Learning rules for optimal selection in a varying environment: mate choice revisited
AbstractThe quality of a chosen partner can be one of the most significant factors affecting an animal's long-term reproductive success. We investigate optimal mate choice rules in an environment where there is both local variation in the quality of potential mates within each local mating pool and spatial (or temporal) variation in the average quality of the pools themselves. In such a situation, a robust rule that works well across a variety of environments will confer a significant reproductive advantage. We formulate a full Bayesian model for updating information in such a varying environment and derive the form of the rule that maximizes expected reward in a spatially varying environment. We compare the theoretical performance of our optimal learning rule against both fixed threshold rules and simpler near-optimal learning rules and show that learning is most advantageous when both the local and environmental variances are large. We consider how optimal simple learning rules might evolve and compare their evolution with that of fixed threshold rules using genetic algorithms as minimal models of the relevant genetics. Our analysis points up the variety of ways in which a near-optimal rule can be expressed. Finally, we describe how our results extend to the case of temporally varying environments. Copyright 2006.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by International Society for Behavioral Ecology in its journal Behavioral Ecology.
Volume (Year): 17 (2006)
Issue (Month): 5 (September)
Contact details of provider:
Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
Fax: 01865 267 985
Web page: http://beheco.oxfordjournals.org/
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Ismail Saglam, 2014.
"Simple Heuristics as Equilibrium Strategies in Mutual Sequential Mate Search,"
Journal of Artificial Societies and Social Simulation,
Journal of Artificial Societies and Social Simulation, vol. 17(1), pages 12.
- Saglam, Ismail, 2013. "Simple heuristics as equilibrium strategies in mutual sequential mate search," MPRA Paper 44222, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.