Human strategic decision making in parametrized games
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Abstract (arXiv)
Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 30 Apr 2021.
Builds on
4 of 20 references.
References · 4
- 2017DeepStack: Expert-Level Artificial Intelligence in No-Limit Pokercited by 228
- 2017Learning Certifiably Optimal Rule Lists for Categorical Datacited by 50
- 2016Computing Human-Understandable Strategiescited by 1
- 2019Most Important Fundamental Rule of Poker Strategycited by 1
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Citing papers per year: 2026: 1.
1 paper.