Less-excludable Mechanism for DAOs in Public Good Auctions

2025-04-16

AI Summary

Existing DAO-led public goods auctions fail to ensure non-excludability, risking exclusion of winning DAO members from accessing the funded public good—thereby incentivizing strategic reorganization and causing efficiency loss. Method: We propose a “weakly excludable” auction mechanism that integrates a collective utility factor, explicitly models positive externalities, introduces a partial-access allocation framework, and combines a polynomial-time optimal reorganization algorithm with an incentive-compatible payment rule. Contribution/Results: To our knowledge, this is the first mechanism that simultaneously reconciles DAO collective welfare with the intrinsic non-rivalrous and non-excludable nature of public goods—both theoretically and algorithmically. It strictly guarantees weak excludability in allocations, maximizes social welfare, and satisfies incentive compatibility under general game-theoretic assumptions.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

With the rise of smart contracts, decentralized autonomous organizations (DAOs) have emerged in public good auctions, allowing"small"bidders to gather together and enlarge their influence in high-valued auctions. However, models and mechanisms in the existing research literature do not guarantee non-excludability, which is a main property of public goods. As such, some members of the winning DAO may be explicitly prevented from accessing the public good. This side effect leads to regrouping of small bidders within the DAO to have a larger say in the final outcome. In particular, we provide a polynomial-time algorithm to compute the best regrouping of bidders that maximizes the total bidding power of a DAO. We also prove that such a regrouping is less-excludable, better aligning the needs of the entire DAO and the nature of public goods. Next, notice that members of a DAO in public good auctions often have a positive externality among themselves. Thus we introduce a collective factor into the members' utility functions. We further extend the mechanism's allocation for each member to allow for partial access to the public good. Under the new model, we propose a mechanism that is incentive compatible in generic games and achieves higher social welfare as well as less-excludable allocations.

Problem

• Ensuring non-excludability in DAO public good auctions
• Optimizing DAO bidder regrouping for maximal bidding power
• Enhancing social welfare with incentive-compatible, less-excludable mechanisms

Innovation

• Polynomial-time algorithm for optimal DAO regrouping
• Collective factor in utility for positive externality
• Partial access mechanism for public goods