Machine Learning as a Tool to Detect and Validate Anomalous 2x2 Games

A central question in behavioral game theory is whether standard theoretical models can accurately predict human choices in simple strategic environments. Existing work suggests that some families of 2x2 games generate especially large discrepancies between theoretical predictions and observed behavior. These disparities motivate an approach in which machine learning is used not only to fit observed choices, but also to automatically generate 2x2 games specifically designed to expose the vulnerabilities of these theoretical frameworks.
The present study compares standard games and historical anomalies drawn from an existing database (\cite{Complexity2025}) with novel "anomaly" games generated by a machine learning procedure. The contribution is twofold. First, the project evaluates whether machine learning provides a more accurate predictive benchmark than standard models (Nash equilibrium, QRE, and level-k). Second, it tests whether ML-generated games reveal systematic blind spots of these standard models.
The experiment will involve participants recruited via Prolific and a pool of 120 games. These games are divided into three categories: 20 standard games, 20 historical "anomaly" games from the existing database, and 80 "anomaly" games generated by our machine-learning procedure. The games are randomly assigned to four fixed blocks of 30 games (5 standard, 5 database anomalies, and 20 ML anomalies). Each participant will play one block of 30 games. Within each block, game order will be randomized. Participants will make a strategic choice for each game and report perceived difficulty. They will also participate in a lottery game, a donation task, and a short IQ test. The main analysis will test whether database anomalies and ML-generated games produce larger discrepancies between theoretical predictions and observed behavior than standard baseline games.