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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
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Bounded learning and planning in public goods games.

Prakhar Godara1, Stephan Herminghaus1

  • 1Max Planck Institute for Dynamics and Self-Organization (MPIDS), Am Faßberg 17, D-37077 Göttingen, Germany.

Physical Review. E
|June 17, 2023
PubMed
Summary

This study enhances agent models with learning and memory bounds, revealing noise positively impacts cooperation in public goods games (PGG). These findings offer testable predictions for future PGG experiments.

Area of Science:

  • Agent-Based Modeling
  • Behavioral Economics
  • Game Theory

Background:

  • Previous agent models utilized bounded rational planning.
  • Agent memory limitations were not previously explored in relation to learning.
  • Understanding cooperation dynamics in public goods games (PGG) is crucial.

Purpose of the Study:

  • To extend existing agent models by incorporating learning with bounded memory.
  • To investigate the specific effects of learning on cooperation in PGG, particularly in extended game scenarios.
  • To provide experimentally testable predictions for PGG with synchronized actions.

Main Methods:

  • Developed an agent-based model incorporating learning and bounded memory.
  • Analyzed the impact of learning on cooperation over extended game durations.

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  • Theoretically explained experimental findings related to group size and mean per capita return (MPCR).

    • Main Results:

    • Learning, especially with bounded memory, significantly influences cooperation in PGG.
    • Noise in player contributions can unexpectedly enhance group cooperation.
    • The model successfully explains existing experimental data on group size and MPCR effects on cooperation.

    Conclusions:

    • Agent learning and memory bounds are critical factors in PGG cooperation.
    • Introducing controlled noise into PGG could be a strategy to foster cooperation.
    • The extended agent model provides a robust framework for understanding PGG dynamics.