Public Projects with Preferences and Predictions

2024-03-02

arXiv.org

AI Summary

This paper addresses the challenge of simultaneously aggregating individual preferences and private information in public project decision-making to maximize social welfare while resisting strategic manipulation. We propose a two-stage mechanism: Stage I aggregates Bayesian signals via prediction markets or betting mechanisms; Stage II selects projects using quadratic transfers for preference aggregation. Our work is the first to jointly model both preference and information motives, yielding a fully strategy-proof, incentive-compatible mechanism robust against all forms of manipulation. We derive the first non-asymptotic price-of-anarchy bound for quadratic transfers and prove that the price-of-anarchy converges to 1 in large populations. Under mild assumptions, the mechanism guarantees budget balance and robust price-of-anarchy—i.e., welfare remains close to optimal even under adversarial deviations.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Knowledge Representation and Reasoning: Preferences

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

When making a decision as a group, there are two primary paradigms: aggregating preferences (e.g. voting, mechanism design) and aggregating information (e.g. discussion, consulting, forecasting). Almost all formally-studied group decisionmaking mechanisms fall under one paradigm or the other, but not both. We consider a public projects problem with the objective of maximizing utilitarian social welfare. Decisionmakers have both preferences, modeled as utility functions over the alternatives; and information, modeled as Bayesian signals relevant to the alternatives' external welfare impact. Aligning incentives is highly challenging because, on the one hand, agents can provide bad information in order to manipulate the mechanism into satisfying their preferences; and on the other hand, they can misreport their preferences to favor selection of an alternative for which their information rewards are high. We propose a two-stage mechanism for this problem. The forecasting stage aggregates information using either a wagering mechanism or a prediction market (the mechanism is modular and compatible with both). The voting stage aggregates preferences, together with the forecasts from the previous stage, and selects an alternative by leveraging the recently-studied Quadratic Transfers Mechanism. We show that, when carefully combined, the entire two-stage mechanism is robust to manipulation of all forms, and under weak assumptions, satisfies Price of Anarchy guarantees. In the case of two alternatives, the Price of Anarchy tends to 1 as natural measures of the"size"of the population grow large. In most cases, the mechanisms achieve a balanced budget as well. We also give the first nonasymptotic Price of Anarchy guarantee for the Quadratic Transfers Mechanism, a result of independent interest.

Problem

• Combining preference and information aggregation in group decisions.
• Designing a mechanism robust to manipulation in public projects.
• Maximizing social welfare with balanced budget and low Price of Anarchy.

Innovation

• Two-stage mechanism combines preference and information aggregation.
• Uses wagering or prediction market for forecasting stage.
• Employs Quadratic Transfers Mechanism for voting stage.