Predicting Human Interactive Learning by Regret-Driven Neural Networks
Davide Marchiori1 and
Massimo Warglien2*
Much of human learning in a social context has an interactive
nature: What an individual learns is affected by what other
individuals are learning at the same time. Games represent a
widely accepted paradigm for representing interactive decision-making.
We explored the potential value of neural networks for modeling
and predicting human interactive learning in repeated games.
We found that even very simple learning networks, driven by
regret-based feedback, accurately predict observed human behavior
in different experiments on 21 games with unique equilibria
in mixed strategies. Introducing regret in the feedback dramatically
improved the performance of the neural network. We show that
regret-based models provide better predictions of learning than
established economic models.
2 Advanced School of Economics and Department of Business Economics and Management, Ca' Foscari University, Venezia, Italy.
* To whom correspondence should be addressed. E-mail: warglien{at}unive.it
