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==Biological Applications==
Stochastic dynamic programming is frequently used to model [[animal behaviour]] in such fields as [[behavioural ecology]]<ref>Mangel, M. & Clark, C. W. 1988. ''Dynamic modeling in behavioral ecology.'' Princeton University Press ISBN 0-691-08506-4</ref><ref>Houston, A. I & McNamara, J. M. 1999. ''Models of adaptive behaviour: an approach based on state''. Cambridge University Press ISBN 0-521-65539-0</ref>. Empirical tests of models of [[Optimal foraging theory|optimal foraging]], [[Biological life cycle|life-history]] transitions such as [[Fledge|fledging in birds]] and egg laying in [[parasitoid]] wasps have shown the value of this modelling technique in explaining the evolution of behavioural decision making. These models are typically many staged, rather than two-staged.
==Economic Applications==
Stochastic dynamic programming is a useful tool in understanding decision making under uncertainty. The accumulation of capital stock under uncertainty is one example, often it is used by resource economists to analyze [[bioeconomic problems]]<ref> Howitt, R., Msangi, S., Reynaud, A and K. Knapp. 2002. "Using Polynomial Approximations to Solve Stochastic Dynamic Programming Problems: or A "Betty Crocker " Approach to SDP." University of California, Davis, Department of Agricultural and Resource Economics Working Paper. http://www.agecon.ucdavis.edu/aredepart/facultydocs/Howitt/Polyapprox3a.pdf </ref>where the uncertainty enters in as weather, etc.
==References==
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