  Which neural mechanisms underlie strategic thinking in the human brain?
Neuroeconomic research has not yet bridged the gap between theoretical models
of higher-order reasoning and the precise mechanisms implemented in neural
networks in the human brain. In this paper, I demonstrate that a recurrent
neural network model can learn to perform strongly in the simple strategic game
Rock-Paper-Scissors. In doing so, it develops implicit representations of
strategically important variables (the levels $k$ of reasoning) which
economists have postulated in theoretical models. These representations can be
extracted from the hidden activations of the neural network. These findings
hint at a connection between the mechanisms implicit in recurrent neural
networks and models of strategic thinking in economic theory. Future empirical
brain research can investigate whether these mechanisms correspond to
mechanisms implicit in biological neural networks.
