Strong Northwestern CS Presence at the ACM Conference on Economics and Computation
Faculty, students, and postdocs participated in the annual forum for advances in theory, empirics, and applications at the interface of economics and computation.
Northwestern CS had a strong presence at the Twenty-Third Association for Computing Machinery (ACM) Conference on Economics and Computation (EC'22), held July 11-15 at the University of Colorado Boulder and sponsored by the ACM Special Interest Group on Economics and Computation (SIGecom).
A leader in CS+Economics research, Hartline is a professor of computer science and, by courtesy, professor of managerial economics and decision sciences in Northwestern’s Kellogg School of Management and professor of economics at the Weinberg College of Arts and Sciences. He applies design and analysis methodologies from computer science to explain and improve the behavior and outcomes of complex economic systems.
“It’s great to see the leadership and impact that researchers across three Northwestern departments, and alumni, are having on the interdisciplinary field of Economics and Computation,” Hartline said. “In addition to the long list of Northwestern papers that were accepted, many of our junior PhD students attended the conference, asked questions during talks, and got to really experience being part of the wonderful research community of the ACM Special Interest Group on Economics and Computation.”
Hartline collaborated with Liren Shan, a fourth-year PhD student in the Northwestern CS Theory Group, advised by professor of computer science Konstantin Makarychev; Yifan Wu, a second-year PhD student advised by Hartline; and former advisee Yingkai Li (PhD ’22), currently a postdoc at the Cowles Foundation for Research in Economics at Yale University, on the paper “Optimization of Scoring Rules.”
The team presented a framework for optimizing proper scoring rules with practical application in peer grading, peer prediction, and exam scoring. Scoring rules are used to elicit information from a strategic agent. Proper scoring rules incentivize forecasters to report their belief distributions about unknown and probabilistic states truthfully. The proposed framework uses proper scoring rules to prompt effort from the agent, for instance algorithmically grading peer reviews to incentivize effort to improve learning outcomes.
The team also presented related outcomes, titled “Optimal Scoring Rules for Multi-dimensional Effort,” during the Recent Research: Delegation and Scoring Rules track at the Algorithmic Contract Design: Present and Future EC’22 workshop.
Modibo Camara (PhD ’22), who was jointly advised by Hartline and Eddie Dekel, William R. Kenan, Jr. Professor of Economics at Northwestern’s Weinberg College of Arts and Sciences, won the ACM SIGecom Best Paper with a Student Lead Author and an Exemplary Theory Track Award for his paper “Computationally Tractable Choice.” The theoretical framework incorporates computational constraints into decision theory to examine how cognitive limitations and common heuristics affect behavior. Applying the Turing machine computational model to a general model of choice under risk, Camara demonstrated the potential value of computational tractability to economic theory and predictions about economic behavior.
Camara also presented his paper “Mechanism Design with a Common Dataset.” He plans to join the faculty of the Stanford Graduate School of Business in 2023 following a position as a Saieh Family Fellow at the University of Chicago's Becker-Friedman Institute.
Annie Liang, assistant professor of economics at Northwestern’s Weinberg College of Arts and Sciences and the Karr Family Assistant Professor of Computer Science, chaired the conference parallel session “Equilibrium in Games.” She also presented the paper “Algorithmic Design: Fairness Versus Accuracy” with collaborators Jay Lu (University of California, Los Angeles) and Xiaosheng Mu (Princeton University).
Additional Northwestern contributions to the EC’22 technical program and virtual poster session included:
- “An Economic Framework for Vaccine Prioritization” — Piotr Dworczak, assistant professor in economics at Weinberg; Mohammad Akbarpour (Stanford University), Eric Budish (University of Chicago), and Scott Kominers (Harvard University)
- “Bias-Variance Games” — Hartline; Aleck Johnsen (PhD ‘21); and Yiding Feng (PhD ’21), a postdoctoral researcher at Microsoft Research Lab – New England; Ronen Gradwohl (Ariel University); and Denis Nekipelov (University of Virginia)
- “Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence” — Li, Jason Gaitonde (Cornell University), Bar Light (Microsoft Research – New York City), Brendan Lucier (Microsoft Research – New England), Aleksandrs Slivkins (Microsoft Research NYC)
- “Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality” — Saeed Alaei, a former visiting scholar; Azarakhsh Malekian, a former postdoctoral fellow advised by Hartline and Nicole Immorlica; Ali Makhdoumi (Massachusetts Institute of Technology); and Rad Niazadeh (University of Chicago). As PhD students, Malekian and Alaei were advised by Khuller when he was a faculty member at the University of Maryland, College Park.
- “Incentivizing Participation in Clinical Trials” — Li and Slivkins
- “Near-Optimal Bayesian Online Assortment of Reusable Resources” — Feng, Niazadeh, and Amin Saberi (Stanford University)
- “Non-Strategic Structural Inference (for Initial Play)” — Hartline, Daniel Chui (University of Alberta) and James Wright (University of Alberta)
- “Online Bayesian Recommendation with No Regret” — Feng, Wei Tang (Washington University in St. Louis), and Haifeng Xu (University of Chicago)
- “Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms” — Malekian, Alireza Fallah (Massachusetts Institute of Technology), Makhdoumi, Asuman Ozdaglar (Massachusetts Institute of Technology)
- “Selling Data to an Agent with Endogenous Information” — Li
- “Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning” — Zhaoran Wang, assistant professor of industrial engineering and management sciences and (by courtesy) of computer science at Northwestern Engineering; Jibang Wu (University of Virginia), Zixuan Zhang (University of Science and Technology of China), Zhe Feng (Google), Zhuoran Yang (Yale University), Michael I. Jordan (University of California, Berkeley), and Xu.
- “Screening of Budgeted Agents” — Hartline; Camara; Johnsen; Sheng Long, a second-year PhD student in computer science advised by Hartline; and Hedyeh Beyhaghi, a former Northwestern CS postdoctoral research fellow mentored by Hartline and Khuller.
- “Visualization Equilibrium” — Hartline; Jessica Hullman, Ginni Rometty Professor of Computer Science at the McCormick School of Engineering; and Paula Kayongo, a fourth-year PhD student in computer science