Using Cox's proportional hazard models to implement optimal strategies: An example from behavioural ecology
✍ Scribed by B. Tenhumberg; M.A. Keller; H.P. Possingham
- Book ID
- 104350893
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 1018 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0895-7177
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✦ Synopsis
Simple behaviour8i rulea, or "rulea of thumb", which lead to behaviour that closely approxim8tea 8n optid etrategy, have generated 8 lot of recent int%reBt in the field of &aging behaviour. In this paper, we derive rulea of thumb from 8 stochastic elmul8tlon model in which the foragars behave optimally. We use a particuler biological system: the patch leaving behaviour of 8 parasitoid. We simulata p8resitolda whose patch leaving behaviour is determined by 8 stoch8stic dynamic progr amming (SDP) model, while allowing parssitoids to make mistakea in their eetimation of ho& density when arcivlng iu 8 patch We use Cox's proportional bazarda models to obtain statistial rulea of thumb from the simulated behaviour. This represents the 6rst use of 8 proportional hazard Bppr oxim8tion to generate rules of thumb from a complex optimal stmtegy. @ 2001 Ekvler Science Ltd. All rights rsssrved. Keywords-Rules of thumb, SDP model, C&s proportional hazards model, Optimal strategks.
1. INTRdDUCTION
Stochastic dynamic programming (SDP) models are widely used to find state-dependent optimal solutions in biology [I], especially in the study of foraging behaviour (optimal foraging theory or OFT). The SDP approach is a very efficient way to find state-dependent optimal strategies in a stochastic system. However, such strategies are often very complex, especially when the state space is large, and it is very unlikely that a simple organism could implement such complex behaviour. Animals can potentially perform close to the optimum by using "rules of thumb" [2]. However, the connection between such rules and optimal solutions is vague. In this paper, we suggeet that Cox's proportional hazards model [3,4] can condense the results of an SDP model into