  The bounded rationality is a crucial component in human behaviors. It plays a
key role in the typical collective behavior of evacuation, in which the
heterogeneous information leads to the deviation of rational choices. In this
study, we propose a deep learning framework to extract the quantitative
deviation which emerges in a cellular automaton (CA) model describing the
evacuation. The well-trained deep convolutional neural networks (CNNs)
accurately predict the rational factors from multi-frame images generated by
the CA model. In addition, it should be noted that the performance of this
machine is robust to the incomplete images corresponding to global information
loss. Moreover, this framework provides us with a playground in which the
rationality is measured in evacuation and the scheme could also be generalized
to other well-designed virtual experiments.
