Pandora's Box Problem

The Pandora's Box Problem is a stochastic optimization framework in the field of optimal stopping where a decision-maker sequentially opens costly boxes containing random rewards, incurring inspection costs, and must choose whether to continue exploring or stop with the best value observed, thereby balancing exploration costs against the value of revealed information.

69

Publications

3.9K

Citations

179

Authors

116

Institutions

Publications per year

2017–2026

27

Variants

Pandora's Box

Authors

179

  1. YP
    Yi Pan

    Qingdao University of Science and Technology, China

    1 publications · 55 citations

    1

    h-index

  2. DA
    David A. Plane

    University of Arizona, United States

    1 publications · 31 citations

    1

    h-index

  3. SE
    Susan Evans Jennings

    Stephen F. Austin State University, United States

    1 publications · 20 citations

    1

    h-index

  4. HE
    Hossein Esfandiari

    University of Maryland, College Park, United States

    1 publications · 16 citations

    1

    h-index

  5. SC
    Shuchi Chawla

    University of Wisconsin–Madison, United States

    1 publications · 30 citations

    1

    h-index

1–5 of 179

Venues

  1. Biological Invasions

    3 publications · 89 citations

    3

    h-index

  2. SSRN Electronic Journal

    Repository · 2 publications · 36 citations

    2

    h-index

  3. Feminism & Psychology

    1 publications · 48 citations

    1

    h-index

  4. Electoral Studies

    1 publications · 59 citations

    1

    h-index

  5. Tourism Review

    1 publications · 43 citations

    1

    h-index

1–5