Author

Date of Award

2022

Document Type

Thesis

Degree Name

Bachelors

Department

Natural Sciences

First Advisor

Gillman, David

Area of Concentration

Applied Mathematics and Computer Science

Abstract

This thesis provides a friendly introduction to the field of multi-armed bandits as a framework to consider the exploration-exploitation dilemma. We summarize recent literature that combines the established theory of bandits with recent ideas involving Bayesian-persuasion from algorithmic economics to consider a three-pronged exploration-exploitation-persuasion trade-off. We include initial empirical results concerning the Bayesian regret bounds for a recent incentive compatible bandit algorithm. We also provide an outline of ideas for future study in the area of incentivized multiagent bandit exploration.

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