Computational Intelligence for Risk and Disaster Management

In this paper we provide a computational intelligence methodology for risk and disaster management using attitudinal and fuzzy modeling. We provide a paradigm representing the relationship among threats, events, control alternatives and losses. In evaluating a control alternative we look beyond the traditional expected value as a summary measure of expectation and the traditional variance as a measure of dispersion. We introduce a new measure of dispersion which incorporates the decision maker's attitude. We present a fuzzy model to use this attitudinal variance in conjunction with the attitudinal expected value in order to assess the overall value of control alternatives.