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.
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.
Abstract: This paper aims to propose an intellectual property management which can sufficiently incentivise to innovation without providing exclusive rights for creators. Specifically, our model applies citation network and rewarding structure employed in cryptocurrencies, and instead of appropriability mechanisms, creators in the model are compensated by the delegation of subsequent review. This system can improve the social welfare on existing systems by free and open access and less management cost as decentralized autonomous organization.
*This paper is now under the review.*
Abstract: Why only human-beings acquired complex cultural traits? In this research, we survey the theories on cumulative cultural evolution that has been dealing with this question. This is important because cultural evolution is so interdisciplinary a research field as not able to sufficiently systematize existing methods even though we focused only on cumulative and theoretical aspects. In order for terse classification, preceding researches were arranged according to two criteria chronological and methodological. As a result, the former depicted, as already pointed out by Horiuchi (2012), that the current theoretical approach is largely based on learning hypothesis and population hypothesis. On the other hand, the latter suggested the circumstances where methods are still miscellaneous and arbitrarily set to obtain predetermined conclusions. Therefore, this survey concludes it is necessary for agents’ learning and inheritance process to have the consistent assumptions supported by empirical studies. While there are potential constraints, working on this issue with reference to other disciplines would strongly contribute to the further development of theoretical research on cumulative cultural evolution.
Japan Association for Evolutionary Economics the 21th anniversary meeting at Kyoto University, (non-published).
Abstract: This paper presents a dynamics of cumulative culture by using the methodology of economic growth theory which similarly deals with the accumulation stemming from human activities. Two main results can be derived from the model we set. First, the steady-state value of cultural stock is not affected by its quality and evaluation. Second, the steady-state is almost stable in all transitional dynamics. These imply that only indirect intervention has the real effect on cultural stock which is especially related to acquisition cost, depreciation and creativity.
Journal of Information Studies, Interfaculty Initiative in Information Studies, The University of Tokyo, No.91.