On optimal decision-making in brains and social insect colonies

James A R Marshall, R Bogacz, A Dornhaus, R Planqué, T M D Kovacs, NR Franks

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

151 Citations (Scopus)

Abstract

The problem of how to compromise between speed and accuracy in decision-making faces organisms at many levels of biological complexity. Striking parallels are evident between decision-making in primate brains and collective decision-making in social insect colonies: in both systems, separate populations accumulate evidence for alternative choices; when one population reaches a threshold, a decision is made for the corresponding alternative, and this threshold may be varied to compromise between the speed and the accuracy of decision making. In primate decision-making, simple models of these processes have been shown, under certain parametrizations, to implement the statistically optimal procedure that minimizes decision time for any given error rate. In this paper, we adapt these same analysis techniques and apply them to new models of collective decision-making in social insect colonies. We show that social insect colonies may also be able to achieve statistically optimal collective decision-making in a very similar way to primate brains, via direct competition between evidence-accumulating populations. This optimality result makes testable predictions for how collective decision-making in social insects should be organized. Our approach also represents the first attempt to identify a common theoretical framework for the study of decision-making in diverse biological systems.
Translated title of the contributionOn optimal decision-making in brains and social insect colonies
Original languageEnglish
Pages (from-to)1065 - 1074
Number of pages10
JournalJournal of the Royal Society Interface
Volume6
Issue number40
DOIs
Publication statusPublished - Feb 2009

Bibliographical note

Other identifier: 2001005
Other: Also appearing as a chapter in "Modelling Natural Action Selection". Anil K. Seth, Tony J. Prescott, Joanna J. Bryson, (eds.). June 2011

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