Ambiguity-aware Optimization

Ambiguity-Aware Optimization is a risk optimization framework that explicitly accounts for uncertainty in probability distributions or model parameters by incorporating preferences over ambiguous outcomes, distinguishing it from conventional stochastic optimization methods through its sensitivity to epistemic uncertainty and robustness under distributional ambiguity.

32

Publications

2K

Citations

81

Authors

47

Institutions

Publications per year

2017–2026

17

Variants

Ambiguity Aware Optimization

Authors

81

  1. CM
    Claus Munk

    Copenhagen Business School, Denmark

    2 publications · 135 citations

    2

    h-index

  2. DW
    David Wozabal

    Technical University of Munich, Germany

    2 publications · 384 citations

    2

    h-index

  3. CR
    Chris Rizos

    UNSW Sydney, Australia

    2 publications · 90 citations

    2

    h-index

  4. LS
    Linda Sandris Larsen

    Copenhagen Business School, Denmark

    2 publications · 135 citations

    2

    h-index

  5. NB
    Nicole Branger

    University of Münster, Germany

    2 publications · 135 citations

    2

    h-index

1–5 of 81

Venues

  1. IEEE Transactions on Power Systems

    3 publications · 391 citations

    3

    h-index

  2. European Journal of Operational Research

    3 publications · 111 citations

    3

    h-index

  3. Quantitative Finance

    2 publications · 323 citations

    2

    h-index

  4. Journal of Geodesy

    2 publications · 90 citations

    2

    h-index

  5. 1

    h-index

1–5