Pothos EM(1), Lewandowsky S(2), Basieva I(1), Barque-Duran A(3), Tapper K(1), Khrennikov A(4). Author information:
(1)Department of Psychology, City, University of London, London EC1V 0HB, UK.
(2)School of Psychological Science, University of Bristol, Bristol BS8 1TU, UK.
(3)Department of Computer Science, Universitat de Lleida, Carrer de Jaume II,
67, 25001 Lleida, Spain.
(4)International Center for Mathematical Modeling in Physics and Cognitive
Science, Linnaeus University, Universitetplatsen 1, 351 95 Växjö, Sweden.
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.
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