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John Rust (Georgetown) – "Has dynamic programming improved decision making?"
CREST Microeconomics Seminar :
Date: 14th June 2019
Place: Room 3001.
John Rust (Georgetown) – “Has dynamic programming improved decision making?”
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 real world applications where DP is actually used to improve decision making. I discuss the literature on numerical solution of DPs and its connection to the literature on reinforcement learning (RL) and artificial intelligence (AI). Despite the highly publicized successes where DP and RL have achieved superhuman levels of performance in board games such as chess or Go, it is harder to find comparable successes where DP has helped individuals and firms solve less well-defined real-world problems. I point to the fuzziness of many real world decision problems and the difficulty in mathematically formulating and modeling them as key obstacles to wider application of DP in real world settings. Nevertheless, I discuss several success stories where DP has demonstrably improved decision making. 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.
Roxana Fernandez Machado (CREST), Marie Laure Allain (CREST), and Linda Schilling (CREST)
food provided, no registration needed