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Strategy selection: An introduction to the modeling challenge.

Julian N Marewski1, Daniela Link1

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Summary
This summary is machine-generated.

Understanding strategy selection is key across many fields. This study explores how humans and agents choose cognitive processes, outlining challenges and diverse approaches to this complex problem.

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Area of Science:

  • Cognitive science
  • Decision science
  • Machine learning
  • Economics
  • Biology

Background:

  • Modeling how agents select among behavioral and cognitive processes is a significant challenge across multiple scientific disciplines.
  • The strategy selection problem is fundamental to understanding decision-making in humans and other agents.

Purpose of the Study:

  • To introduce the strategy selection problem using cognitive and decision sciences as a case study.
  • To explain the assumption of a repertoire of processes in humans and animals.
  • To outline challenges and review existing approaches to strategy selection.

Main Methods:

  • Literature review and theoretical analysis.
  • Case study approach focusing on cognitive and decision sciences.
  • Categorization and overview of various strategy selection models.

Main Results:

  • Identified three core challenges (descriptive, predictive, prescriptive) in modeling strategy selection.
  • Presented a comprehensive overview of diverse approaches, including cost-benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization models.
  • Highlighted the interdisciplinary nature of the strategy selection problem.

Conclusions:

  • The strategy selection problem is complex and remains an active area of research.
  • Further research is needed to fully resolve the mechanisms underlying behavioral and cognitive process selection.
  • Understanding strategy selection is crucial for advancements in cognitive science, AI, and economics.