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This study introduces an agent-based model for the public goods game (PGG) using bounded rationality. The model successfully replicates human behavior and cooperation without needing learning mechanisms.

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Area of Science:

  • Computational Social Science
  • Behavioral Economics
  • Agent-Based Modeling

Background:

  • The public goods game (PGG) is a fundamental model for studying cooperation and social dilemmas.
  • Existing models often rely on learning mechanisms to explain human behavior in PGG.
  • Bounded rationality offers an alternative framework for understanding decision-making in strategic interactions.

Purpose of the Study:

  • To develop an agent-based model (ABM) for the public goods game (PGG) incorporating bounded rationality.
  • To investigate whether learning mechanisms are essential for modeling human behavior in PGG.
  • To relate model parameters to measurable human preferences.

Main Methods:

  • Developed an agent-based model (ABM) for the PGG based on bounded rationality.
  • Utilized a path integral formulation of reward maximization within a Markov decision process.
  • Fitted simulated game trajectories to experimental data from 16 cities worldwide.

Main Results:

  • The developed ABM accurately models human behavior in the PGG, including emergent cooperation.
  • Only two fitting parameters were found to be relevant for explaining behavioral variations across diverse populations.
  • The model demonstrates that learning is not a necessary component to replicate empirical PGG data.

Conclusions:

  • Agent-based models with bounded rationality can effectively capture human behavior in the PGG.
  • Simplified models with minimal parameters can explain complex social dynamics.
  • Rethinking the necessity of learning mechanisms in game theory models is warranted.