Y. Sawaragi

Estimation of nitrogen dioxide concentrations in the vicinity of a roadway by optimal filtering theory

Abstract Distributed filtering theory and maximum likelihood estimation are applied to the joint estimation of atmospheric diffusion parameters and of nitrogen dioxide (NO 2 ) concentration levels in the vicinity of a heavily travelled roadway in Tokushima, Japan. An optimal estimator for the air pollution problem is derived in the case of time-averaged and pointwise observations. Based on the NO 2 measurement data in the study area, traffic volumes and weather conditions, an algorithm is developed that can be used in conjunction with urban air pollution models to estimate the spatial and temporal distribution of airborne NO 2 levels.

An application of the stochastic automaton to the investment game

Abstract An application of the stochastic automaton to the investment game is considered. It is shown that the use of the stochastic automaton with learning properties is an efficient method for the investment game.

On Methodology for Group Decision Support

This paper studies a group decision making process from a general point of view. The former part of the process is the clarification of the basic elements in a group decision situation and the latter part is the analysis of the situation followed by the modification of it. It is shown that several measures appear and are utilized in the process. Furthermore, a method for group decision support — the extended contributive rule method — which contains a power of aggregation as a parameter, is explained in comparison with the concept of partial comparability of individual utilities by Sen.

A Trial Towards Group Decisions in Structuring Environmental Science

The special research project “Environmental Science”, which is sponsored by the Education Ministry of Japan, has been playing an active part in the research on the environmental management since about 1970.

Parametric Preference Orderings in Group Decision Making

Abstract This paper considers parametric preference orderings in group decision making. Some families of parametric group preference orderings are defined on the basis of three wellknown preference aggregation rules; the sum of individual utilities, the Rawls maximin rule and the Nash bargaining solution function. They have some desirable properties such as monotonicity along with meaningful parameters, and therefore help the group of decision makers understand the present situation objectively and proceed the decision making process smoothly. Thus considering the parametric preference orderings suggested in this paper is an effective approach to group decision making or consensus formation.

NEW TRENDS IN SYSTEMS APPROACH BY THE INTERACTIVE USE OF MICROCOMPUTERS

Abstract Placting stress on 1) the use of heuristics or experiences of an expert and 2) the interactive use of microcomputers or personal computers, some of the new trends are reviewed and discussed in systems approach especially in the field of interactive modeling of large scale systems, system failure diagnosis by use of knowledge engineering techniques, multiple criteria decision making and gaming system for complex problems

Stochastic Dominance for Decision Problems with Multiple Attributes and/or Multiple Decision-Makers

Abstract In decision making under uncertainty, the best alternative may be obtained by maximizing the expected utility. To this end, many researchers have been paying much attention to the identification of utility functions. Inpractical situations, however, there seem to be not a few possible cases such as group decisions where it is difficult to identify utility functions. When only partial knowledge of utility functions is available, one way for ranking alternatives is to make the most of information on probability distributions of alternatives. The theory of stochastic dominance has been developed for this purpose. Although the use of stochastic dominance may seldom lead to the final decision in general, it may be useful for narrowing down the alternetive set. In this paper, several kinds of stochastic dominance will first be surveyed and discussed. Then their potential effectiveness to risk management will be verified along with some examples.

Air Quality Mathematical Modeling and Estimation

Abstract The need of maintaining a satisfactory air quality level has led to develop mathematical models for simulating atmospheric pollutant dispersion to be used in conjunction with traditional approaches such as field experiments and physical models. This paper discusses an air quality mathematical modeling and estimation problem by using a distributed parameter estimation theory. This problem has been dealt with a mixed deterministic-stochastic formulation based on the atmospheric diffusion equation and the Kalman filtering technique. Results of real data processing to estimate the pollution evels of sulphur dioxide and nitrogen dioxide in Tokushima, Japan, are discussed.

Surveillance Network Design for Air Pollution Control

Abstract An interactive optimization method for designing an air quality monitoring network in an urban area is proposed. A constrained Ward method clustering is used to determine the number and location of monitoring stations and their representative areas under averaged meteorological condition. Participation of human in the design process is allowed in such a way that one can modify constraints by taking account of economic and physical conditions as well as inaccuracy of simulation models. The goal is to determine the representative area that can be covered by the individual station under every possible atmospheric condition. This can be done approximately by identifying the membership functions for several typical conditions. An application to the NOx monitoring network in Kyoto, Japan is presented.

An Improvement of the Adaptive Transformation Network Method by a New Global Minimization Algorithm and its Application to Forecasts of Air Pollutions

Abstract This paper deals with an improvement of the Adaptive Transformation Network Method which is known as a useful tool for the prediction of air pollution. The improvement consists in using a new hybrid algorithm of minimization, combining the DFP method with the random optimization method. Thanks to the hybrid algorithm, we are able to find out an appropriate parameter vector included in the transformation network in a small number of steps. Good forecasts of SO 2 density in Tokyo are given by the improved Adaptive Transformation Network Method.