Adaptive search and information updating in sequential mate choice

Détails

ID Serval
serval:BIB_524585E524BB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Adaptive search and information updating in sequential mate choice
Périodique
American Naturalist
Auteur(s)
Mazalov V., Perrin N., Dombrovsky Y.
ISSN
0003-0147
Statut éditorial
Publié
Date de publication
07/1996
Peer-reviewed
Oui
Volume
148
Numéro
1
Pages
123-137
Langue
anglais
Notes
http://www.jstor.org/stable/2463074
Résumé
Classical treatments of problems of sequential mate choice assume that the distribution of the quality of potential mates is known a priori. This assumption, made for analytical purposes, may seem unrealistic, opposing empirical data as well as evolutionary arguments. Using stochastic dynamic programming, we develop a model that includes the possibility for searching individuals to learn about the distribution and in particular to update mean and variance during the search. In a constant environment, a priori knowledge of the parameter values brings strong benefits in both time needed to make a decision and average value of mate obtained. Knowing the variance yields more benefits than knowing the mean, and benefits increase with variance. However, the costs of learning become progressively lower as more time is available for choice. When parameter values differ between demes and/or searching periods, a strategy relying on fixed a priori information might lead to erroneous decisions, which confers advantages on the learning strategy. However, time for choice plays an important role as well: if a decision must be made rapidly, a fixed strategy may do better even when the fixed image does not coincide with the local parameter values. These results help in delineating the ecological-behavior context in which learning strategies may spread.
Web of science
Création de la notice
24/01/2008 18:53
Dernière modification de la notice
03/03/2018 17:13
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