  Human behavior is incredibly complex and the factors that drive decision
making--from instinct, to strategy, to biases between individuals--often vary
over multiple timescales. In this paper, we design a predictive framework that
learns representations to encode an individual's 'behavioral style', i.e.
long-term behavioral trends, while simultaneously predicting future actions and
choices. The model explicitly separates representations into three latent
spaces: the recent past space, the short-term space, and the long-term space
where we hope to capture individual differences. To simultaneously extract both
global and local variables from complex human behavior, our method combines a
multi-scale temporal convolutional network with latent prediction tasks, where
we encourage embeddings across the entire sequence, as well as subsets of the
sequence, to be mapped to similar points in the latent space. We develop and
apply our method to a large-scale behavioral dataset from 1,000 humans playing
a 3-armed bandit task, and analyze what our model's resulting embeddings reveal
about the human decision making process. In addition to predicting future
choices, we show that our model can learn rich representations of human
behavior over multiple timescales and provide signatures of differences in
individuals.
