International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

The basic paradigm for decision making under uncertainty is introduced. A methodology is suggested for the calculation of the variance associated with each of the alternatives in the case when the uncertainty is not necessarily of a probabilistic nature.

Modern financial institutions require sophisticated risk assessment tools to integrate human expertise and historical data in a market that is changing and broadening qualitatively, quantitatively, and geographically. The need is especially acute in newly developed countries where expertise and data are scarce, and knowledge bases and assumptions imported from the West may be of limited applicability.

Second order logical models can be a valuable tool in such situations. They integrate the robustness of neural or statistical modeling of data, the perspicuity of logical rule induction, and the experience and understanding of skilled human experts. The approach is illustrated in the context of risk assessment in the Korean surety insurance industry.

Predicting foreign exchange rates and stock market indices have been a well researched topic in the field of financial engineering. However, most methods suffer from serious drawback due to the inherent uncertainty in the data acquisition process. Here, we have analyzed the very nature of the time series data from a pure dynamic system point of view and explored the deterministic chaotic characteristic in it. In this research, the concept of chaos has been analyzed thoroughly and the relationships among chaos, stability and order have been explained with respect to the concept of time. A method of predicting time series data based on deterministic dynamically system has been presented in this monograph. The present research revolves around the concepts of embedding and fuzzy reconstruction. In this regard, the necessary and sufficient condition for this reconstruction of the state space of the dynamic system in a multi-dimensional Euclidean space has been substantiated in accordance to Theory of embedding. Finally, a fuzzy reconstruction method based on fuzzy multiple regression analysis method has been used to predict the foreign exchange rates with accuracy.

In this paper, we present a fuzzy dynamic programming procedure for long term risk management. This approach is designed to provide insights on trade-offs between potential risks and rewards, the dynamics of interacting economic factors, and the feasibility of corporate goals over a long term planning horizon. This approach is applicable to many long term planning problems involving selection from a number of alternatives, when the decision parameters are imprecise and absolute requirements and decision thresholds can not be specified. A few examples of problems of this type include: portfolio optimization, risk management, evaluation of securities, liquidity management, and asset/liability management (which is given particular emphasis in this paper). Our formulation provides a computationally efficient and natural representation of the decision trade-offs inherent in many long term money management problems. This, in turn, facilitates the exploration and analysis of many alternative investment strategies under many possible future scenarios.

In this issues, we continue to publish abstracts and reviews of recents papers on interval methods in knowledge representation. In knowledge representation, intervals are used for two main purposes:

• to describe durations of events; and

• to describe uncertainty of measurement results and expert estimates of different quantities; often, we do not know the exact value of a quantity, but we know its lower and upper bounds (e.g., we may not know the exact value of someone's weight, but we may know that this weight is in between 140 and 160 pounds).

An important case of this uncertainty occurs in knowledge elicitation, when we ask experts to numerically estimate their degrees of belief in their own statements; in this case, it is often difficult for an expert to estimate this degree of belief precisely, but an expert can often provide us with an interval of possible values.

The reviews are collected by Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA, email vladik@cs.utep.edu