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Information overload for (bounded) rational agents.

Emmanuel M Pothos1, Stephan Lewandowsky2, Irina Basieva1

  • 1Department of Psychology, City, University of London, London EC1V 0HB, UK.

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Summary
This summary is machine-generated.

Bayesian inference, optimal for decision-making, can lead to dysfunctional disagreement when information overload prompts simplification. Simplified Bayesian approaches, like networks or quantum probability, paradoxically increase societal rifts.

Keywords:
Bayesian inferencedecision-makingdisagreemententrenchmentrationality

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Area of Science:

  • Cognitive Science
  • Decision Theory
  • Evolutionary Psychology

Background:

  • Bayesian inference provides an optimal framework for information processing and decision-making.
  • A recent increase in societal dysfunctional disagreement is observed, particularly in public discourse.
  • This trend is puzzling given Bayesian inference's inherent convergence properties that should prevent such disagreement.

Purpose of the Study:

  • To investigate the link between information overload, simplified Bayesian inference, and dysfunctional disagreement.
  • To explore how individuals attempting Bayesian rationality under information overload might deviate from optimal inference.
  • To analyze the role of specific simplification strategies in exacerbating societal rifts.

Main Methods:

  • Theoretical analysis of Bayesian inference under information overload.
  • Modeling of simplified inference strategies, including Bayesian networks and quantum probability formalisms.
  • Examination of how these simplifications can lead to divergent conclusions and disagreement.

Main Results:

  • Information overload can necessitate simplification of full Bayesian inference.
  • Both Bayesian networks and quantum probability-based knowledge partitioning offer computational simplification.
  • These simplification strategies, while reducing individual cognitive load, can systematically amplify societal disagreement.

Conclusions:

  • Simplified Bayesian inference, adopted to cope with information overload, can paradoxically foster dysfunctional disagreement.
  • Understanding these cognitive and probabilistic mechanisms is crucial for addressing societal polarization.
  • The study highlights potential pitfalls in applying rational inference models in complex, information-rich environments.