Tractable Agreement Protocols
Natalie Collina, Surbhi Goel, Varun Gupta, Aaron Roth

TL;DR
This paper introduces a computationally efficient interactive protocol that enables multiple parties, including humans, to reach agreement on predictions, improving accuracy through iterative feedback and generalizing classical agreement theorems.
Contribution
It develops a general framework for agreement protocols that are tractable, extend classical theorems, and scale efficiently to multiple parties and complex outcome spaces.
Findings
Protocols improve prediction accuracy over individual parties.
Convergence results extend Aumann's Agreement Theorem under weaker assumptions.
Number of rounds to agreement is independent of outcome space size d.
Abstract
We present an efficient reduction that converts any machine learning algorithm into an interactive protocol, enabling collaboration with another party (e.g., a human) to achieve consensus on predictions and improve accuracy. This approach imposes calibration conditions on each party, which are computationally and statistically tractable relaxations of Bayesian rationality. These conditions are sensible even in prior-free settings, representing a significant generalization of Aumann's classic "agreement theorem." In our protocol, the model first provides a prediction. The human then responds by either agreeing or offering feedback. The model updates its state and revises its prediction, while the human may adjust their beliefs. This iterative process continues until the two parties reach agreement. Initially, we study a setting that extends Aumann's Agreement Theorem, where parties aim…
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Videos
Agreement and Alignment for Human-AI Collaborative Decision Making· youtube
Taxonomy
TopicsLaw, logistics, and international trade
MethodsSparse Evolutionary Training
