Relative uncertainty and evidence sets: A constructivist framework

Several measures of uncertainty, in its various forms of nonspecificity, conflict, and fuzziness, valid both in finite and infinite domains are investigated. It is argued that dimensionless measures, relating uncertainty situations to the information content of their respective universal sets, can capture uncertainty efficiently both in finite and infinite domains. These measures are also considered more intuitive. To establish them, a more general approach to uncertainty measures is developed. After this, the utilization of these measures is exemplified in the measurement of the uncertainty content of evidence sets. These interval-based set structures, defined through evidence theory, are shown to possess ideal characteristics for the modeling of human cognitive categorization processes, within a constructivist framework.