Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. We trained our prediction networks on the behavioral data from several published psychological experiments of human decision making, and demonstrated a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as Iowa Gambling Task and multi-agent scenarios such as Iterated Prisoner's Dilemma.
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