Game Design for Eliciting Distinguishable Behavior

TL;DR

This paper introduces a novel framework for designing behavior diagnostic games that maximize mutual information to distinguish psychological traits, using prospect theory and MDPs, validated through empirical experiments.

Contribution

It formulates game design as a mutual information maximization problem and demonstrates its effectiveness with a new theoretical and empirical approach.

Findings

Designed games outperform manual designs in trait differentiation

Framework successfully distinguishes players with different traits

Empirical validation confirms the approach's effectiveness

Abstract

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 \emph{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.