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Inverse Generative Social Science: Backward to the Future.

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Summary
This summary is machine-generated.

Inverse generative science (iGSS) evolves micro-agents to generate target patterns, reversing traditional agent-based modeling. This approach uses evolutionary computing to discover agent families as outputs, not designed inputs.

Keywords:
Agent-Based ModelingArtificial IntelligenceEvolutionary ComputingGenerative Social ScienceInverse Generative Social ScienceRational Choice Theory

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Area of Science:

  • Generative Social Science
  • Computational Social Science
  • Agent-Based Modeling

Background:

  • Traditional agent-based models (ABMs) design agents with predefined rules to simulate emergent macro-patterns.
  • This forward approach focuses on agent design as input to generate desired outcomes.

Purpose of the Study:

  • Introduce and motivate Inverse Generative Science (iGSS) as a novel approach to ABM.
  • Shift focus from designing agents to evolving them as outputs.
  • Explore the potential of evolutionary computing for iGSS.

Main Methods:

  • iGSS reverses the traditional ABM by starting with a macro-target pattern.
  • It evolves micro-agents that generate the target, stipulating only primitive rules and combinators.
  • Tools from Evolutionary Computing are employed to solve this backward problem.

Main Results:

  • Demonstrates a framework for evolving agent populations rather than designing individual agents.
  • Highlights the potential for discovering novel agent behaviors and structures.
  • Proposes iGSS as a method to evolve alternatives to the Rational Actor model.

Conclusions:

  • iGSS offers a powerful alternative paradigm for generative social science.
  • It facilitates the discovery of agent families as outputs, expanding the scope of ABM.
  • Future research should focus on foundational issues and applications, such as evolving economic agent alternatives.