# Game Design for Eliciting Distinguishable Behavior

arXiv: https://arxiv.org/abs/1912.06074 · HTML: https://mlsanity.com/paper/1912.06074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

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

- [Identifying Informative Environments for Cognition Parameter Inference via Bayesian Experimental Design](https://mlsanity.com/paper/2607.28894/) (2026)

## Abstract (arXiv)

The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such traits range from surveys to manually-constructed experiments and games. However, these traditional games are limited because they are typically designed based on heuristics. In this paper, we formulate the task of designing behavior diagnostic games that elicit distinguishable behavior as a mutual information maximization problem, which can be solved by optimizing a variational lower bound. Our framework is instantiated by using prospect theory to model varying player traits, and Markov Decision Processes to parameterize the games. We validate our approach empirically, showing that our designed games can successfully distinguish among players with different traits, outperforming manually-designed ones by a large margin.