scholarly journals Approximation algorithms and decision making in the Dempster-Shafer theory of evidence — An empirical study

1997 ◽  
Vol 17 (2-3) ◽  
pp. 217-237 ◽  
Author(s):  
Mathias Bauer
Keyword(s):  
Decision Making ◽  
Approximation Algorithms ◽  
Empirical Study ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence

An extension of case-based decision theory by using Dempster–Shafer theory of evidence

2018 ◽  
Vol 13 (1) ◽  
pp. 179-189 ◽  
Author(s):  
Fei Du ◽  
Feiyan Liu
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Future Research ◽  
Traditional Concept ◽  
Content Type ◽  
Dempster Shafer Theory ◽  
Computational Procedures ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Case Based

Purpose This study aims to propose a new decision-making method by integrating case-based decision theory and the Dempster–Shafer theory of evidence. Design/methodology/approach The study developed the entire computational procedures for the proposed method and used a numerical example to illustrate its method. Findings The results show that not only the own experiences of the decision-maker but also the opinions of other persons contribute to the selection. Case-based decision theory provides a fundamental technique for the decision-making procedure, and the Dempster–Shafer theory of evidence offers support to deal with the different sources of decision information. Research limitations/implications In case-based decision theory, the utility is a subjective concept, which cannot be measured easily in numbers. Thus, future research should seek a new method to replace the utility. In addition, how to assess the importance of different persons’ experiences and opinions is an important component of this method. Originality/value The contributions of the paper are mainly reflected in three aspects. The first is to expand the traditional concept of “case” of case-based decision theory to multiple sources of cases, which include not only the decision-maker’s own experiences but also other persons’ opinions. The second is to provide a decision-making framework by integrating case-based decision theory and the Dempster–Shafer theory of evidence. The third is to develop the entire computational procedures for the proposed method.

LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE

2010 ◽  
Vol 18 (03) ◽  
pp. 287-304 ◽  
Author(s):  
J. M. MERIGÓ ◽  
M. CASANOVAS ◽  
L. MARTÍNEZ
Keyword(s):  
Decision Making ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
New Approach ◽  
Dempster Shafer Theory ◽  
Wide Range ◽  
Different Types ◽  
Linguistic Decision Making ◽  
Shafer Theory ◽  
Theory Of Evidence

In this paper, we develop a new approach for decision making with Dempster-Shafer theory of evidence by using linguistic information. We suggest the use of different types of linguistic aggregation operators in the model. We then obtain as a result, the belief structure — linguistic ordered weighted averaging (BS-LOWA), the BS — linguistic hybrid averaging (BS-LHA) and a wide range of particular cases. Some of their main properties are studied. Finally, we provide an illustrative example that shows the different results obtained by using different types of linguistic aggregation operators in the new approach.

REASONING UNDER UNCERTAINTY FOR SHILL DETECTION IN ONLINE AUCTIONS USING DEMPSTER–SHAFER THEORY

2010 ◽  
Vol 20 (07) ◽  
pp. 943-973 ◽  
Author(s):  
FEI DONG ◽  
SOL M. SHATZ ◽  
HAIPING XU
Keyword(s):  
Decision Making ◽  
Online Auctions ◽  
Single Source ◽  
Reasoning Under Uncertainty ◽  
Multiple Sources ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Positive Results

This paper describes the design of a decision support system for shill detection in online auctions. To assist decision making, each bidder is associated with a type of certification, namely shill, shill suspect, or trusted bidder, at the end of each auction's bidding cycle. The certification level is determined on the basis of a bidder's bidding behaviors including shilling behaviors and normal bidding behaviors, and thus fraudulent bidders can be identified. In this paper, we focus on representing knowledge about bidders from different aspects in online auctions, and reasoning on bidders' trustworthiness under uncertainties using Dempster–Shafer theory of evidence. To demonstrate the feasibility of our approach, we provide a case study using real auction data from eBay. The analysis results show that our approach can be used to detect shills effectively and efficiently. By applying Dempster–Shafer theory to combine multiple sources of evidence for shill detection, the proposed approach can significantly reduce the number of false positive results in comparison to approaches using a single source of evidence.

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