USC researchers will present four papers at the 2026 ACM Conference on Economics and Computation (EC), with research exploring algorithmic fairness, computational social choice, market design and theoretical foundations at the intersection of economics and computer science.
Held July 6–10 in Rome, Italy, EC is the premier international conference on economics and computation, bringing together researchers from the crossroads of computer science, economics and related fields to advance the design and analysis of algorithms, markets and economic systems.
Faculty and students from labs across the university are attending this year’s conference, and this includes researchers from USC Viterbi School of Engineering’s Thomas Lord Department of Computer Science and Daniel J. Epstein Department of Industrial and Systems Engineering, USC Mark and Mary Stevens School of Computing and AI, USC Marshall School of Business and the USC Dornsife College of Letters, Arts and Sciences.
USC researchers will present work across the conference’s technical program, with computer science assistant professor Evi Micha leading two accepted papers. Micha’s research sits at the intersection of computer science, particularly artificial intelligence and theoretical computer science, and economics, with a focus on computational social choice and algorithmic fairness.
Aside from presenting papers, USC researchers are also serving in key conference leadership roles, with two faculty members on the senior program committee and seven faculty and students on the program committee.
The conference continues to grow increasingly competitive, with an acceptance rate of approximately 25% this year, underscoring the selectivity of the research presented at the annual event.
USC @ EC 2026 Research Spotlights
Smart Selling in the Digital Age: How to Compete with an All-Knowing Market
Every day, businesses must make split-second decisions about whether to accept an offer now or wait for a potentially better one later. In the paper, “Multiunit I.I.D. Prophet Inequalities via Extreme Value Asymptotics,” USC researchers investigate how sellers can make smarter decisions in fast-moving online markets, from digital advertising and e-commerce to transportation and revenue management.
The research examines “prophet inequalities,” a mathematical framework that compares real-time decision-making against an idealized “prophet” who knows every future offer in advance. Using tools from Extreme Value Theory, the researchers show that in large markets with many potential buyers, sellers can come much closer to this theoretical optimum than previously thought when selling multiple items. The work also reveals that a widely used shortcut strategy can perform poorly when supply is limited and competition is high, offering new insights for designing more reliable algorithms for modern online marketplaces.
This paper is led by Karthyek Murthy, an assistant professor of industrial and systems engineering, and his PhD student, Jieming Kong.
Best Strategy for Managing High-Stakes Competition Without Financial Incentives
From research grant competitions to project selection and school admissions, many high-stakes decisions require choosing among competing proposals without relying on financial incentives. In the paper, “Selecting Competing Proposals,” USC researchers present a new mathematical framework for designing fairer and more effective selection mechanisms in these settings.
The research examines what economists call “mechanism design without transfers,” in which organizers cannot use monetary payments to encourage honest behavior or balance competing interests. Instead, the researchers show that the optimal strategy intentionally adjusts selection standards while introducing advantages for participants in weaker competitive positions. By strategically leveling the playing field, the framework reduces conflict between competitors and produces more stable, efficient outcomes, offering new insights for organizations that must make difficult allocation decisions when money cannot be used as a tool.
This paper is led by USC Dornsife’s assistant professor of economics, Jonathan A. Libgober, and his postdoctoral scholar, Peiran Xiao.
USC-Affiliated Papers
(USC authors bolded)
Learning Fair Allocation of Indivisible Items from Limited Feedback
Xinyu Liu, David Kempe, Evi Micha
Jonathan Libgober, Peiran Xiao
Dynamic Consistent Proportionally Fair Clustering
Evi Micha, Satish Panda, Vasilis Varsamis
Multiunit I.I.D. Prophet Inequalities via Extreme Value Asymptotics
Jieming Kong, Karthyek Murthy
Published on July 6th, 2026
Last updated on July 7th, 2026