Ziyang Guo, Yifan Wu, Jason Hartline, Kenneth Holstein, Jessica Hullman
原文摘要
Multi-agent decision pipelines can outperform single agent workflows when complementarity holds, i.e., different agents bring unique information to inform a final decision. We propose ComplLLM, a post-training framework based on decision theory that fine-tunes a decision-assistant LLM to output signals that complement existing agent decisions, using complementary information as reward. We validate ComplLLM on synthetic and real-world tasks involving domain experts, demonstrating how the approach recovers known complementary information, produces plausible explanations of complementary signals to support downstream decision-makers, and improves human and LLM agent decision performance in controlled studies.
This paper performs a robust analysis of welfare (i.e., the price of anarchy) in multi-unit first-price auctions with independent private values and unit-demand bidders. We employ a smoothness framework (see \citealp{ST-13}) to analyze the price of anarchy when supply is large. The main barrier to analysis in this framework is a worst-case bid distribution that establishes a lower bound on the price of anarchy of $1- \frac{1}{e}$. We show that such worst-case distributions cannot arise in equilibrium when agents' values satisfy a mild density lower bound condition. This characterization leads to novel asymptotic lower bounds. In particular, the price of anarchy for $ε$-approximate equilibrium as the number of units goes to infinity is at least $1-Θ\left(ε\log \left(\frac{1}ε\right)\right)$. Our analysis relies on a key structural property of equilibrium bid distributions: for a welfare analysis in any multi-unit first-price auction, it is without loss to analyze (approximate) equilibria with no atoms. In addition to this, we establish continuity and monotonicity properties of Bayes-Nash equilibria. We believe these results are of independent interest.
Huaman Sun, Dingcheng Wang, Jason Hartline, Jessica Hullman
原文摘要
Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit decision costs. We propose a decision-theoretic framework that decomposes LLMs' decision loss into two components: forming accurate beliefs from provided evidence and translating those beliefs into actions that optimize a provided utility function. Using a synthetic benchmark with known ground truth, we apply the decomposition to characterize probabilistic reasoning in frontier and open-sourced models. We further evaluate whether RL interventions targeting beliefs, decisions, or both improve these components across three domains, whether improvements transfer across components and elicitation formats, and whether decision performance can improve without improvement in belief formation. We find that targeting one component of probabilistic reasoning redistributes decision loss, improving the target without necessarily transferring to others, and that jointly targeting belief formation and decision-making improves both but hinges on matched formats between training and evaluation.