Risk-sensitive Dynamic Programming

Risk-Sensitive Dynamic Programming is a dynamic programming framework that incorporates risk measures into the optimization of sequential decision-making under uncertainty, distinguishing itself from risk-neutral approaches by explicitly accounting for the variability and higher moments of cumulative rewards.

32

Publications

1.5K

Citations

70

Authors

49

Institutions

Publications per year

2017–2026

11

Authors

70

  1. CP
    Christian P. Traeger

    University of California, Berkeley, United States

    2 publications · 42 citations

    2

    h-index

  2. BC
    Benjamin Crost

    University of Colorado Denver, United States

    2 publications · 42 citations

    2

    h-index

  3. JL
    Jean‐Paul Laurent

    University of Mons, Belgium

    2 publications · 177 citations

    2

    h-index

  4. RC
    Rolando Cavazos–Cadena

    Universidad Autónoma Agraria Antonio Narro, Mexico

    2 publications · 28 citations

    2

    h-index

  5. SI
    Steven I. Marcus

    University of Maryland, College Park, United States

    2 publications · 37 citations

    2

    h-index

1–5 of 70

Venues

  1. Mathematical Finance

    3 publications · 199 citations

    3

    h-index

  2. Annals of Operations Research

    2 publications · 56 citations

    2

    h-index

  3. Mathematical Programming

    2 publications · 458 citations

    2

    h-index

  4. Operations Research Letters

    2 publications · 43 citations

    2

    h-index

  5. Journal of the Operational Research Society

    OR · 2 publications · 29 citations

    2

    h-index

1–5