  In decision making tasks under uncertainty, humans display characteristic
biases in seeking, integrating, and acting upon information relevant to the
task. Here, we reexamine data from previous carefully designed experiments,
collected at scale, that measured and catalogued these biases in aggregate
form. We design deep learning models that replicate these biases in aggregate,
while also capturing individual variation in behavior. A key finding of our
work is that paucity of data collected from each individual subject can be
overcome by sampling large numbers of subjects from the population, while still
capturing individual differences. In addition, we can predict human behavior
with high accuracy without making any assumptions about task goals, reward
structure, or individual biases, thus providing a model-agnostic fit to human
behavior in the task. Such an approach can sidestep potential limitations in
modeler-specified inductive biases, and has implications for computational
modeling of human cognitive function in general, and of human-AI interfaces in
particular.
