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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Recasting a biologically motivated computational model within a Fechnerian and random utility framework.

Clintin P Davis-Stober1, Nicholas Brown2, Sanghyuk Park3

  • 1Department of Psychological Sciences, 219 McAlester Hall, University of Missouri at Columbia, Columbia, MO 65211, USA.

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Summary

The selective integration model explains how noise can cause rational agents to make inconsistent choices. This study connects this model to Fechnerian and random utility frameworks, clarifying its implications for transitive preference.

Keywords:
Fechnerian modelSelective integrationrandom utilitytriangle inequalitiesweak stochastic transitivity

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Theory

Background:

  • The selective integration model (Tsetsos et al., 2016a) offers a biologically motivated computational framework for understanding intransitive preference and choice.
  • Previous work suggested that noise in a decision-making system can lead to violations of transitivity, even in rational agents.
  • Understanding the theoretical underpinnings of such models is crucial for advancing decision theory.

Purpose of the Study:

  • To interpret the selective integration model from a Fechnerian perspective.
  • To connect the selective integration model to a random utility framework.
  • To elucidate the relationship between the selective integration model and probabilistic models of transitive preference.

Main Methods:

  • Interpreting the selective integration model within established psychophysical frameworks.
  • Formulating connections between the selective integration model and random utility theory.
  • Analyzing the relationship between the selective integration model and probabilistic tests of transitivity (weak stochastic transitivity, triangle inequalities).

Main Results:

  • The selective integration model can be understood through a Fechnerian lens, linking subjective experience to objective stimuli.
  • The model aligns with random utility frameworks, providing a probabilistic interpretation of choice behavior.
  • Explicit connections were established between the selective integration model and established probabilistic criteria for transitive preference.

Conclusions:

  • The selective integration model provides a valuable framework for understanding intransitive preferences arising from noise in rational decision-making.
  • Interpreting the model within Fechnerian and random utility frameworks enhances its theoretical integration within decision science.
  • This work clarifies how the selective integration model relates to established measures of preference transitivity, supporting its empirical validation.