Model-Agnostic Fits for Understanding Information Seeking Patterns in Humans
Keywords:Simulating Humans, Transfer/Adaptation/Multi-task/Meta/Automated Learning, Learning Human Values and Preferences
AbstractIn 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. We 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.
How to Cite
Chatterjee, S., & Shenoy, P. (2021). Model-Agnostic Fits for Understanding Information Seeking Patterns in Humans. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 784-791. https://doi.org/10.1609/aaai.v35i1.16160
AAAI Technical Track on Cognitive Modeling and Cognitive Systems