Journal Article

Mean-Risk Trade-Offs in Inductive Expert Systems

Mannino, Michael V, Mookerjee, Vijay S

2000

Overview

Notably absent in previous research on inductive expert systems is the study of meanrisk trade-offs. Such trade-offs may be significant when there are asymmetries such as unequal classification costs, and uncertainties in classification and information acquisition costs. The objective of this research is to developmodels to evaluate mean-risk trade-offs in value-based inductive approaches. We develop a combined mean-risk measure and incorporate it into the Risk-Based induction algorithm. The mean-risk measure has desirable theoretical properties (consistency and separability) and is supported by empirical results on decision making under risk. Simulation results using the Risk-Based algorithm demonstrate: (i) an order of magnitude performance difference between mean-based and risk-based algorithms and (ii) an increase in the performance difference between these algorithms as either risk aversion, uncertainty, or asymmetry increases given modest thresholds of the other two factors.

• Publisher: INFORMS,The Institute for Operations Research and the Management Sciences (INFORMS),Institute for Operations Research and the Management Sciences
• Subject: Algorithms / Cost structure / Datasets / Expert systems / Financial risk / inductive expert systems / Information classification / Information storage and retrieval systems / Machine learning / Random variables / Risk aversion / risk aversion in expert systems / Studies / Total costs / value-based system design