# Human strategic decision making in parametrized games

arXiv: https://arxiv.org/abs/2104.14744 · HTML: https://mlsanity.com/paper/2104.14744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Learning Certifiably Optimal Rule Lists for Categorical Data](https://mlsanity.com/paper/1704.01701/) (2017)
- [Computing Human-Understandable Strategies](https://mlsanity.com/paper/1612.06340/) (2016)
- [Most Important Fundamental Rule of Poker Strategy](https://mlsanity.com/paper/1906.09895/) (2019)

## Built on by (most cited first, 1 of 1)

- [Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions](https://mlsanity.com/paper/2605.00226/) (2026)

## 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.