Inverse Generative Social Science: Backward to the Future

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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 growa target—the forward problem—we start with the macro-target and evolve microagents 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. In this overarching essay of the current JASSS Special Section, Part 1 discusses the motivation for iGSS. Part 2 discusses its goals, as distinct from other approaches. Part 3 discusses how to do it concretely, previewing the five iGSS applications that follow. Part 4 discusses several foundational issues for agent-based modeling and economics. Part 5 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 6. 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.

Original languageEnglish (US)
Article number9
JournalJASSS
Volume26
Issue number2
DOIs
StatePublished - Mar 31 2023

Keywords

  • Agent-Based Modeling
  • Artificial Intelligence
  • Evolutionary Computing
  • Generative Social Science
  • Inverse Generative Social Science
  • Rational Choice Theory

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Social Sciences

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