Collective animal behavior from Bayesian estimation and probability matching.

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles appr...

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Autores principales:	Alfonso Pérez-Escudero, Gonzalo G de Polavieja
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1c4bfca5716947fea6e5a4e9e3ae1e3a
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spelling	oai:doaj.org-article:1c4bfca5716947fea6e5a4e9e3ae1e3a2021-11-18T05:51:46ZCollective animal behavior from Bayesian estimation and probability matching.1553-734X1553-735810.1371/journal.pcbi.1002282https://doaj.org/article/1c4bfca5716947fea6e5a4e9e3ae1e3a2011-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22125487/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.Alfonso Pérez-EscuderoGonzalo G de PolaviejaPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 7, Iss 11, p e1002282 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alfonso Pérez-Escudero Gonzalo G de Polavieja Collective animal behavior from Bayesian estimation and probability matching.
description	Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is mainly based on empirical fits to observations, with less emphasis in obtaining first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching. In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability equal to the Bayesian-estimated probability that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior.
format	article
author	Alfonso Pérez-Escudero Gonzalo G de Polavieja
author_facet	Alfonso Pérez-Escudero Gonzalo G de Polavieja
author_sort	Alfonso Pérez-Escudero
title	Collective animal behavior from Bayesian estimation and probability matching.
title_short	Collective animal behavior from Bayesian estimation and probability matching.
title_full	Collective animal behavior from Bayesian estimation and probability matching.
title_fullStr	Collective animal behavior from Bayesian estimation and probability matching.
title_full_unstemmed	Collective animal behavior from Bayesian estimation and probability matching.
title_sort	collective animal behavior from bayesian estimation and probability matching.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/1c4bfca5716947fea6e5a4e9e3ae1e3a
work_keys_str_mv	AT alfonsoperezescudero collectiveanimalbehaviorfrombayesianestimationandprobabilitymatching AT gonzalogdepolavieja collectiveanimalbehaviorfrombayesianestimationandprobabilitymatching
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Collective animal behavior from Bayesian estimation and probability matching.

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