About

Jason D. Hartline is a Professor of Computer Science at Northwestern University, affiliated with the Northwestern Engineering school. His research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. He focuses on optimal behavior and outcomes in complex environments, applying the theory of approximation to demonstrate that simple and natural behaviors can be approximately optimal in such settings. His work is particularly applied to auction theory and mechanism design, and he is the author of the graduate textbook 'Mechanism Design and Approximation,' which is under preparation.

Research topics

• Mathematics
• Computer Science
• Data Mining
• Machine Learning
• Artificial Intelligence
• Mathematical economics
• Economics
• Geometry
• Statistics
• Mathematical analysis
• Mathematical optimization
• Econometrics
• Materials science
• Combinatorics
• Chromatography

Selected publications

• ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making

  ArXiv.org · 2026-01-01

  articleOpen access

  Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM using complementary information as reward to output signals that complement existing agent decisions. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information and produces plausible explanations of complementary signals to support downstream decision-makers.

  PublisherOA PDF

• The Economics of No-regret Learning Algorithms

  ArXiv.org · 2026-01-29

  articleOpen access1st authorCorresponding

  A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.

  PublisherOA PDF

• Clarification of `Algorithmic Collusion without Threats'

  Open MIND · 2026-02-15

  preprint1st authorCorresponding

  This brief note clarifies that the scenario described in Arunachaleswaran et al. (2025) -- titled `Algorithmic Collusion without Threats' -- is not one of collusion, but one where one player is behaving non-competitively and the other is behaving competitively.

  DOI

• Revenue Non-monotonicity in Matching Markets

  ArXiv.org · 2026-02-24

  articleOpen access1st authorCorresponding

  The Vickrey-Clarke-Groves (VCG) mechanism is infamously revenue non-monotone in combinatorial auctions. I.e., when a buyer increases their value for a bundle of items, the total auction revenue may decrease. Combinatorial auctions exhibit complementarities which broadly result in complexities in auction theory. This brief note shows that non-monotonicity in multi-item auctions is not a result of complementarities, and in fact, VCG is revenue non-monotone even in matching markets.

  PublisherOA PDF

• Clarification of `Algorithmic Collusion without Threats'

  arXiv (Cornell University) · 2026-02-15

  articleOpen access1st authorCorresponding

  This brief note clarifies that the scenario described in Arunachaleswaran et al. (2025) -- titled `Algorithmic Collusion without Threats' -- is not one of collusion, but one where one player is behaving non-competitively and the other is behaving competitively.

  PublisherOA PDF

• Revenue Non-monotonicity in Matching Markets

  Open MIND · 2026-02-24

  preprint1st authorCorresponding

  The Vickrey-Clarke-Groves (VCG) mechanism is infamously revenue non-monotone in combinatorial auctions. I.e., when a buyer increases their value for a bundle of items, the total auction revenue may decrease. Combinatorial auctions exhibit complementarities which broadly result in complexities in auction theory. This brief note shows that non-monotonicity in multi-item auctions is not a result of complementarities, and in fact, VCG is revenue non-monotone even in matching markets.

  DOI

• ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making

  arXiv (Cornell University) · 2026-02-23

  preprintOpen access

  Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to the table to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM using complementary information as reward to output signals that complement existing agent decisions. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information and produces plausible explanations of complementary signals to support downstream decision-makers.

  PublisherDOI

• The Economics of No-regret Learning Algorithms

  Open MIND · 2026-01-29

  preprint1st authorCorresponding

  A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.

  DOI

• AI Suppression: E-Discovery Software and <i>Brady</i>

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• A Perfectly Truthful Calibration Measure

  ArXiv.org · 2025-08-18

  preprintOpen access1st authorCorresponding

  Calibration requires that predictions are conditionally unbiased and, therefore, reliably interpretable as probabilities. A calibration measure quantifies how far a predictor is from perfect calibration. As introduced by Haghtalab et al. (2024), a calibration measure is truthful if it is minimized in expectation when a predictor outputs the ground-truth probabilities. Predicting the true probabilities guarantees perfect calibration, but in reality, when calibration is evaluated on a random sample, all known calibration measures incentivize predictors to lie in order to appear more calibrated. Such lack of truthfulness motivated Haghtalab et al. (2024) and Qiao and Zhao (2025) to construct approximately truthful calibration measures in the sequential prediction setting, but no perfectly truthful calibration measure was known to exist even in the more basic batch setting. We design a simple, perfectly and strictly truthful, sound and complete calibration measure in the batch setting: averaged two-bin calibration error (ATB). ATB is quadratically related to two existing calibration measures: the smooth calibration error smCal and the lower distance to calibration distCal. The simplicity in our definition of ATB makes it efficient and straightforward to compute, allowing us to give the first linear-time calibration testing algorithm, improving a result of Hu et al. (2024). We also introduce a general recipe for constructing truthful measures based on the variance additivity of independent random variables, which proves the truthfulness of ATB as a special case and allows us to construct other truthful calibration measures such as quantile-binned l_2-ECE.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Mechanism Design and Approximation

  NSF · $300k · 2008–2012

• CAREER: Mechanism Design

  NSF · $416k · 2009–2014

• AF: Small: Non-revelation Mechanism Design

  NSF · $450k · 2016–2020

• ICES: Large: Collaborative Research: Towards Realistic Mechanisms: statistics, inference, and approximation in simple Bayes-Nash implementation

  NSF · $333k · 2011–2016

• HDR TRIPODS: Collaborative Research: Institute for Data, Econometrics, Algorithms and Learning

  NSF · $850k · 2019–2023

Frequent coauthors

• Shuchi Chawla

  The University of Texas at Austin

  25 shared

• Andrew V. Goldberg

  Amazon (United States)

  18 shared

• Aleck Johnsen

  17 shared

• Robert Kleinberg

  17 shared

• Denis Nekipelov

  16 shared

• Nima Haghpanah

  Pennsylvania State University

  15 shared

• Brendan Lucier

  Microsoft Research (United Kingdom)

  15 shared

• Yiding Feng

  University of Chicago

  15 shared