Computational systems for qualitative economics (Q1891388)

When dealing with partially known systems there are several approaches one might take in coping with incomplete and uncertain knowledge. Quantitative methods have the advantage of producing precise results, but frequently one lacks confidence in the appropriateness of the underlying model. Precise answers, on the other hand, are often not of primary interest when conducting economic studies; qualitative information like signs, ranges and directions of change of system variables may be sufficient for satisfactorily explaining and predicting economic systems. Because qualitative economic analysis had been essentially developed prior to the era of modern computer technology, it was limited by the absence of computational power. Recent research in qualitative reasoning, a new field that has emerged from artificial intelligence, has developed a variety of representational languages and inference mechanisms for qualitative modeling. The goals of this paper are to integrate the developments in artificial intelligence into the qualitative economics field, to discuss the representational issues of qualitative modeling, and to present a new computational approach for analyzing qualitative economic systems.