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Dealing with Complexity through ‘Swarm AI’

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Social phenomena involves webs of large numbers of elements that interact with each other in complicated, non-linear ways. These interactions create coherent structures and “emergent” properties.

Seen in this way, social phenomena bears a relationship to the flow of a turbulent fluid or the behavior of a traffic jam which requires a fundamental rethinking of scientific modeling. To give a specific example, a country’s economy is a complex system par excellence: millions of individual humans interacting with each other in a movable maze of possibilities and constraints ranging from the physical availability of resources to education levels and fiscal policies, all while the country itself is furiously exchanging goods, services and individuals (aka agents) with the rest of the world.

Agent-based modeling is a form of computer simulation that programs groups of “agents” with certain behaviors, goals, and ideas and then measures or observes the effects of those particularities at the macro level. Economist Thomas Schelling developed in the 1970s a famous example of an Agent-Based Model (or ABM) (a version of the model can be downloaded here). In very simplified terms, each agent was given a limited goal that the modeler could adjust rather than being asked to form segregated or integrated groups, and then the patterns of separation emerged seemingly all by themselves.

This tendency towards self-organization or the spontaneous emergence of patterns is the fundamental idea that drives research with ABMs, and it applies whether the models are being used to study economic relationships, rebellion and policing, or cellular formation and the formation of insect hives. This is why ABM is also sometimes called Swarm AI. The “intelligence” or adaptiveness of the simulation is not programmed in from the top-down, but rather emerges from the aggregated effects of interacting parts.

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What are the uses of agent-based models? ABMs can:

  • Test Hypotheses, comparing the outputs of the model against real-world quantitative data and ultimately supporting or challenging different theories about the underlying causes of different social phenomena;
  • Support Theory Building, transforming an informal or qualitative idea into clear procedures and behaviors that can be programmed into a model, forcing researchers to clarify their assumptions into testable ideas.

Scholars and researchers in the social sciences and the humanities often balk at the second approach, arguing that the texture of human life is far too complex and qualitative to ever be usefully reduced to a set of simulated procedures. But these objections are often missing the point of modeling altogether. Joshua Epstein, a key foundational figure in ABM research, has a compelling response to these challenges.

The first question that arises frequently-sometimes innocently and sometimes not-is simply, ‘Why model?” Imagining a rhetorical inquisitor, my favorite retort is, “You are a modeler.” Anyone who ventures a projection, or imagines how a social dynamic-an epidemic, war, or migration-would unfold is running some model.

But typically, it is an implicit model in which the assumptions are hidden, their internal consistency is untested, their logical consequences are unknown, and their relation to data is unknown. But when you close your eyes and imagine an epidemic spreading, or any other social dynamic, you are running some model or other, it is just an implicit model that you haven’t written down.

The question to be asked is then not whether to build models; yet whether to build explicit ones in which assumptions are laid out in detail in order to study exactly what they entail.

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ABMs and ‘Swarm AI’ models are not a silver bullet, and they are never intended to fully replicate the tapestry of human experience in all its gloriously messy complexity. Yet, they are a powerful means for testing our assumptions and investigating how the orders and patterns of social life emerge from simple rules and simple goals. They are an invaluable tool for anyone pursuing an empirical and scientific approach to culture and society. Such an intersection is very complex, and even more difficult to predict. If we accept that there is no new normal and our lives will be changing with probably an accelerating pace for the rest of the lives of everyone who is alive today, we may find ABM’s more helpful.

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Ayse Kok
Ayse completed her masters and doctorate degrees at both University of Oxford (UK) and University of Cambridge (UK). She participated in various projects in partnership with international organizations such as UN, NATO, and the EU. She also served as an adjunct faculty member at Bosphorus University in her home town Turkey. Furthermore, she is the editor of several international journals, including those for Springer, Wiley and Elsevier Science. She attended various international conferences as a speaker and published over 100 articles in both peer-reviewed journals and academic books. Having published 3 books in the field of technology & policy, Ayse is a member of the IEEE Communications Society, member of the IEEE Technical Committee on Security & Privacy, member of the IEEE IoT Community and member of the IEEE Cybersecurity Community. She also acts as a policy analyst for Global Foundation for Cyber Studies and Research. Currently, she lives with her family in Silicon Valley where she worked as a researcher for companies like Facebook and Google.

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