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Our study provides a survey on how existing stablecoins-- cryptocurrencies aiming at price stabilization-- peg their value to other assets, from the perspective of Decentralized Payment Systems (DPSs). This attempt is important because there has been no preceding surveys focusing on the stablecoin as DPSs, i.e., the one aiming at not only price stabilization but also decentralization. Specifically, we first classified existing stablecoins into four types according to their collaterals (fiat, commodity, crypto, and non-collateralized) and pointed out the high potential of non-collateralized stablecoins as DPSs; then, we further classified existing non-collateralized stablecoins into two types according to their intervention layers (protocol, application) and confirmed details of their representative mechanisms. Utilizing concepts such as Quantity Theory of Money (QTM), Tobin tax, and speculative attack, our survey revealed the status quo where, despite the high potential of non-collateralized stablecoins, they have no standard mechanism to achieve the stablecoin for practical DPSs., Comment: 15 pages, IIAI AAI 2019 accepted

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)., Comment: 16 pages, 5 figures

There have been various studies analyzing public sentiment after a large-scale disaster. However, few studies have focused on the relationship between public sentiment on social media and its results on people's activities in the real world. In this paper, we conduct a long-term sentiment analysis after the Great East Japan Earthquake and Tsunami of 2011 using Facebook Pages with the aim of investigating the correlation between public sentiment and people's actual needs in areas damaged by water disasters. In addition, we try to analyze whether different types of disaster-related communication created different kinds of relationships on people's activities in the physical world. Our analysis reveals that sentiment of geo-info-related communication, which might be affected by sentiment inside a damaged area, had a positive correlation with the prices of used cars in the damaged area. On the other hand, the sentiment of disaster-interest-based-communication, which might be affected more by people who were interested in the disaster, but were outside the damaged area, had a negative correlation with the prices of used cars. The result could be interpreted to mean that when people begin to recover, used-car prices rise because they become more positive in their sentiment. This study suggests that, for long-term disaster-recovery analysis, we need to consider the different characteristics of online communication posted by locals directly affected by the disaster and non-locals not directly affected by the disaster.

We deduce the asymptotic error distribution of the Euler method for the nonlinear filtering problem with continuous-time observations. Previous works by several authors have shown that the error structure of the method is characterized by conditional expectations of some functionals of multiple stochastic integrals. Our main result is a proof of the stable convergence of a sequence of such conditional expectations, using the technique of martingale limit theorems in the spirit of Jacod., Comment: major revision: 23 pages, the title is modified, gaps in the proofs are filled

The discrete-time approximation for nonlinear filtering problems is related to both of strong and weak approximations of stochastic differential equations. In this paper, we propose a new method of proof for the convergence of approximate nonlinear filter analyzed by Jean Picard (1984), and show a more general result than the original one. For the proof, we develop an analysis of Hilbert space valued functionals on Wiener space., Comment: 15 pages

Motivated by weak convergence results in the paper of Takahashi and Yoshida (2005), we show strong convergence for an accelerated Euler-Maruyama scheme applied to perturbed stochastic differential equations. The Milstein scheme with the same acceleration is also discussed as an extended result. The theoretical results can be applied to analyzing the multi-level Monte Carlo method originally developed by M.B. Giles. Several numerical experiments for the SABR stochastic volatility model are presented in order to confirm the efficiency of the schemes., Comment: 21 pages, 6 figures

Weak approximations have been developed to calculate the expectation value of functionals of stochastic differential equations, and various numerical discretization schemes (Euler, Milshtein) have been studied by many authors. We present a general framework based on semigroup expansions for the construction of higher-order discretization schemes and analyze its rate of convergence. We also apply it to approximate general L\'{e}vy driven stochastic differential equations., Comment: Published in at http://dx.doi.org/10.1214/08-AAP568 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)