Abstract: In this study, we aim to incorporate the expertise of anonymous curators into a token-curated registry (TCR), a decentralized recommender system for collecting a list of high-quality content. This registry is important, because previous studies on TCRs have not specifically focused on technical content, such as academic papers and patents, whose effective curation requires expertise in relevant fields. To measure expertise, curation in our model focuses on both the content and its citation relationships, for which curator assignment uses the Personalized PageRank (PPR) algorithm while reward computation uses a multi-task peer-prediction mechanism. Our proposed CitedTCR bridges the literature on network-based and token-based recommender systems and contributes to the autonomous development of an evolving citation graph for high-quality content. Moreover, we experimentally confirm the incentive for registration and curation in CitedTCR using the simplification of a one-to-one correspondence between users and content (nodes).
Abstract: This chapter critically investigates the application of blockchain technology for intellectual property management. To date, there have been relatively few critical discussions of the feasibility of utilising blockchain technology for this purpose, although much has been written, in media and industry sources, about the potential. Our aim, by contrast, is to examine possible limitations—and, subsequently, to suggest tentative solutions to the limitations we identify. Specifically, this chapter aims to examine the use of blockchain technology for intellectual property management from two perspectives: operation and implementation. We conclude that, while commentators often focus on technical characteristics of blockchain technology itself, it is the incentive design—which was fundamental to the original Bitcoin proposal—that is also critical to truly decentralised, and disintermediated, intellectual property management.
- 最近の論文がWeb工学の分野でBest Paper Awardを受賞した。
- 選択アルゴリズム+ピア予測法 の新たなメカニズムを提案した。
Abstract: Our study aims to strengthen truthfulness of the two-path mechanism: an information diffusion algorithm to find an influential node in non-cooperative directed acrylic graphs (DAGs). This subject is important because the two-path mechanism ensures only weak truthfulness (i.e., nodes are indifferent between reporting true or false out-edges), which restricts node selection accuracy. To enhance the mechanism, we employed an additional reward layer based on a multi-task peer prediction, where an informative equilibrium provides strictly higher rewards than any other equilibrium in virtually all cases (strong truthfulness). Rewards, which are derived from a comparison of each report, encourage a node to report true out-edges without affecting its own probability of being selected by the original two-path mechanism. We have also experimentally confirmed that our proposed strongly truthful two-path mechanism can sufficiently elicit true out-edges from each node.
【Best Paper Award】at the 20th International Conference on Information Integration and Web-based Applications & Services (iiWAS2018), November 19th to 21st, 2018, Yogyakarta: Grand Mercure Yogyakarta Adisucipto, pp.96-104.
Abstract: In this paper, we pointed the potential utility of peer prediction method to the existing consensus building in decentralized oracle systems where participants aim to verify the validity of input information to blockchain without relying on a trusted third party (TTP). This is important because, despite the recent expectation of implementing decentralized oracle systems, few discussions have dealt with the incentive design for their consensus building, much less the synergy with peer prediction method. Specifically, we mentioned the followings through the survey of preceding studies: (i) the current predominant method of staking that allows validators to bet the reward tokens has the limitations such as a vulnerability to strategic behavior and a lack of incentive to participate in the verification, (ii) these problems could be solved by peer prediction method which determines the amount of rewards based on the posterior probability distribution on the report of others updated by one’s own report. Peer prediction method can encourage validators to perform proper verification while supplementing the token-based rewards, and thereby can contribute to the realization of the mining mechanism based on subjective review instead of computational resources. On the other hand, several obstacles still remain to propose a practical incentive design, such as the fluctuation of token price that would prevent peer prediction from incentivizing proper verification.