This paper models the learning process of a population of randomly-rematched tabula rasa neural network agents playing randomly generated 3 × 3 normal form games of all strategic types. Evidence was found of the endogenous emergence of a similarity measure of games based on the number and types...

In this article, we investigate the relative performance of artificial neural networks and structural models of decision theory by training 69 artificial intelligence models on a dataset of 7080 human decisions in extensive form games. The objective is to compare the predictive power of AIs that...

In strategic games with a unique mixed strategy equilibrium, players face both an incentive to best-respond to valuations and to act unpredictably. We developed a model of how neural circuitry represents a balance between these two incentives in the course of a decision. Choice is modelled as...

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70%...