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The Common Currency for Behavioral Decisions

McNamara, John M., Houston, Alasdair I.·1986·The American Naturalist

Abstract

The study of optimal life histories involves the maximization of lifetime fitness but usually ignores the details of behavioral sequences. In contrast, the study of optimal behavioral sequences usually looks at the details of a sequence in isolation and not as part of the whole life history of the animal. The currency that is maximized is assumed to be related to lifetime fitness, but this relationship is rarely explored. In this paper we develop a common currency for behavioral decisions that is directly related to lifetime fitness. The common currency makes it possible to compare the benefits of qualitatively different behaviors. We show that many different costs can be used to explain a given behavioral sequence. Most of these costs have no biological interpretation. From these we single out a particular cost, the canonical cost, which measures the reduction in fitness that results from choosing a suboptimal action. Our general framework is illustrated by the example of a small bird in winter. We quantify the value of energy in terms of fitness and show how this value depends on energy reserves and time of day. As a result of this dependence, optimal foraging decisions depend on energy reserves and time of day, as does the optimal trade-off between foraging and looking around for predators.

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Metadata

Authors
McNamara, J. M., & Houston, A. I.
Venue
The American Naturalist
Published
1986-03
Volume
127
Issue
3
Pages
358-378
ISSN
  • 0003-0147 (ISSN-L)
  • 1537-5323
OpenAlex
W2009180034
Cited by
624
References
0
FWCI
53.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Common Currency for Behavioral Decisions. https://app.cochat.ai/papers/10.1086/284489
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McNamara, J. M., & Houston, A. I. (1986). The Common Currency for Behavioral Decisions. The American Naturalist. https://doi.org/10.1086/284489

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.