Reward Optimization

Reward Optimization is a methodological framework within reward governance that systematically aligns incentive structures with desired behavioral outcomes by maximizing the efficacy, fairness, and strategic coherence of rewards in multi-agent or organizational systems.

42

Publications

1.5K

Citations

139

Authors

63

Institutions

Publications per year

2017–2026

21

Authors

139

  1. SE
    Shengbo Eben Li

    Tsinghua University, China

    2 publications · 71 citations

    2

    h-index

  2. JH
    Jack Hessel

    University of Washington, United States

    2 publications · 33 citations

    2

    h-index

  3. XW
    Xinping Wang

    Xi'an University of Science and Technology, China

    2 publications · 46 citations

    2

    h-index

  4. RL
    Ronan Le Bras

    University of Washington, United States

    2 publications · 33 citations

    2

    h-index

  5. HY
    Heeseung Yun

    Seoul National University, South Korea

    2 publications · 33 citations

    2

    h-index

1–5 of 139

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 225 citations

    4

    h-index

  2. 2

    h-index

  3. Journal of Building Engineering

    1 publications · 11 citations

    1

    h-index

  4. Performance Evaluation

    1 publications · 39 citations

    1

    h-index

  5. PLoS ONE

    1 publications · 40 citations

    1

    h-index

1–5