An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives

Rajendra P. Srivastava

International Journal of Accounting Information Systems, 2011, vol. 12, issue 2, 126-135

Abstract: The main purpose of this article is to introduce the evidential reasoning approach, a research methodology, for decision making under uncertainty. Bayesian framework and Dempster–Shafer theory of belief functions are used to model uncertainties in the decision problem. We first introduce the basics of the DS theory and then discuss the evidential reasoning approach and related concepts. Next, we demonstrate how specific decision models can be developed from the basic evidential diagrams under the two frameworks. It is interesting to note that it is quite efficient to develop Bayesian models of the decision problems using the evidential reasoning approach compared to using the ladder diagram approach as used in the auditing literature. In addition, we compare the decision models developed in this paper with similar models developed in the literature.

Keywords: Evidential reasoning; Bayesian; Dempster–Shafer theory; Belief functions (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1467089510000904
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:12:y:2011:i:2:p:126-135

DOI: 10.1016/j.accinf.2010.12.003

Access Statistics for this article

International Journal of Accounting Information Systems is currently edited by S.V. Grabski

More articles in International Journal of Accounting Information Systems from Elsevier
Bibliographic data for series maintained by Catherine Liu ().