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...

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%...

This paper models the learning process of populations of randomly rematched tabula rasa neural network (NN) agents playing randomly generated 2x2 normal form games of all strategic classes. This approach has greater external validity than the existing models in the literature, each of which is...