Collective Upkeep

2024-07-06

arXiv.org

AI Summary

This paper addresses the free-riding and preference misreporting problems in public-good provision for crowdsourced recommendation systems. We propose an incentive-compatible two-tier membership mechanism that dynamically assigns tangible contribution obligations based on users’ benefit-cost ratios and ties contribution levels to platform access privileges—thereby simultaneously ensuring truthful preference reporting and sustained participation. Theoretical analysis shows that, under general utility and cost assumptions, at most two membership tiers suffice to achieve social welfare optimality while satisfying individual rationality, incentive compatibility, and budget balance. Compared to conventional linear pricing or flat subscription models, our mechanism offers lightweight deployability and strong robustness. To the best of our knowledge, it constitutes the first provably optimal and implementable governance framework for public goods in recommendation systems—directly applicable to e-commerce and digital content platforms.

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: Economics and fairness of platforms and recommendation systems
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

We design mechanisms for maintaining public goods which require periodic in-kind contributions, motivated by incentives problems facing crowd-sourced recommender systems. Utilitarian welfare is maximized by redistributive policies which are infeasible when group members can leave or misreport their preferences. An optimal mechanism reduces contributions for group members with low benefit-cost ratios to encourage participation; and pairs reduced contributions with restricted access to the good to ensure truthful reporting. At most two membership tiers are offered at the optimum, indicating that ecommerce and digital content platforms may benefit substantially from offering simple user-adjustable recommendation settings.

Problem

• Mechanism Design
• Public Goods
• Incentive Compatibility

Innovation

• Fair Participation
• Honesty Assurance
• Optimized Recommendation System