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What are social structures built of?

Topic: Emergence
by Jack, 2020 Cohort

The concept of emergence is not universally or strictly defined, but typically it centres on how a system can be entirely defined by a set of components or parts, while exhibiting properties beyond those components individually. Often the components of a complex system have certain predictable behaviours, following a set of rules or trends as they act and interact with ‘neighbouring’ components. As many neighbouring components repeatedly interact and influence each other, wider system properties can develop or change. Emergence has traditionally been used to study phenomena in physics and biology. Classical examples of emergence include:

• Living cells which comprise ‘simple’ chemical compounds
• Human consciousness, which seems to be more than the sum of individual neurons
• Ant colonies behaving as one coordinated entity However, the concept of emergence can also be applied to social phenomena.

These phenomena (or systems) may be structural parts of society-for example structural racism, or consumer culture-or they may be systematic processes-for example urban gentrification or the Me Too movement. Such societal systems are abstract and harder to delineate than a colony of ants, yet they exhibit many of the same properties and behaviours. Typically, these systems can be seen as the result of many people (or agents) interacting in particular ways, often with a set of rules or directions which they follow. In the example of urban gentrification, people typically choose to live in an area based on a fixed set of factors: e.g. affordability, location of friends and family, proximity to work and education, desirability of the area. When thousands of people do this, they influence each other, creating feedback loops and clustering behaviour which may not be predicted from any one individual’s rule set. These systems are also typically decentralised, meaning they are not controlled or directed by one person or group.

Considering structural racism, there are some highly influential actors, for example politicians, celebrities and business owners, but no single actor can fundamentally change the nature of structural racism. How, then, can we understand and possibly explain or predict these complex, sprawling systems? One modern method is agent-based modelling, or ABM, which uses computer simulations to model social phenomena. Rather than modelling the behaviour of groups of people at the macro level, ABM models many individual agents, each with a ‘rule set’. Through interactions, these agents can create feedback loops, and trigger thresholds which have not been explicitly encoded in the model. ABM models can also allow an agent’s rule set to change or evolve over time in response to their environment. This allows an interplay between agent decisions on the micro-level and system behaviours on the macro-level; an interplay central to the idea of emergence. Social phenomena are complex systems that are often difficult to understand and predict. However, the concepts of emergence can help navigate this complexity and find useful insights. Emergence highlights the need to approach such phenomena on both the macro and micro levels, as transformation likely cannot be instigated on one level alone.

Explore this topic further#

• Emergence{.link-ext target=”_blank”} (Wikipedia)
• Complexity: A Guided Tour by Melanie Mitchell Prologue [PDF]{.link-ext target=”_blank”}
• Emergence- How Stupid Things Become Smart Together

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The concept of emergence is not universally or strictly defined, but typically it centres on how a system can be entirely defined by a set of components or parts, while exhibiting properties beyond those components individually. Often the components of a complex system have certain predictable behaviours, following a set of rules or trends as they act and interact with ‘neighbouring’ components. As many neighbouring components repeatedly interact and influence each other, wider system properties can develop or change. Emergence has traditionally been used to study phenomena in physics and biology. Classical examples of emergence include:

• Living cells which comprise ‘simple’ chemical compounds
• Human consciousness, which seems to be more than the sum of individual neurons
• Ant colonies behaving as one coordinated entity However, the concept of emergence can also be applied to social phenomena.

These phenomena (or systems) may be structural parts of society-for example structural racism, or consumer culture-or they may be systematic processes-for example urban gentrification or the Me Too movement. Such societal systems are abstract and harder to delineate than a colony of ants, yet they exhibit many of the same properties and behaviours. Typically, these systems can be seen as the result of many people (or agents) interacting in particular ways, often with a set of rules or directions which they follow. In the example of urban gentrification, people typically choose to live in an area based on a fixed set of factors: e.g. affordability, location of friends and family, proximity to work and education, desirability of the area. When thousands of people do this, they influence each other, creating feedback loops and clustering behaviour which may not be predicted from any one individual’s rule set. These systems are also typically decentralised, meaning they are not controlled or directed by one person or group.

Considering structural racism, there are some highly influential actors, for example politicians, celebrities and business owners, but no single actor can fundamentally change the nature of structural racism. How, then, can we understand and possibly explain or predict these complex, sprawling systems? One modern method is agent-based modelling, or ABM, which uses computer simulations to model social phenomena. Rather than modelling the behaviour of groups of people at the macro level, ABM models many individual agents, each with a ‘rule set’. Through interactions, these agents can create feedback loops, and trigger thresholds which have not been explicitly encoded in the model. ABM models can also allow an agent’s rule set to change or evolve over time in response to their environment. This allows an interplay between agent decisions on the micro-level and system behaviours on the macro-level; an interplay central to the idea of emergence. Social phenomena are complex systems that are often difficult to understand and predict. However, the concepts of emergence can help navigate this complexity and find useful insights. Emergence highlights the need to approach such phenomena on both the macro and micro levels, as transformation likely cannot be instigated on one level alone.