Description: A fuzzy random variable [FRV] has been conceptualized as a vague perception of a crisp but unobservable random variable [RV] and also as a random fuzzy set. Since actuaries seem receptive to these notions of FRV, and there are sources of uncertainty that RVs can not accommodate, but FRVs can, one would expect FRVs to be a component of the actuarial arsenal, but generally this is not the case. Assuming that the reason for this is that potential users are not sufficiently familiar with FRV methodology, the purpose of this article is to help alleviate this situation by presenting a FRV primer.

Description: The purpose of this article is to revise the fuzzy regression portions of earlier studies and to discuss issues related to the Tanaka approach, including a consideration of fuzzy least-squares regression models. From Actuarial Research Clearing House, 2006.

Description: Recent articles addressed the subject of fuzzy regression [FR] and the term structure of interest rates [TSIR]. Their approach relied on possibilistic regression. Although possibilistic regression has been used in many applications, it has a number of limitations, not the least of which is its nebulous relation to the least-squares concept. As an alternative to possibilistic regression, this paper uses fuzzy least square regression to investigate the TSIR.

Description: This paper examines the models underlying three of the most influential FAHP articles.

Description: A mathematical framework for benefits and choices must be created, in order to model flexible benefit selection. This paper creates such a framework by defining benefit plans as reimbursement functions. These are then used with a defined choice function to calculate the cost deviation due to selection. From Actuarial Research Clearing House 1992, VOL. 1.

Description: This 1978 session on classification models included 1. models demonstrating the effect of a classification system superimposed on a merit rating system and 2. models demonstrating the subsidization of poorer risks.

Description: Presented at October 1986 Annual Meeting. This session discussion is about the use of simulation, or Monte Carlo, techniques to analyze complex insurance models such as estimating reserves, solvency considerations, and estimation of mortality and distribution of aggregate losses with the aid of a computer program

Description: This paper gives an overview of fuzzy optimization, presents applications of fuzzy optimization and investigates ways to extend the methodology to risk assessment.

Description: This abstract describes a paper that explores the answers to the questions of: 1. How is each view of FRVs conceptualized? 2. What are the differences and similarities between the metrics for each of these views? 3. How are the metrics for these three views implemented?

Description: Genetic algorithms [GAs] and neural networks [NNs] are currently being used in actuarial and financial modeling. Nonetheless, while there is a general awareness of the nature of these techniques, there is often only vague familiarity with the details of how they are implemented. This paper is intended to help alleviate this situation. Its purpose is to present an overview of GAs and NNs, which includes an explanation of what they are, how they work, and an example of how they are implemented.