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Has Dynamic Programming Improved Decision Making?

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  • John Rust

Abstract

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.

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  • John Rust, 2019. "Has Dynamic Programming Improved Decision Making?," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 833-858, August.
    • Handle: RePEc:anr:reveco:v:11:y:2019:p:833-858
    DOI: 10.1146/annurev-economics-080218-025721
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    Cited by:

    1. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    2. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
    3. Dainis Zegners & Uwe Sunde & Anthony Strittmatter, 2020. "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," CESifo Working Paper Series 8341, CESifo.
    4. Junyi Wu & Shari Shang, 2020. "Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability," Sustainability, MDPI, vol. 12(21), pages 1-17, October.
    5. Yongyang Cai & Kenneth L. Judd, 2023. "A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 14(2), pages 651-687, May.
    6. Geng, Tong & Lin, Xiliang & Nair, Harikesh S. & Hao, Jun & Xiang, Bin & Fan, Shurui, 2020. "Comparison Lift: Bandit-Based Experimentation System for Online Advertising," Research Papers 3904, Stanford University, Graduate School of Business.
    7. Schuurman, Daniel & Weersink, Alfons & Delaporte, Aaron, 2021. "Optimal Sequential Crop Choices for Soil Carbon Management: A Dynamic Programming Approach," 2021 Annual Meeting, August 1-3, Austin, Texas 314042, Agricultural and Applied Economics Association.
    8. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312R4, Cowles Foundation for Research in Economics, Yale University, revised Jun 2023.
    9. Guo, Peijun, 2022. "Dynamic focus programming: A new approach to sequential decision problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(1), pages 328-336.
    10. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312R3, Cowles Foundation for Research in Economics, Yale University, revised Jan 2023.
    11. Luigi Biagini & Simone Severini, 2021. "The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool," Papers 2104.14188, arXiv.org.

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