The New Palgrave Dictionary of Economics

Living Edition
| Editors: Palgrave Macmillan

Dynamic Programming

  • John Rust
Living reference work entry


This article reviews the history and theory of dynamic programming (DP), a recursive method of solving sequential decision problems under uncertainty. It discusses computational algorithms for the numerical solution of DP problems, and an important limitation in our ability to solve realistic large-scale dynamic programming problems, the ‘curse of dimensionality’. It also summarizes recent research in complexity theory that delineates situations where the curse can be broken (allowing us to solve DPs using fast polynomial time algorithms), and situations where it is insuperable. The literature on econometric estimation and testing of DP models is reviewed, as is another ‘scientific limit to knowledge’, namely, the identification problem.


Backward induction Bellman equation Computational complexity Computational experiments Concavity Continuous and discrete time models Curse of dimensionality Decision variables Discount factor Dynamic discrete choice models Dynamic games Dynamic programming Econometric estimation Euler equations Game tree Identification Independence Indirect inference Infinite horizons Kalman filtering Kuhn–Tucker th Markov chain Monte Carlo methods Markovian decision problems Maximum likelihood Method of simulated moments Method of simulated scores Minimum residual method Monotonicity Monte Carlo integration Neural networks Nonlinear regression Nonparametric regression Optimal decision rules Policy iteration Principle of optimality Rational expectations Sequential decision problems Simulated maximum likelihood Simulated method of moments Simulated minimum distance Simulation-based estimation State variables Stationarity Statistical decision theory Structural estimation Subgame perfection Uncertainty Wald, A 

JEL Classification

C51 C61 
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Authors and Affiliations

  • John Rust
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  1. 1.