Campus Units

Supply Chain and Information Systems

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2016

Journal or Book Title

INFORMS Journal on Computing

Volume

29

Issue

2

First Page

268

Last Page

286

DOI

10.1287/ijoc.2016.0728

Abstract

Input distortion is a common problem faced by expert systems, particularly those deployed with a Web interface. In this study, we develop novel methods to distinguish liars from truth-tellers, and redesign rule-based expert systems to address such a problem. The four proposed methods are termed split tree (ST), consolidated tree (CT), value-based split tree (VST), and value-based consolidated tree (VCT), respectively. Among them, ST and CT aim to increase an expert system’s accuracy of recommendations, and VST and VCT attempt to reduce the misclassification cost resulting from incorrect recommendations. We observe that ST and VST are less efficient than CT and VCT in that ST and VST always require selected attribute values to be verified, whereas CT and VCT do not require value verification under certain input scenarios. We conduct experiments to compare the performances of the four proposed methods and two existing methods, i.e., the traditional true tree (TT) method that ignores input distortion and the knowledge modification (KM) method proposed in prior research. The results show that CT and ST consistently rank first and second, respectively, in maximizing the recommendation accuracy, and VCT and VST always lead to the lowest and second lowest misclassification cost. Therefore, CT and VCT should be the methods of choice in dealing with users’ lying behaviors. Furthermore, we find that KM is outperformed by not only the four proposed methods, but sometimes even by the TT method. This result further confirms the advantage necessity of differentiating liars from truth-tellers when both types of users exist in the population.

Comments

This is an accepted manuscript published as 3. Cai , Y., Z. Jiang, V. Mookerjee. "How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost." INFORMS Journal on Computing, 2017; 29(2); 268-286. 10.1287/ijoc.2016.0728. Posted with permission.

Copyright Owner

INFORMS

Language

en

File Format

application/pdf