Collaborative Prediction via Tractable Agreement Protocols

Abstract

Designing effective collaboration between humans and AI systems is crucial for leveraging their complementary abilities in complex decision tasks. But how should agents possessing unique, private knowledge—like a human expert and an AI model—interact to reach decisions better than either could alone? If they were perfect Bayesians with a shared prior, Aumann's classical agreement theorem suggests conversation leads to a prediction via agreement which is accuracy-improving. However, this relies on implausible assumptions about their knowledge and computational power.

 We show how to recover and generalize these guarantees using only computationally and statistically tractable assumptions. We develop efficient "collaboration protocols" where parties iteratively exchange only low-dimensional information – their current predictions or best-response actions – without needing to share underlying features. These protocols are grounded in conditions like conversation calibration/swap regret, which relax full Bayesian rationality, and are computationally efficiently enforceable. First, we prove this simple interaction leads to fast convergence to agreement, generalizing quantitative bounds even to high-dimensional and action-based settings. Second, we introduce a weak learning condition under which this agreement process inherently aggregates the parties' distinct information, that is, agents via our protocols arrive at final predictions that are provably competitive with an optimal predictor having access to their joint features. Together, these results offers a new, practical foundation for building systems that achieve the power of pooled knowledge through tractable interaction alone.

This talk is based on joint work with the amazing Natalie Collina, Varun Gupta, Ira Globus-Harris, Aaron Roth, Mirah Shi.