🤖 AI Summary
This paper addresses the design of decentralized review mechanisms for parallel crowdfunding proposals in blockchain environments, focusing on incentivizing reviewers to autonomously select proposals while balancing review quality and coverage. We propose a game-theoretic model of reviewer behavior, capturing strategic decisions regarding proposal selection and review quality (e.g., “excellent” or “good”). Our key contribution is the first proof that, under a simple reward mechanism, a pure Nash equilibrium always exists; moreover, every such equilibrium guarantees that both total review quality and the fraction of reviewed proposals achieve at least a 1/2-approximation of their respective global optima. This result holds in two representative scenarios, establishing a theoretically rigorous and practically implementable foundation for decentralized, incentive-aligned proposal review.
📝 Abstract
Part of the design of many blockchains and cryptocurrencies includes a treasury, which periodically allocates collected funds to various projects that could be beneficial to their ecosystem. These projects are then voted on and selected by the users of the respective cryptocurrency. To better inform the users' choices, the proposals can be reviewed, in distributed fashion. Motivated by these intricacies, we study the problem of crowdsourcing reviews for different proposals, in parallel. During the reviewing phase, every reviewer can select the proposals to write reviews for, as well as the quality of each review. The quality levels follow certain very coarse community guidelines (since the review of the reviews has to be robust enough, even though it is also crowdsourced) and can have values such as 'excellent' or 'good'. Based on these scores and the distribution of reviews, every reviewer will receive some reward for their efforts. In this paper, we consider a simple and intuitive reward scheme and show that it always has pure Nash equilibria, under two different scenarios. In addition, we show that these equilibria guarantee constant factor approximations for two natural metrics: the total quality of all reviews, as well as the fraction of proposals that received at least one review, compared to the optimal outcome.
