Sampling-based Decision Making

Sampling-Based Decision Making is a decision-making paradigm within the bounded rationality framework that, as a genus, relies on drawing limited samples from available options or outcomes rather than exhaustive evaluation, and, as its differentia, leverages probabilistic heuristics to approximate optimal choices under cognitive or computational constraints.

271

Publications

14.3K

Citations

539

Authors

312

Institutions

Publications per year

2017–2026

57

Authors

539

  1. YP
    Yijie Peng

    Peking University, China

    8 publications · 281 citations

    8

    h-index

  2. SE
    Stephen E. Chick

    INSEAD, France

    7 publications · 366 citations

    7

    h-index

  3. MC
    Michael C. Fu

    University of Maryland, College Park, United States

    6 publications · 241 citations

    6

    h-index

  4. RH
    Ralph Hertwig

    Max Planck Institute for Human Development, Germany

    5 publications · 299 citations

    5

    h-index

  5. PI
    Peter I. Frazier

    Cornell University, United States

    4 publications · 225 citations

    4

    h-index

1–5 of 539

Venues

  1. Technometrics

    13 publications · 528 citations

    11

    h-index

  2. Journal of the American Statistical Association

    12 publications · 441 citations

    11

    h-index

  3. The Annals of Mathematical Statistics

    5 publications · 121 citations

    5

    h-index

  4. Cognition

    5 publications · 427 citations

    5

    h-index

  5. 5

    h-index

1–5