Has Dynamic Programming Improved Decision Making?

Posted: 4 Sep 2019

See all articles by John Rust

John Rust

Department of Economics, Georgetown University

Date Written: August 2019


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.

Suggested Citation

Rust, John, Has Dynamic Programming Improved Decision Making? (August 2019). Annual Review of Economics, Vol. 11, pp. 833-858, 2019. Available at SSRN: https://ssrn.com/abstract=3445888 or http://dx.doi.org/10.1146/annurev-economics-080218-025721

John Rust (Contact Author)

Department of Economics, Georgetown University ( email )

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