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

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  • Srivastava, Rajendra P.

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.

Suggested Citation

  • Srivastava, Rajendra P., 2011. "An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 126-135.
    • Handle: RePEc:eee:ijoais:v:12:y:2011:i:2:p:126-135
    DOI: 10.1016/j.accinf.2010.12.003
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    References listed on IDEAS

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    1. Rajendra P. Srivastava & Theodore J. Mock & Jerry L. Turner, 2009. "Bayesian Fraud Risk Formula for Financial Statement Audits," Abacus, Accounting Foundation, University of Sydney, vol. 45(1), pages 66-87, March.
    2. Mock, Theodore J. & Sun, Lili & Srivastava, Rajendra P. & Vasarhelyi, Miklos, 2009. "An evidential reasoning approach to Sarbanes-Oxley mandated internal control risk assessment," International Journal of Accounting Information Systems, Elsevier, vol. 10(2), pages 65-78.
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    Cited by:

    1. Adi Masli & Matthew G. Sherwood & Rajendra P. Srivastava, 2018. "Attributes and Structure of an Effective Board of Directors: A Theoretical Investigation," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 485-523, December.
    2. Desai, Vikram & Bucaro, Anthony C. & Kim, Joung W. & Srivastava, Rajendra & Desai, Renu, 2023. "Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    3. Shi Qiu & Yuansheng Luo & Hongwei Guo, 2021. "Multisource evidence theory‐based fraud risk assessment of China's listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1524-1539, December.
    4. Yangyan Zeng & Xiangzhi Zhang & Wenzhi Cao & Jilin Deng & Hao Zeng, 2024. "An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution," Sustainability, MDPI, vol. 16(20), pages 1-16, October.
    5. Hironori Fukukawa & Theodore J. Mock & Rajendra P. Srivastava, 2014. "Assessing the Risk of Fraud at Olympus and Identifying an Effective Audit Plan," The Japanese Accounting Review, Research Institute for Economics & Business Administration, Kobe University, vol. 4, pages 1-25, December.

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