Mohammad Hajiaghayi, the Jack and Rita G. Minker Professor of Computer Science at the University of Maryland, will be recognized with two Test of Time Awards from the Association for Computing Machinery’s Special Interest Group on Economics and Computation (ACM SIGecom) for papers that helped shape the modern field of algorithmic economics and online mechanism design.
Hajiaghayi co-authored two papers that will each receive an ACM SIGecom Test of Time Award, one of the field’s highest honors recognizing research that has had a lasting impact on the intersection of computer science and economics.
The awards will be presented at the upcoming 27th ACM Conference on Economics and Computation (EC’26), held this year in Rome from July 6–10.
“These papers were ahead of their time in identifying fundamental challenges in online markets and proposing techniques that remain influential today,” said Edith Elkind, the Ginni Rometty Professor of Computer Science at Northwestern University and chair of the award selection committee. “Their impact can be seen across a wide range of later work in mechanism design, online algorithms and digital market theory.”
The first award recognizes “Adaptive Limited-Supply Online Auctions,” published in the proceedings of the 2004 ACM Conference on Electronic Commerce (EC’04). Hajiaghayi co-authored the paper while he was a Ph.D. student at the Massachusetts Institute of Technology alongside Robert Kleinberg and David C. Parkes.
The paper addressed a central challenge in online markets: how to design auctions when buyers arrive over time and the seller has only a limited supply of goods. It pioneered the use of the “secretary problem” in online auctions and mechanism design, where buyers arrive sequentially in random order and allocation decisions must be made immediately without knowledge of future arrivals.
The authors studied settings in which bidder valuations were drawn independently from unknown distributions and developed adaptive mechanisms that learn from early arrivals before deciding when to allocate resources. More broadly, the work introduced secretary-style methods into online mechanism design, creating a framework for balancing exploration and commitment under uncertainty.
The resulting mechanisms achieved provable guarantees for revenue and social welfare, helping influence later work in online mechanism design, matching and real-time market algorithms.
The research also combined ideas from online algorithms, auction theory and economics to create mechanisms that remained “strategyproof”—meaning participants could not gain an advantage by misrepresenting information—while still performing well compared with an ideal offline auction. The paper additionally established mathematical limits on how well online mechanisms could perform relative to an optimal offline auction, results that later became influential in research on online decision-making and mechanism design.
At the time, online auctions and digital marketplaces were becoming increasingly important in areas such as advertising, ticket sales and e-commerce. The paper helped establish theoretical foundations for understanding how markets function when decisions must be made in real time without knowing future demand.
The second award recognizes “Automated Online Mechanism Design and Prophet Inequalities,” published in the proceedings of the AAAI Conference on Artificial Intelligence in 2007. Hajiaghayi, who was a postdoctoral associate at Carnegie Mellon University when the paper was published, co-authored the work with Kleinberg and Tuomas Sandholm.
The research addressed a fundamental challenge in digital markets: how to design online auction mechanisms when sellers have statistical information about buyers but do not know the eventual size of the market.
The work pioneered the use of “prophet inequalities” in mechanism design, introducing one of the first frameworks showing how prior distributional information can be used to make near-optimal online decisions under uncertainty.
Prophet inequalities compare an online algorithm—which must make irrevocable decisions sequentially—to an idealized “prophet” with complete knowledge of future outcomes. The paper showed how historical distributions of bidder valuations could be used to derive adaptive pricing and allocation rules that closely approximate the performance of this offline optimum.
The work established a framework for using historical statistical information to design effective online pricing and allocation mechanisms, influencing later research in online mechanism design, posted-price systems, revenue optimization and learning-augmented algorithms.
The paper also proved strong impossibility results, showing that without reliable information about market size, no online mechanism can consistently achieve a constant-factor approximation to optimal revenue or social welfare.
Together, the two papers helped establish foundational frameworks for online mechanism design by introducing secretary and prophet methods into online auctions and economic decision-making. Their ideas influenced later work in online matching, pricing, allocation and market design.
ACM SIGecom, which presents the award, promotes research at the intersection of economics and computation, with a focus on using computational reasoning and economic theory to better understand markets, incentives and social interactions in digital environments.
The Test of Time Award is among the most selective honors in economics and computation, recognizing papers that continue to influence research and applications years after publication. Past recipients include researchers whose work defined major areas of theoretical computer science and online markets, including landmark papers on Nash equilibria, digital goods auctions and online advertising systems.
Hajiaghayi joined the University of Maryland faculty in 2010, where his interview talk focused on the two papers now receiving Test of Time recognition. Since then, he has built an internationally recognized research program spanning algorithms, artificial intelligence, game theory, machine learning and optimization. His work has addressed topics ranging from network design and computational complexity to market mechanisms and AI for social good.
In addition to his appointment in the Department of Computer Science, Hajiaghayi holds a joint appointment with the University of Maryland Institute for Advanced Computer Studies (UMIACS) and is a core faculty member of the UMD Center for Machine Learning. He is also affiliated with the UMD Artificial Intelligence Interdisciplinary Institute, the Institute for Systems Research and the Department of Economics.
Hajiaghayi said the papers emerged at a time when researchers were only beginning to understand how online systems would reshape economic interactions and decision-making.
“These papers came from collaborations with remarkable researchers and from questions that were just beginning to emerge as online systems became more central to everyday life,” he said. “It is gratifying to see that the ideas still resonate with the community today.”
Many of the ideas introduced in those papers remain relevant today as companies and researchers continue developing digital marketplaces, automated pricing systems and AI-driven platforms.
—Story by Tom Ventsias, UMIACS communications group