Applications and Examples[edit]
Biological applications[edit]
Stochastic dynamic programming is frequently used to model animal behaviour in such fields as behavioural ecology.[8][9] Empirical tests of models of optimal foraging, life-history transitions such as 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[edit]
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[10] where the uncertainty enters in such as weather, etc.
Software tools[edit]
Modelling languages[edit]
All discrete stochastic programming problems can be represented with any algebraic modeling language, manually implementing explicit or implicit non-anticipativity to make sure the resulting model respects the structure of the information made available at each stage. An instance of an SP problem generated by a general modelling language tends to grow quite large (linearly in the number of scenarios), and its matrix loses the structure that is intrinsic to this class of problems, which could otherwise be exploited at solution time by specific decomposition algorithms. Extensions to modelling languages specifically designed for SP are starting to appear, see:
They both can generate SMPS instance level format, which conveys in a non-redundant form the structure of the problem to the solver.