Information overload for (bounded) rational agents

Emmanuel M. Pothos, Stephan Lewandowsky, Irina Basieva, Albert Barque-Duran, Katy Tapper, Andrei Khrennikov

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Bayesian inference offers an optimal means of processing environmental information and so an advantage in natural selection. We consider the apparent, recent trend in increasing dysfunctional disagreement in, for example, political debate. This is puzzling because Bayesian inference benefits from powerful convergence theorems, precluding dysfunctional disagreement. Information overload is a plausible factor limiting the applicability of full Bayesian inference, but what is the link with dysfunctional disagreement? Individuals striving to be Bayesian-rational, but challenged by information overload, might simplify by using Bayesian networks or the separation of questions into knowledge partitions, the latter formalized with quantum probability theory. We demonstrate the massive simplification afforded by either approach, but also show how they contribute to dysfunctional disagreement.
Original languageEnglish
Pages (from-to)20202957
Number of pages1
JournalProceedings of the Royal Society B: Biological Sciences
Volume288
Issue number1944
DOIs
Publication statusPublished - 3 Feb 2021

Structured keywords

  • Cognitive Science
  • Memory

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