Adaptive Heuristic Selection

Adaptive Heuristic Selection is a decision-making framework that, as a subclass of bounded rationality models, dynamically chooses among available heuristics based on environmental cues and performance feedback to optimize behavior under cognitive and informational constraints.

22

Publications

1.1K

Citations

68

Authors

43

Institutions

Publications per year

2017–2026

7

Authors

68

  1. AS
    Amr Soghier

    University of Nottingham, United Kingdom

    2 publications · 61 citations

    2

    h-index

  2. RQ
    Rong Qu

    University of Nottingham, United Kingdom

    2 publications · 61 citations

    2

    h-index

  3. KF
    Kamil Fuławka

    Uniwersytet SWPS, Poland

    1 publications · 36 citations

    1

    h-index

  4. RH
    Raoul H. S. Philipse

    University of Oxford, United Kingdom

    1 publications · 54 citations

    1

    h-index

  5. MS
    Michèle Sébag

    Centre National de la Recherche Scientifique, France

    1 publications · 31 citations

    1

    h-index

1–5 of 68

Venues

  1. Judgment and Decision Making

    2 publications · 61 citations

    2

    h-index

  2. Journal of Heuristics

    1 publications · 79 citations

    1

    h-index

  3. 1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 22 citations

    1

    h-index

  5. European Journal of Operational Research

    1 publications · 79 citations

    1

    h-index

1–5