Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits

TL;DR

This paper introduces a Bayesian-based UCL algorithm for generalized Gaussian multi-armed bandit problems, demonstrating its optimal regret bounds and alignment with human decision-making behavior through empirical validation.

Contribution

It develops the UCL algorithm for various bandit settings, extending its applicability and showing its effectiveness in modeling human decision-making with empirical support.

Findings

UCL algorithm achieves logarithmic regret in standard bandits

Human behavior is well modeled by the stochastic UCL algorithm

Extensions to transition costs and graph structures maintain optimal regret

Abstract

We present a formal model of human decision-making in explore-exploit tasks using the context of multi-armed bandit problems, where the decision-maker must choose among multiple options with uncertain rewards. We address the standard multi-armed bandit problem, the multi-armed bandit problem with transition costs, and the multi-armed bandit problem on graphs. We focus on the case of Gaussian rewards in a setting where the decision-maker uses Bayesian inference to estimate the reward values. We model the decision-maker's prior knowledge with the Bayesian prior on the mean reward. We develop the upper credible limit (UCL) algorithm for the standard multi-armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors. We show how good priors and good assumptions on the correlation structure among arms can greatly enhance decision-making performance, even over short time horizons. We extend to the stochastic UCL algorithm and draw several connections to human decision-making behavior. We present empirical data from human experiments and show that human performance is efficiently captured by the stochastic UCL algorithm with appropriate parameters. For the multi-armed bandit problem with transition costs and the multi-armed bandit problem on graphs, we generalize the UCL algorithm to the block UCL algorithm and the graphical block UCL algorithm, respectively. We show that these algorithms also achieve logarithmic cumulative expected regret and require a sub-logarithmic expected number of transitions among arms. We further illustrate the performance of these algorithms with numerical examples. NB: Appendix G included in this version details minor modifications that correct for an oversight in the previously-published proofs. The remainder of the text reflects the published work.