Stochastic Dynamic Optimization
Stochastic Dynamic Optimization is a mathematical framework for modeling sequential decision-making processes under uncertainty, where optimal policies are derived by maximizing expected cumulative rewards over time subject to stochastic state transitions.
532
Publications
28.9K
Citations
1.2K
Authors
539
Institutions
Publications per year
2017–2026
148
Parents
532
The "Ratchet Principle" and Performance Incentives
Martin L. Weitzman · The Bell Journal of Economics · 1980 · 468 citations
1–5 of 532
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539
Tsinghua University
Beijing, China
15 h-index
25 publications
1.1K citations
Massachusetts Institute of Technology
Cambridge, United States
12 h-index
20 publications
1K citations
University of California, Berkeley
Berkeley, United States
11 h-index
12 publications
697 citations
University of Michigan
Ann Arbor, United States
11 h-index
22 publications
2.1K citations
Boston University
Boston, United States
11 h-index
26 publications
882 citations
Stanford University
Stanford, United States
11 h-index
16 publications
1.1K citations
National University of Singapore
Singapore, Singapore
11 h-index
17 publications
1.1K citations
Georgia Institute of Technology
Atlanta, United States
10 h-index
20 publications
1.1K citations
1–8 of 539
26 publications · 2.4K citations
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26 publications · 1.2K citations
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16 publications · 1.2K citations
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Repository · 11 publications · 184 citations
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Repository · 10 publications · 234 citations
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