Bayesian Optimization for Building Social-Influence-Free Consensus

2025-02-11

AI Summary

This paper addresses the challenge of biased individual preference elicitation in collective decision-making due to social influence, which impedes unbiased consensus formation. To tackle this, we propose Social Bayesian Optimization (SBO), a novel framework featuring a dual-voting mechanism jointly modeled with an implicit social graph. We theoretically establish that social graph learning converges faster than black-box utility optimization, enabling early reduction of costly private votes and prioritizing debiased public votes. SBO integrates Bayesian optimization, noise-robust voting modeling, axiomatized rational preferences, and social graph inference. Evaluated on real-world applications—including thermal comfort regulation, team formation, travel negotiation, and energy collaboration—SBO significantly improves consensus quality (+23.6% consensus rate) and voting efficiency (−41.8% average rounds).

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Social influence distortion in consensus
• Efficient social graph estimation
• Dual voting system optimization

Innovation

• Social Bayesian Optimization algorithm
• Dual voting system strategy
• Social graph estimation technique