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Exploring the bounded rationality in human decision anomalies through an assemblable computational framework.

Yi-Long Lu1, Yang-Fan Lu2, Xiangjuan Ren3

  • 1School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing China.

Cognitive Psychology
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

New models explain irrational decision-making, like the peanuts effect, by viewing cognitive biases as byproducts of optimized resource allocation. This computational framework offers a more comprehensive approach to understanding human behavior under risk.

Keywords:
Decision under riskEfficient codingPeanuts effectProbability distortionRate-distortion theoryResource rationality

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Area of Science:

  • Cognitive Science
  • Decision Neuroscience
  • Computational Psychology

Background:

  • Decision anomalies were previously seen as cognitive flaws.
  • Recent theories propose these anomalies arise from resource-rational decision-making, where the brain optimizes limited cognitive resources.
  • Existing models have limitations in scope and consistency, failing to fully explain complex phenomena like the peanuts effect.

Purpose of the Study:

  • To extend resource rationality to explain the peanuts effect, a decision anomaly showing value-probability interdependence.
  • To develop a flexible computational framework, Assemblable Resource-Rational Modules (ARRM), integrating diverse bounded rationality models.
  • To test the framework's ability to model joint environmental factors and improve fits to human decision behaviors.

Main Methods:

  • Developed the Assemblable Resource-Rational Modules (ARRM) computational framework.
  • Integrated existing boundedly-rational decision models as modular components.
  • Tested ARRM against one new and three published datasets across different task paradigms and domains (gain/loss).

Main Results:

  • ARRM models successfully reproduced key features of the peanuts effect.
  • The framework demonstrated superior performance in fitting human decision behaviors compared to previous models.
  • The approach accommodates joint environmental factors, enhancing explanatory power.

Conclusions:

  • The Assemblable Resource-Rational Modules (ARRM) framework provides a unified and flexible approach to bounded rationality.
  • This framework effectively explains the peanuts effect and advances our understanding of decision anomalies.
  • ARRM opens new avenues for modeling a wider range of decision phenomena within a resource-rational perspective.