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Has Dynamic Programming Improved Decision Making?
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DOI：10.1146/annurev-economics-080218-025721.png)
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Dynamic programming (DP) is a powerful tool for solving a wide class of sequential decision-making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision in any situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence with the comparative paucity of its real-world applications to improve the decision making of individuals and firms. The fuzziness of many real-world decision problems and the difficulty in mathematically modeling them are key obstacles to a wider application of DP in real-world settings. Nevertheless, I discuss several success stories, and I conclude that DP offers substantial promise for improving decision making if we let go of the empirically untenable assumption of unbounded rationality and confront the challenging decision problems faced every day by individuals and firms.
Keyword:
actor-critic algorithms
dynamic programming
artificial intelligence
behavioral economics
bounded rationality
curse of dimensionality
computational complexity
neural networks
revenue management
reinforcement learning
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