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Rationalizable irrationalities of choice.

¹Gatsby Computational Neuroscience Unit, University College London.

Topics in Cognitive Science

|March 21, 2014

Summary

Understanding irrational choices is key to improving decision-making. This study identifies statistical, algorithmic, and implementational factors causing non-normative behavior under uncertainty, offering insights for better choices.

Keywords:

Bounded rationality Model-based Model-free Noisy decision-making Pavlovian Pruning Reinforcement learning

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Area of Science:

Cognitive Science
Behavioral Economics
Neuroscience

Background:

Human decision-making often deviates from rational models.
The underlying causes of these non-normative choices under uncertainty remain poorly understood.

Purpose of the Study:

To investigate the systematic sources of irrationality in decision-making.
To identify factors limiting normative behavior under uncertainty.

Main Methods:

Utilized three distinct experimental tasks to probe non-normative choices.
Analyzed decision-making processes under conditions of uncertainty.

Main Results:

Identified statistical, algorithmic, and implementational sources of irrationality.
Highlighted the roles of incomplete long-run utility evaluation, Pavlovian actions, habits, and noise in non-normative choices.

Conclusions:

Proposed that irrationality stems from specific, identifiable mechanisms.
Suggested structural and functional adaptations to mitigate the maladaptive effects of these irrational tendencies.