2 citations · 3 across the 17 of their papers we have counts for
20 papers
Learning Checklists to Reduce Lottery Effects in Scientific Peer Review
Xuanyou Liu, Ziyang Guo, Jason Hartline +1
Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We…
Diagnosing and Improving Probabilistic Reasoning in Large Language Models
Huaman Sun, Dingcheng Wang, Jason Hartline +1
Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit decision costs. We propose a…
Truthful Calibration Measures for Sequential Prediction
Anagha Gokul, Jason Hartline, Lunjia Hu +2
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated…
Sample Complexity of Peer Prediction
Abdellah Aznag, Robin Bowers, Rachel Cummings +3
Peer prediction seeks to incentivize agents to truthfully report an observed signal by rewarding joint sets of reports without observing a ground truth. Following the generalizatio…
Scoring Rules! Statistical and Strategic Alignment for Text Evaluation Metrics
Shengwei Xu, Yuxuan Lu, Yifan Wu +2
Reference-based text evaluation metrics, which are widely used to assess natural language generation systems, score a candidate response by comparing it with a reference response.…
ComplLLM: Fine-tuning LLMs to Discover Complementary Signals for Decision-making
Ziyang Guo, Yifan Wu, Jason Hartline +2
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 deci…
Clarification of `Algorithmic Collusion without Threats'
Jason Hartline
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…
Scale-robust Auctions
Jason Hartline, Aleck Johnsen, Yingkai Li
We study auctions that are robust at any scale, i.e., they can be applied to sell both expensive and cheap items and achieve the best multiplicative approximation of the optimal re…
A Perfectly Truthful Calibration Measure
Jason Hartline, Lunjia Hu, Yifan Wu
Calibration requires that predictions are conditionally unbiased and, therefore, reliably interpretable as probabilities. A calibration measure quantifies how far a predictor is fr…
Behavioral Study of Dashboard Mechanisms
Paula Kayongo, Jessica Hullman, Jason Hartline
Visualization dashboards are increasingly used in strategic settings like auctions to enhance decision-making and reduce strategic confusion. This paper presents behavioral experim…
Aligned Textual Scoring Rules
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Scoring rules elicit probabilistic predictions from a strategic agent by scoring the prediction against a ground truth state. A scoring rule is proper if, from the agent's perspect…
Smooth Calibration and Decision Making
Jason Hartline, Yifan Wu, Yunran Yang
Calibration requires predictor outputs to be consistent with their Bayesian posteriors. For machine learning predictors that do not distinguish between small perturbations, calibra…
Explaining and Improving Information Complementarities in Multi-Agent Decision-making
Ziyang Guo, Yifan Wu, Jason Hartline +1
Multiple agents are increasingly combined to make decisions with the expectation of achieving complementary performance, where the decisions they make together outperform those mad…
Unexploited Information Value in Human-AI Collaboration
Ziyang Guo, Yifan Wu, Jason Hartline +1
Humans and AIs are often paired on decision tasks with the expectation of achieving complementary performance -- where the combination of human and AI outperforms either one alone.…
ElicitationGPT: Text Elicitation Mechanisms via Language Models
Yifan Wu, Jason Hartline
Scoring rules evaluate probabilistic forecasts of an unknown state against the realized state and are a fundamental building block in the incentivized elicitation of information. T…
Subgame Optimal and Prior-Independent Online Algorithms
Jason Hartline, Aleck Johnsen, Anant Shah
This paper takes a game theoretic approach to the design and analysis of online algorithms and illustrates the approach on the finite-horizon ski-rental problem. This approach allo…
Fundamental Limits of Throughput and Availability: Applications to prophet inequalities & transaction fee mechanism design
Aadityan Ganesh, Jason Hartline, Atanu R Sinha +1
This paper studies the fundamental limits of availability and throughput for independent and heterogeneous demands of a limited resource. Availability is the probability that the d…
A Decision Theoretic Framework for Measuring AI Reliance
Ziyang Guo, Yifan Wu, Jason Hartline +1
Humans 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…
Extension of Minimax for Algorithmic Lower Bounds
Jason Hartline, Aleck Johnsen
This paper considers the use of Yao's Minimax Theorem in robust algorithm design, e.g., for online algorithms, where the algorithm designer aims to minimize the ratio of the algori…
Designing Shared Information Displays for Agents of Varying Strategic Sophistication
Dongping Zhang, Jason Hartline, Jessica Hullman
Data-driven predictions are often perceived as inaccurate in hindsight due to behavioral responses. In this study, we explore the role of interface design choices in shaping indivi…