8 papers
Truthful Calibration Errors for Multi-Class Prediction
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune pr…
The Economics of No-regret Learning Algorithms
Jason Hartline
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 out…
Optimization of Scoring Rules
Jason D. Hartline, Yingkai Li, Liren Shan +1
We characterize the optimal reward functions (scoring rules) that incentivize an agent to acquire information and report it truthfully to the principal. The optimal scoring rules l…
A Geometric Analysis of Gains from Trade
Jason Hartline, Kangning Wang
We provide a geometric proof that the random proposer mechanism is a -approximation to the first-best gains from trade in bilateral exchange. We then refine this geometric analy…
Underspecified Human Decision Experiments Considered Harmful
Jessica Hullman, Alex Kale, Jason Hartline
Decision-making with information displays is a key focus of research in areas like human-AI collaboration and data visualization. However, what constitutes a decision problem, and…
Regulation of Algorithmic Collusion, Refined: Testing Pessimistic Calibrated Regret
Jason D. Hartline, Chang Wang, Chenhao Zhang
We study the regulation of algorithmic (non-)collusion amongst sellers in dynamic imperfect price competition by auditing their data as introduced by Hartline et al. [2024]. We dev…