Can Fuzzy Logic Bring Complex Problems into Focus? Modeling Imprecise Factors in Environmental Policy

Introduction You and a friend walk outside on an April morning. You announce that the weather is “mild”. Your friend declares it “cold”. Who is wrong? Or are you both right? We all recognize that language can be imprecise and that words such as cold, hot, or mild do not have well-defined boundaries. In 1965, Lotfi Zadeh introduced fuzzy logic as a means of processing data by extending classical set theory to handle partial membership1. Classical set theory deals with sets that are “crisp” in the sense that members are either in or out according to rules of binary logic. For example the apple in the basket is Red OR Not Red (binary logic). Some of the apples could be categorized as Red AND Not Red (fuzzy logic). Many of the concepts that we deal with in everyday life and in fields such environmental health involve factors that defy classification into “crisp” sets—safe, harmful, acceptable, unacceptable, etc. A classic example is when a regulator, who after she carefully explains the result of a detailed quantitative risk assessment to a community group is then asked “But are we safe?” In this case, “safe,” defies crisp classification because it is a multivariate state with gradations, that very among different individuals and groups.