A decision support tool coupling a causal model and a multi-objective genetic algorithm

Abstract

The knowledge-driven causal models, implementing some inferential techniques, can prove useful in the assessment of effects of actions in contexts with complex probabilistic chains. Such exploratory tools can thus help in "forevisioning" of future scenarios, but frequently the inverse analysis is required, that is to say, given a desirable future scenario, to discover the "best" set of actions. This paper explores a case of such "future-retrovisioning", coupling a causal model with a multi-objective genetic algorithm. We show how a genetic algorithm is able to solve the strategy-selection problem, assisting the decision-maker in choosing an adequate strategy within the possibilities offered by the decision space. The paper outlines the general framework underlying an effective knowledge-based decision support system engineered as a software tool. © Springer-Verlag Berlin Heidelberg 2005.