New Mathematics and Natural Computation

We use techniques from fuzzy mathematics to develop metrics for measuring how well the US is achieving its overarching national security goal: to protect itself, its allies and its friends from both nuclear attack and coercive pressures by states possessing nuclear weapons. The metrics are linear equations assigning weights to each of the six components of the overarching goal. These weights are based on expert opinions. We determine the degree to which the experts consider certain goals as the most important. We conclude by examining the degree of agreement among experts.

This paper reports some novel approach on linguistic logic with our intention to realize CWW, Computing With Words, via a simple example which consists of only five words. As a by product, this simple example of the linguistic logical system may serve as a mathematical model, modeling the degree of truthfulness in daily usage. The five words set of a linguistic variable modeling the degree of truthfulness are; true, nearly true, undecided, nearly false and false. We subjectively choose trapezoidal fuzzy numbers as our linguistic truth values in order to model our linguistic logic system. Firstly, some natural operations and linguistic logic operators are defined to suit our objective of developing a closed linguistic variable set. Then the computation of linguistic truth values for this linguistic logical system is developed in order to facilitate us to perform the linguistic inferences. Properties of these natural operations can be derived accordingly. It is perhaps quite rewarding to see numerous linguistic truth relations defined on a single linguistic truth set and linguistic implications ended up with numerous linguistic truth tables. In addition, the linguistic inferences of generalized modus ponens and generalized tollens determined by linguistic compositional rules based on the linguistic truth relation and some natural operations are introduced. The simple examples of the linguistic inferences of the various generalized tautologies are illustrated. Finally, we have proved via a simple dictionary that a closed and self consistent linguistic logical system indeed can be constructed and it is possible to move a chunk of information as modeled by a fuzzy set to a higher level according to the theory of semiotics. These results have shown some promise in realizing the appealing theory of CWW.

In this paper, the author aims to examine the differences in perception concerning the anxiety toward the risk among three countries — Japan, the United States of America and China. The anxiety, in this case, is triggered by uncertainty. This paper also intends to clarify the effect of information to improve people's risk management targeted on the respondents of the Chinese population focusing on earthquake disasters. The social survey using questionnaire has been carried out in order to obtain the needed quantitative data for my research project. It is interesting to conclude that both respondents in China and in the United States tend to accept the impact of uncertainty better. They have shown somewhat lower level of anxiety toward nineteen items of the risks as compared with that of the Japanese respondents. The significant effects on information designed as a part of the risk management action plan as well as the living sufficiency safeguard are clearly observed.

The causes of risks are hard to identify if events occur temporarily and disappear before the occurrence of their observable effects. In the face of this hurdle, transient causal events of significant effects are desired to be explained, for the safety of human life. In this paper, Kamishibai KeyGraph, a variation of KeyGraph developed to deal with sequential data, is presented as a tool to explain the causality involving transient causes. This method is applied here for two data sets: (1) newspaper text on social events, and (2) data on earthquakes in Japan. The performance of this method is hard to evaluate quantitatively due to the nature of transient events, so we partially evaluate the qualitative scenarios interpreted subjectively from the visualized maps.

In this paper, we propose interactive and visual discovery method of hidden factors for accidents or incidents. Accidents or incidents tend to occur because of very small differences from an ideal situation. From the above viewpoint, we introduce scenario violation model and regard scenario violation as a gap from the proper scenario. Previously, we proposed abduction-based scenario violation determination strategy, but in this paper, we adopt the Kamishibai-KeyGraph to analyze multiple patterns at the same time. Then, we show a gap discovery procedure by using the Kamishibai-KeyGraph.

In order to visualize the financial situation and trend of a company, an experiment model of chance discovery has been proposed, which enables the stakeholders inside and outside the business to recognize the performance of management and realize the possibility of investment. Usually, the statistical analysis and prediction models are common ways to understand the overall financial trend. Apart from analyzing the public offering financial statements directly, some visualization methods are potential approaches for stakeholders to understand the financial status intuitively. Chance discovery is one of the visualization methods which may be suitable for being applied in the exploration of financial status and trend. Using the experiment model, the annual and serial KeyGraphs as well as the trend diagram and integrated map were generated to depict the overview of the successive five-year scenario. Finally, the visualization of financial trend was then attained in this study.

A method for visualizing discussion flow is proposed for analyzing the thread in KGBBS (KeyGraph-based BBS). It is known that group discussion is effective for chance discovery, in which information visualization plays an important role in providing participants with a material to that brainstorm various ideas, opinions, and interpretations. In order to enable online discussion while sharing the same visualized material, KGBBS is proposed based on KeyGraph, which is one of the typical visualization techniques in chance discovery. The aim of this paper is to utilize the result of discussion using KGBBS, by visualizing discussion flow of the thread in KGBBS. The method consists of 2 kinds of visualization techniques — a comment chain diagram to visualize the structure of comment chain, and a visual summary on KeyGraph to visualize concrete transition of topics through successive 2 or 3 comments. The proposed method is applied to actual discussion data, and the analysis results show the combination of both the visualization techniques, making it possible to analyze concrete transition of topics without reading comments in a thread.