term derives from psychology but is used within complex adaptive systems theory to denote the internal logic governing an agent’s behavior. The internal logic or schema that governs the behavior of agents within complex adaptive systems spans from the very elementary to the very complex. The most basic type of logic is called an algorithm and more advanced conceptual systems may be called schemata.

Algorithms

The most basic form of logic an agent can have is one that simply responds to a given input signal with an output action that is always the same. For example, if one taps one’s knee at the right location it will trigger the nerves to actuate the muscles into generating a sharp reactionary motion. Every time we input the same stimulus to this physiological system we will get the same response. This is the most basic algorithm conceivable, always mapping the same input to the same output.
More advanced algorithms are able to discern between a given set of inputs and use an if-then logic to select an appropriate output. For example, the control system within a chemical processing plant might be able to select from a set of output temperature values based upon a range of input temperature values in order to regulate a chemical process chamber.2 An example of a basic algorithm might be the rules that are thought to govern the flocking of birds. They consist of just three simple rules which are; one, separation, meaning always maintain a certain distance from your neighbors; two, alignment, meaning steer towards the average heading of your neighbors; and three, cohesion, meaning to steer towards the average position of neighbors in order to maintain long range attraction. Here the individual bird is continuously inputting a value to these three required parameters, processing this information according to the set of instructions and then selecting from a range of appropriate motion responses in order to maintain its correct positioning.3

Schemata

As advanced as these algorithms may become they are essentially designed to just generate a response to a given range of stimuli. As such, they capture much of the logic behind mechanical control systems and those governing many biological systems such as in our bird example above. But the advanced cognitive capability of a modern human being far exceeds a simple set of algorithms. With this cognitive capacity, human agents can create conceptual representations or models of the world and we call these schemata.
The word schema comes from the Greek word meaning to shape, or more generally, plan. A schema is a cognitive framework or concept that helps organize and interpret information. As such, it is a conceptual template that determines how reality is interpreted, and from this what are appropriate responses to a given stimuli. With a schema, an agent can create a model of what it encounters, identify similarities and differences amongst things in order to create categories and relations between categories. This allows an agent to quickly take in new information and classify it with reference to what it already knows. Every time an agent receives new information it references it against the information it already has.

Bayesian Inference

This process of obtaining new information and filtering it to ensure its validity is often modeled using Bayesian inference. Bayesian inference references any new information received by the agent against prior knowledge in order to ascribe a probability value to the likelihood of its validity.4 If the information is deemed to have a high probability of validity it is incorporated into the agent’s schema and used as a reference to infer the validity of any future information it receives. For example, throughout one’s life, one have received constant information endorsing the validity to the existence of the force of gravity.
This massive amount of information confirming it gives it a very high probability of being valid, and every day that probability goes up as one receive more confirmation of its existence, the result being that if you are presented with some piece of information that disproves the existence of a gravitational force on planet Earth your immediate reaction will be to ascribe this new piece of information with a very low probability of being valid. In this way, a schema can develop as it receives new information and incorporates this into the framework, both reinforcing preexisting categories and reducing the overall state of uncertainty as new information confirms or disaffirms the space of unknown possibilities.

Complex Representations

Schemata are complex models of the world as seen in this image of a young scientist modeling the molecular structure to a substance

Schemata are complex models of the world as seen in this image of a young scientist modeling the molecular structure to a substance

With a schema, we have not only the basic functioning of a control system that is able to respond to an immediate stimulus but by being capable of creating a complex model of a situation we can understand what is generating this stimulus in the first place. A schema allows the agent to identify the causes that create the effects. And not only this, but an agent with an advanced schema is able to also create a model of its own operation, that is, how it responds to any given stimulus, and can then try to alter this basic behavior. For example, we might be able to identify that every time we get stressed we start smoking and then try to alter this reaction. This somewhat self-referential capacity for a system to model and analyze its own regulatory system is the subject of what is called second-order or new cybernetics.5 These advanced schemata, of course, have many benefits to an agent over a simple algorithmic logic. It is ultimately the foundation that has enabled technology, advanced civilization, and human’s capacity to dominate its physical environment. But of course it comes at a cost, and not only in terms of the physical energy to maintain the system. But there is now a tension between the basic control system that is designed to react to stimulus, thus ensuring immediate self-preservation, and the schema that creates a broader vision interested in the system’s long-term objectives and consequences of its actions, with the possibility of these two levels conflicting and reducing the agent’s capacity for action.
Human agents within complex adaptive systems are not only governed by the need for physical self-preservation but being governed by these advanced conceptual frameworks they are required to maintain both conceptual homeostasis as much as physical homeostasis. Through a number of mechanisms, information can be systematically filtered to ensure it does not threaten the basic assumptions that support the schema and that the system is in regular contact with information sources that endorse and preserve this current schema because it is critical to the functioning of the whole system.
Psychology has plenty of examples of this, such as confirmation bias which is a tendency to search for or interpret information in a way that confirms one’s pre-existing schema and placing much higher validation standards on information that threatens it.6 In the same way, agents actively seek out environments that are inductive to their physical requirements, they will often actively seek out information sources that preserve and maintain the status quo of their schema. Thus, we should not expect human agents to be rational or logical. Ultimately, humans are not computers where logic is a precondition to their operation, but there is instead a subjective dimension to humans that is driven by emotions and independent from logical validation.

Culture

Geisha moves through the crowd on April 8, 2013 in the Gion district of Kyoto. The Geisha are the highest ranked in Japan. Cultural displays like this are manifestations of people's representation of their world.

Geisha moves through the crowd on April 8, 2013 in the Gion district of Kyoto. The Geisha are the highest ranked in Japan. Cultural displays like this are manifestations of people’s representation of their world.

This subjective domain to human agents is played out in what we call culture. E.B. Tylor defined culture as “that complex whole which includes knowledge, belief, art, morals, law, custom and any other capabilities and habits acquired by man as a member of society.” Culture often comes in the form of a story or a set of stories about how the world is that endorse what is considered right and wrong, with people then acting out these stories as rituals in order to validate them and feel a part of them.
People buy Nike shoes because advertising agencies have created a story around the brand. People want to be associated with that and they live this story out by wearing the shoes. There is no economic logic as to why people would pay an extra 50 dollars to buy a pair of shoes with a tick on the side of them. Much of human activity only makes sense within the context of the cultural narrative that it is a part of. This may add a whole new level of complexity to our models but we pay a high price when we exclude it in terms of capacity to capture the real-world phenomena exhibited by many complex adaptive systems.

Cite this article as: Joss Colchester, "Schemata," in Complexity Academy, April 15, 2016, http://complexityacademy.io/schemata/.

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