This paper proposed a new approach that integrated an artificial market simulation and text-mining with real information. In this approach, economic trends were extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. The simulation could support users' action to the actual market. This approach was used for the decision of exchange rate policy and suggested that the operation by intervention was effective for the stabilization of the yen-dollar rate in 1995. Our simulation revealed that the action rule proposed by our system could reduce over 70% of rate fluctuation. This approach can offer a useful social simulation as a tool to users.

We constructed an evaluation system of the self-impact in a financial market using an artificial market and text-mining technology. Economic trends were first extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. Our simulation revealed that an operation by intervention could reduce over 70 % of rate fluctuation in 1995. By the simulation results, the system was able to help for its user to find the exchange policy which can stabilize the yen-dollar rate.

This paper analyzes structured P2P systems where peers choose both their interaction mode, i.e., how they process incoming queries, and additional contacts in the network autonomously. Since additional contacts incur additional costs, a new kind of free riding behavior, namely having only few contacts, comes into the fray. We refer to it as deliberately poor connectedness (dpc). In this paper, we show that dpc is dominant in many situations. This leads to networks with a low degree of connectivity and a higher overall forwarding load than necessary. We then propose an incentive mechanism against dpc and demonstrate its effectiveness using a formal analysis and experiments.

This chapter presents discussion of word weighting algorithms in user modelling and adaptive information systems. We specifically address two types of user interest: (1) broad and consistent interest; and (2) narrow, spot interest. A user’s consistent interests can be modelled utilising the user’s information access history; a user’s spot interests can be determined based on that. We developed a word-weighting algorithm to measure the user’s spot interest. The information access history of a user is represented as a set of words. It is considered to be a user model. This method weights words in a document according to their relevancy to the user model. The relevancy is measured by the biases of co-occurrence, called the Interest Relevance Measure, between a word in a document and words in the user model. The future methodology of word weighting is described herein while demonstrating our approach.

With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of the latest DGMs: (1) deep neural networks are encapsulated by probability distributions and (2) models are designed and learned based on an objective function. Taking these features into account, we propose a new DGM library called Pixyz. We experimentally show that our library is faster than existing probabilistic modeling languages in learning simple DGMs and we show that our library can be used to implement complex DGMs in a simple and concise manner, which is difficult to do with existing libraries.