The Glass is Half-Full: Overestimating the Quality of a Novel Environment is Advantageous

PLoS One

Tal Avgar¹  Oded Berger-Tal² 

[1] Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada;Mitrani Department of Desert Ecology, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beer-Sheva, Israel

关键词: Foraging;    Memory;    Learning;    Food consumption;    Habitats;    Simulation and modeling;    Animal behavior;    Machine learning;   

DOI  :  10.1371/journal.pone.0034578

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

According to optimal foraging theory, foraging decisions are based on the forager's current estimate of the quality of its environment. However, in a novel environment, a forager does not possess information regarding the quality of the environment, and may make a decision based on a biased estimate. We show, using a simple simulation model, that when facing uncertainty in heterogeneous environments it is better to overestimate the quality of the environment (to be an “optimist”) than underestimate it, as optimistic animals learn the true value of the environment faster due to higher exploration rate. Moreover, we show that when the animal has the capacity to remember the location and quality of resource patches, having a positively biased estimate of the environment leads to higher fitness gains than having an unbiased estimate, due to the benefits of exploration. Our study demonstrates how a simple model of foraging with incomplete information, derived directly from optimal foraging theory, can produce well documented complex space-use patterns of exploring animals.

【 授权许可】

CC BY