The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
RISK AND FORAGING IN STOCHASTIC ENVIRONMENTS
Abstract
Foraging animals confront problems conceptually similar to those facing an economically minded consumer (46, 47), and foraging theory shares a methodology in common with economics. Indeed, the last 20 years have seen wide application of economic models in biology. A growing consensus suggests that ecological and economic theories are ultimately indistinguishable (6, 30). Economic analyses begin with an economic agent that chooses from alternative objects or activities. The analyst extracts a set of measurable attributes from this collection of objects (e.g. energy content, costs, etc) that are combined into a single index. This index has the property that objects whose attributes generate higher values are preferred by the consumer over objects generating lower values. In economics, this index (the 'utility function') is the basic organizing principle (36). Behavioral ecologists use this technique to learn what attributes should be included in an organism's utility function to determine how various attributes should be combined and weighted, and then to predict choices by maximizing the utility function under applicable constraints. The organism's utility function is, therefore, both an object of empirical study and a predictive tool. Economic analysis usually stops here, but evolutionary biologists go farther. They assume that the organisms' preferences are related to evolutionary fitness and that the options
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Citations
Cites
Knight, F. H. (1921) · Cited by 9,948
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Tsoukias, A., Keeney, R. L., & Raiffa, H. (1994) · The Journal of the Operational Research Society · doi:10.2307/2584151 · Cited by 8,370
The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme
Gould, S. J., & Lewontin, R. C. (1979) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1979.0086 · Cited by 7,770
Risk Aversion in the Small and in the Large
Pratt, J. W. (1976) · Econometrica · doi:10.2307/1912743 · Cited by 4,654
On Optimal Use of a Patchy Environment
MacArthur, R. H., & Pianka, E. R. (1966) · The American Naturalist · doi:10.1086/282454 · Cited by 4,593
Essays in the Theory of Risk-Bearing.
Hakansson, N. H., & Arrow, K. J. (1972) · The Journal of Finance · doi:10.2307/2978877 · Cited by 3,991
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Optimal Statistical Decisions.
Siegmund, D., & DeGroot, M. H. (1972) · Journal of the American Statistical Association · doi:10.2307/2284675 · Cited by 3,386
Caste and ecology in the social insects
Brian, M. V. (1979) · Animal Behaviour · doi:10.1016/0003-3472(79)90042-3 · Cited by 1,868
Optimization Theory in Evolution
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Cited by
Search Theory and Mate Choice. I. Models of Single-Sex Discrimination
Real, L. (1990) · The American Naturalist · doi:10.1086/285103 · Cited by 555
Self-citationRISK-SPREADING AND BET-HEDGING IN INSECT POPULATION BIOLOGY
Hopper, K. R. (1999) · Annual Review of Entomology · doi:10.1146/annurev.ento.44.1.535 · Cited by 470
Animal choice behavior and the evolution of cognitive architecture
Real, L. A. (1991) · Science · doi:10.1126/science.1887231 · Cited by 377
Self-citationNatural selection and the regulation of defenses
Nesse, R. M. (2005) · Evolution and Human Behavior · doi:10.1016/j.evolhumbehav.2004.08.002 · Cited by 365
Cerdá, X., Retana, J., & Cros, S. (1998) · Functional Ecology · doi:10.1046/j.1365-2435.1998.00160.x · Cited by 293
Metadata
- Authors
- Real, L., & Caraco, T.
- Venue
- Annual Review of Ecology and Systematics
- Publisher
- Annual Reviews
- Published
- 1986-11
- Volume
- 17
- Issue
- 1
- Pages
- 371-390
- ISSN
- 0066-4162 (ISSN-L)
- OpenAlex
- W2176640102
- Cited by
- 373
- References
- 46
- FWCI
- 34.70
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.