Funding Public Goods with Expert Advice in Blockchain System

Abstract

Public goods projects, including open source technology, client development, and blockchain knowledge education, play an important role in the flourishing blockchain ecosystem. Accordingly, decision making for public goods funding is a key issue in the studies of the blockchain ecosystem. This work develops a human oracle protocol approach, involved with public goods projects, experts, and funders, as a solution to the public goods investment problem on blockchain. In our human oracle, funders contribute their investments, which are stored in a funding pool. Experts provide investment advice on public goods projects based on their experience. Decisions made by the human oracle on the amount of support from the funding pool are based on experts’ reputation. The reputation of each expert is updated by the performance of the project’s implementation in comparison to her advice. That is, better investment performance brings a higher reputation. Besides being applied to static model, our human oracle can also be extended to accommodate dynamic settings, in which the experts might leave or join the decision-making process. We introduce a regret bound to measure the effectiveness of our human oracle. Theoretically, we prove an upper regret bound for both static and dynamic models, and prove its tightness with an asymptotically equal lower bound. Empirically, we show that our oracle’s investment decision is close to the optimal investment in hindsight.