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Supporting knowledge elicitation and consensus building for dempster‐shafer decision models
Author(s) -
OseiBryson KwekuMuata
Publication year - 2003
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10078
Subject(s) - expert elicitation , dempster–shafer theory , computer science , process (computing) , artificial intelligence , group decision making , expert opinion , knowledge management , nominal group , action (physics) , management science , psychology , mathematics , engineering , social psychology , medicine , statistics , intensive care medicine , operating system , linguistics , philosophy , physics , quantum mechanics
Many decision models that are based on Dempster‐Shafer belief functions involve the elicitation of subjective belief data from a group of experts based on qualitative preferences. Given that a major reason for using a group is the assumption that the combined group judgment is likely superior to individual judgment, then the issues of synthesis and consensus assessment and consensus building become important. In this article we present an integrated structured, noninvasive action learning knowledge elicitation process for eliciting from a group of experts the basic probability assignments that are required by Dempster‐Shafer theory (DST)–based expert systems. This process accommodates the expert's uncertainty, identifies inconsistencies in the expert's opinion, takes advantage of the potential benefits of group work while mitigating against the negative effects, and addresses the issues of consensus assessment and consensus building. © 2003 Wiley Periodicals, Inc.