The adaptive value of probability distortion and risk-seeking in macaques' decision-making

A. Nioche, N. P. Rougier, M. Deffains, S. Bourgeois-Gironde, S. Ballesta, T. Boraud*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

In humans, the attitude toward risk is not neutral and is dissimilar between bets involving gains and bets involving losses. The existence and prevalence of these decision features in non-human primates are unclear. In addition, only a few studies have tried to simulate the evolution of agents based on their attitude toward risk. Therefore, we still ignore to what extent Prospect theory's claims are evolutionarily rooted. To shed light on this issue, we collected data from nine macaques that performed bets involving gains or losses. We confirmed that their overall behaviour is coherent with Prospect theory's claims. In parallel, we used a genetic algorithm to simulate the evolution of a population of agents across several generations. We showed that the algorithm selects progressively agents that exhibit risk-seeking, and has an inverted S-shape distorted perception of probability. We compared these two results and found that monkeys' attitude toward risk is only congruent with the simulation when they are facing losses. This result is consistent with the idea that gambling in the loss domain is analogous to deciding in a context of life-threatening challenges where a certain level of risk-seeking behaviour and probability distortion may be adaptive. This article is part of the theme issue 'Existence and prevalence of economic behaviours among non-human primates'.

Original languageEnglish
Article number20190668
JournalPHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B: BIOLOGICAL SCIENCES
Volume376
Issue number1819
DOIs
Publication statusPublished - 1 Mar 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • autonomous cognitive testing
  • cognitive biases
  • experimental economics
  • genetic algorithm
  • monkey

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