Jason D. Hartline
· Professor of Computer ScienceNorthwestern University · Chemical Engineering
Active 2001–2026
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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
Selected publications
Proceedings of the 23rd ACM Conference on Economics and Computation · 2022 · 19 citations
This paper introduces an objective for optimizing proper scoring rules. The objective is to maximize the increase in payoff of a forecaster who exerts a binary level of effort to refine a posterior belief from a prior belief. In this framework we characterize optimal scoring rules in simple settings, give efficient algorithms for computing optimal scoring rules in complex settings, and identify simple scoring rules that are approximately optimal. In comparison, standard scoring rules in theory a…
A Decision Theoretic Framework for Measuring AI Reliance
2024-06-03 · 14 citations
articleOpen accessHumans frequently make decisions with the aid of artificially intelligent (AI) systems. A common pattern is for the AI to recommend an action to the human who retains control over the final decision. Researchers have identified ensuring that a human has appropriate reliance on an AI as a critical component of achieving complementary performance. We argue that the current definition of appropriate reliance used in such research lacks formal statistical grounding and can lead to contradictions. We…
Full surplus extraction from samples
Journal of Economic Theory · 2021 · 11 citations
Non-Quasi-Linear Agents in Quasi-Linear Mechanisms (Extended Abstract)
arXiv (Cornell University) · 2020 · 8 citations
Mechanisms with money are commonly designed under the assumption that agents are quasi-linear, meaning they have linear disutility for spending money. We study the implications when agents with non-linear (specifically, convex) disutility for payments participate in mechanisms designed for quasi-linear agents. We first show that any mechanism that is truthful for quasi-linear buyers has a simple best response function for buyers with non-linear disutility from payments, in which each bidder simp…
Underspecified Human Decision Experiments Considered Harmful
2025-04-24 · 2 citations
articleOpen accessSenior author
Recent grants
Collaborative Research: Mechanism Design and Approximation
NSF · $300k · 2008–2012
NSF · $416k · 2009–2014
AF: Small: Non-revelation Mechanism Design
NSF · $450k · 2016–2020
Frequent coauthors
- 25 shared
Shuchi Chawla
The University of Texas at Austin
- 18 shared
Andrew V. Goldberg
Amazon (United States)
- 17 shared
Aleck Johnsen
- 17 shared
Robert Kleinberg
- 16 shared
Denis Nekipelov
- 15 shared
Nima Haghpanah
Pennsylvania State University
- 15 shared
Brendan Lucier
Microsoft Research (United Kingdom)
- 15 shared
Yiding Feng
University of Chicago
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