Inverse Generative Social Science: Backward to the Future

Joshua M. Epstein ()
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Joshua M. Epstein: https://publichealth.nyu.edu/faculty/joshua-epstein

Journal of Artificial Societies and Social Simulation, 2023, vol. 26, issue 2, 9

Abstract: The agent-based model is the principal scientific instrument of generative social science. Typically, we design completed agents—fully endowed with rules and parameters—to grow macroscopic target patterns from the bottom up. Inverse generative science (iGSS) stands this approach on its head: Rather than handcrafting completed agents to grow a target—the forward problem—we start with the macro-target and evolve micro-agents that generate it, stipulating only primitive agent-rule constituents and permissible combinators. Rather than specific agents as designed inputs, we are interested in agents—indeed, families of agents—as evolved outputs . This is the backward problem and tools from Evolutionary Computing can help us solve it. As the overarching essay in the current JASSS Special Section, Part 1 discusses the goals of iGSS as distinct from other approaches. Part 2 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 3 discusses several foundational issues for agent-based modeling and economics. Part 4 proposes a central future application of iGSS: to evolve explicit formal alternatives to the Rational Actor, with Agent_Zero as one possible point of evolutionary departure. Conclusions and future research directions are offered in Part 5. Looking ‘backward to the future,’ I also include, as Appendices, a pair of 1992 memoranda to the then President of the Santa Fe Institute on the forward (growing artificial societies from the bottom up) and backward (iGSS) problems.

Keywords: Agent-Based Modeling; Generative Social Science; Inverse Generative Social Science; Artificial Intelligence; Evolutionary Computing; Rational Choice Theory (search for similar items in EconPapers)
Date: 2023-03-31
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