Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task
This work addresses the challenge of modeling human behavior in exploration-exploitation tasks for researchers in cognitive science and human-computer interaction, though it is incremental as it builds on existing Bayesian optimization methods.
The paper tackled the problem of inferring human decision-making strategies in optimization tasks by introducing a probabilistic framework to estimate parameters of an acquisition function from observed human behavior, finding that many subjects exhibited exploration preferences beyond standard models, leading to an augmented model with superior fit.
This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as a collection of sample paths from a Bayesian optimization procedure. The methodology involves defining a likelihood on observed human behavior from an optimization task, where the likelihood is parameterized by a Bayesian optimization subroutine governed by an unknown acquisition function. This structure enables us to make inference on a subject's acquisition function while allowing their behavior to deviate around the solution to the Bayesian optimization subroutine. To test our methods, we designed a sequential optimization task which forced subjects to balance exploration and exploitation in search of an invisible target location. Applying our proposed methods to the resulting data, we find that many subjects tend to exhibit exploration preferences beyond that of standard acquisition functions to capture. Guided by the model discrepancies, we augment the candidate acquisition functions to yield a superior fit to the human behavior in this task.