People use fast, goal-directed simulation to reason about novel games

TL;DR

This paper investigates how people quickly evaluate novel games using goal-directed simulation, showing they rely on limited, partial game reasoning rather than extensive search or prior experience.

Contribution

It introduces a resource-limited model that explains how humans assess new games through minimal, goal-directed simulations without extensive look-ahead search.

Findings

People can accurately judge fairness and fun with minimal game thinking

A small number of partial simulations suffices for human-like judgments

The proposed model aligns with human reasoning in novel game evaluation

Abstract

People can evaluate features of problems and their potential solutions well before we can effectively solve them. When considering a game we have never played, for instance, we might infer whether it is likely to be challenging, fair, or fun simply from hearing the game rules, prior to deciding whether to invest time in learning the game or trying to play it well. Many studies of game play have focused on optimality and expertise, characterizing how people and computational models play based on moderate to extensive search and after playing a game dozens (if not thousands or millions) of times. Here, we study how people reason about a range of simple but novel Connect-N style board games. We ask people to judge how fair and how fun the games are from very little experience: just thinking about the game for a minute or so, before they have ever actually played with anyone else, and we propose a resource-limited model that captures their judgments using only a small number of partial game simulations and almost no look-ahead search.