Social Bayesian Optimization for Building Truthful Consensus
Keywords: Bayesian optimisation, social choice theory, preference learning
Abstract: We introduce *Social Bayesian Optimization* (SBO), a query-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 truthful consensus. We demonstrate that under standard rationality assumptions, reaching truthful consensus—the most preferable decision based on the aggregated latent agent utilities—using noisy feedback alone is impossible. To address this, SBO employs a dual voting system: cost-effective but noisy public votes, and more accurate, though expensive, private votes. We model social influence using an unknown social graph and leverage the dual voting system to efficiently learn this graph. Our findings 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 truthful feedback. We validate the effectiveness of SBO across multiple real-world applications, including thermal comfort optimization, team building, travel destination discussion, and strategic alliance in energy trading.
Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 982
Loading