Bayesian Optimization for Building Social-Influence-Free Consensus

TL;DR

This paper introduces Social Bayesian Optimization (SBO), a dual voting algorithm that efficiently achieves social-influence-free consensus in group decision-making by modeling social influence and reducing reliance on costly private votes.

Contribution

The paper presents SBO, a novel dual voting approach that models social influence and accelerates consensus-building with noisy public votes, reducing dependence on expensive private feedback.

Findings

Social graph estimation converges faster than utility estimation.

SBO effectively reduces private vote reliance early in the process.

Validated across diverse real-world applications.

Abstract

We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective decision-making encompasses group dynamics that may distort agents' preference feedback, thereby impeding their capacity to achieve a social-influence-free consensus -- the most preferable decision based on the aggregated agent utilities. We demonstrate that under mild rationality axioms, reaching social-influence-free consensus using noisy feedback alone is impossible. To address this, SBO employs a dual voting system: cheap but noisy public votes (e.g., show of hands in a meeting), and more accurate, though expensive, private votes (e.g., one-to-one interview). We model social influence using an unknown social graph and leverage the dual voting system to efficiently learn this graph. Our theoretical findigns show that social graph estimation converges faster than the black-box estimation of agents' utilities, allowing us to reduce reliance on costly private votes early in the process. This enables efficient consensus-building primarily through noisy public votes, which are debiased based on the estimated social graph to infer social-influence-free feedback. We validate the efficacy of SBO across multiple real-world applications, including thermal comfort, team building, travel negotiation, and energy trading collaboration.