Iterative combinatorial auctions are widely used in high stakes settings such as spectrum auctions. Such auctions can be hard to analyze, making it difficult for bidders to determine how to behave and for designers to optimize auction rules to ensure desirable outcomes such as high revenue or welfare. In this paper, we investigate whether multi-agent reinforcement learning (MARL) algorithms can be used to understand iterative combinatorial auctions, given that these algorithms have recently shown empirical success in several other domains. We find that MARL can indeed benefit auction analysis, but that deploying it effectively is nontrivial. We begin by describing modelling decisions that keep the resulting game tractable without sacrificing important features such as imperfect information or asymmetry between bidders. We also discuss how to navigate pitfalls of various MARL algorithms, how to overcome challenges in verifying convergence, and how to generate and interpret multiple equilibria. We illustrate the promise of our resulting approach by using it to evaluate a specific rule change to a clock auction, finding substantially different auction outcomes due to complex changes in bidders' behavior.

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM), a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2

Over 13 months in 2016-17 the US Federal Communications Commission (FCC) conducted an "incentive auction" to repurpose radio spectrum from broadcast television to wireless internet. The result of the auction was to remove 14 UHF-TV channels from broadcast use, sell 70 MHz of wireless internet licenses for $19.8 billion, and create 14 MHz of spectrum for unlicensed uses. With fewer UHF channels remaining for TV broadcast, the TV spectrum was also reorganized. Each station was either "repacked" in the leftover channels or voluntarily sold its broadcast rights, either going off the air or switching to a different band. The volunteers received a total of $10.05 billion to yield or exchange their rights and make repacking possible.

research-article Designing and Evolving an Electronic Agricultural Marketplace in Uganda Share on Authors: Neil Newman University of British Columbia, Vancouver, BC, Canada University of British Columbia, Vancouver, BC, CanadaView Profile , Lauren Falcao Bergquist University of Chicago, Chicago, IL, USA University of Chicago, Chicago, IL, USAView Profile , Nicole Immorlica Microsoft Research, Cambridge, MA, USA Microsoft Research, Cambridge, MA, USAView Profile , Kevin Leyton-Brown University of British Columbia, Vancouver, BC, Canada University of British Columbia, Vancouver, BC, CanadaView Profile , Brendan Lucier Microsoft Research, Cambridge, MA, USA Microsoft Research, Cambridge, MA, USAView Profile , Craig McIntosh University of California San Diego, San Diego, CA, USA University of California San Diego, San Diego, CA, USAView Profile , John Quinn Makerere Univeresity, Kampala, Uganda Makerere Univeresity, Kampala, UgandaView Profile , Richard Ssekibuule Makerere Univeresity, Kampala, Uganda Makerere Univeresity, Kampala, UgandaView Profile Authors Info & Affiliations COMPASS '18: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable SocietiesJune 2018 Article No.: 14Pages 1–11https://doi.org/10.1145/3209811.3209862Published:20 June 2018 3citation164DownloadsMetricsTotal Citations3Total Downloads164Last 12 Months29Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Computational mechanism analysis is a recent approach to economic analysis in which a mechanism design setting is analyzed entirely by a computer. For games with non-trivial numbers of players and actions, the approach is only feasible when these games can be encoded compactly, e.g., as Action-Graph Games. Such encoding is currently a manual process requiring expert knowledge; our aim is to simplify and automate it. Our contribution, the Positronic Economist is a software system having two parts: (1) a Python-based language for succinctly describing mechanisms; and (2) a system that takes such descriptions as input, automatically identifies computationally useful structure, and produces a compact Action-Graph Game.

We investigate the problem of repacking stations in the FCC's upcoming, multi-billion-dollar "incentive auction". Early efforts to solve this problem considered mixed-integer programming formulations, which we show are unable to reliably solve realistic, national-scale problem instances. We describe the result of a multi-year investigation of alternatives: a solver, SATFC, that has been adopted by the FCC for use in the incentive auction. SATFC is based on a SAT encoding paired with a wide range of techniques: constraint graph decomposition; novel caching mechanisms that allow for reuse of partial solutions from related, solved problems; algorithm configuration; algorithm portfolios; and the marriage of local-search and complete solver strategies. We show that our approach solves virtually all of a set of problems derived from auction simulations within the short time budget required in practice.