Computational Incentive Science

Game Theory × Optimization × Data Science

Computational Incentive Science

I study the algorithmic foundations of incentives in markets, institutions, and decision-making, combining game theory, optimization, and data science.

ゲーム理論・最適化・データ科学を組み合わせ、市場・制度・意思決定に関わるインセンティブの算法を研究しています。

Research interests

Market Design: Theory and Applications

  • Matchings with constraints
  • Combinatorial auctions
  • Data-Driven Market Design

Repeated Games and Learning

  • Equilibrium computation
  • Private monitoring and POMDP
  • Evolutionary games

Recent work

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  1. 2026
    Asymmetric Perturbation in Solving Bilinear Saddle-Point Optimization
    Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu, Atsushi Iwasaki. ICML 2026, Oral.
  2. 2026
    Regulating Matching Markets with Distributional Constraints
    Kei Ikegami, Atsushi Iwasaki, Akira Matsushita, Kyohei Okumura. SIGecom Exchanges 23(2), 51–58.
  3. 2025
    Approximate State Abstraction for Markov Games
    Hiroki Ishibashi, Kenshi Abe, Atsushi Iwasaki. AAAI 2025, 17555–17563.
  4. 2025
    Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games
    Kenshi Abe, Mitsuki Sakamoto, Kaito Ariu, Atsushi Iwasaki. ICLR 2025.