CDS PhD Seminar Series - Tejovan Parker & Gabe Maayan

Abstract: We consider the form of externalities where some agent(s) have preferences over the outcome of a mechanism, and their preferences cannot be known before the mechanism is run. Often, this problem is solved by an auctioneer inserting dummy bids to represent the externalities. However, there may be ethical, trust, or power issues with delegating the determination of one's values to a central entity. And, it is extremely unreasonable to have all agents constantly estimate and report their values for the actions of all other agents in a system. Even if it were acceptable for a central entity to estimate externalities, it is more efficient to only audit what is more likely to be harmful, rather than auditing everything. To address this, we consider auctions where the auctioneer has the power to (randomly) audit bidders to learn their externality, and impose penalties accordingly. In this setting, the power to audit results in equivalent bidder behavior as letting the auctioneer set individualized entry fees for bidders as a function of their non-manipulable externality type, and this results in thresholds of participation as functions of externality. This setting is motivated by a variety of practical scenarios. For example, an auctioneer might run a social or traditional media platform where bidders compete to post news or ads on user feeds. In this setting, end users can experience bidders' posts as nuisance costs, incurring negative externalities. Our objective is to maximize total welfare, i.e. the sum of individual value and externalities. In this paper, we show how penalty functions induce thresholds of participation, and prove analytically that welfare optimal participation thresholds in the i.i.d. setting with no competition are linear. Additionally, in the setting with competition for a single item and where i.i.d. bidders may only take two discrete types, the optimal threshold is linear and behaves analogously to Myersonian revenue-maximizing reserve-prices. To illustrate results in more complicated settings, we use simulation with computational optimization to characterize welfare increases over participation threshold functions. We collect a dataset from X (formerly Twitter) to create an empirical joint-distribution of sender and receiver value, and simulate auctions from this empirical data. We find that optimal thresholds shift welfare from producers to users and increase overall welfare in all settings. We also observe that optimal thresholds are linear even with the empirical type distributions. However, the penalty functions will not be linear in general, which makes an interesting comparison to linear contracts. Our results suggest that auditing and penalizing externalities in real-world sponsored-search and advertising auctions have the potential to create substantial increases in social welfare. Bio: Tejovan Parker is a third-year PhD student at Boston University’s Faculty of Computing and Data Sciences. Previously, he studied Mechanical and Global Engineering at the University of Colorado Boulder. He is interested in better management of social, political, and economic systems through mathematical and algorithmic methods. Tejovan began his PhD studies at BU in Fall 2022. In his first two years at CDS, he is building his expertise and looking to assist in existing research within misinformation markets. Gabe is a third-year PhD student at Boston University’s Faculty of Computing and Data Sciences. Previously, he worked on a variety of projects at the MITRE Corporation and received his Bachelor of Science in Computer Science at Rensselaer Polytechnic Institute. His research interests are in Complexity Science, Complex Systems Analysis and Modeling, and Agent-Based Modeling.