Evidential Reasoning Approach

Evidential Reasoning Approach

In decision theory, the Evidential Reasoning approach (ER), is a generic evidence-based Multi-Criteria Decision Analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties including ignorance and randomness. It has been used to support various decision analysis, assessment and evaluation activities such as environmental impact assessment [Y. M. Wang, J. B. Yang and D. L. Xu, “Environmental Impact Assessment Using the Evidential Reasoning Approach”, European Journal of Operational Research, Vol.174, No.3, pp.1885-1913, 2006.] and organizational self-assessment [C. H. R. Siow, J. B. Yang and B. G. Dale, “A new modelling framework for organisational self-assessment: development and application”, Quality Management Journal, Vol.8, No.4, pp.34-47, 2001.] based on a range of quality models.

Overview

The Evidential Reasoning approach is recently developed on the basis of decision theory in particular utility theory, [Keeney, R.L. and Raiffa, H. (1976), Decisions with Multiple Objectives, Cambridge University Press.] artificial intelligence in particular the theory of evidence [Shafer, G.A. (1976), Mathematical Theory of Evidence, Princeton University Press.] , statistical analysis and computer technology. It uses a belief structure to model an assessment with uncertainty, a belief decision matrix to represent an MCDA problem under uncertainty, evidential reasoning algorithms [Yang, J.B. and Xu, D.L. (2002), On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty, IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans Vol. 32, 289-304.] to aggregate criteria for generating distributed assessments, and the concepts of the belief and plausibility functions to generate a utility interval for measuring the degree of ignorance. A conventional decision matrix used for modeling an MCDA problem is a special case of a belief decision matrix [D. L. Xu, J. B. Yang and Y. M. Wang, “The ER approach for multi-attribute decision analysis under interval uncertainties”, European Journal of Operational Research, Vol.174, No.3, pp.1914-1943, 2006.] .

The use of belief decision matrices for MCDA problem modelling in the ER approach results in the following features:
# An assessment of an option can be more reliably and realistically represented by a belief decision matrix than by a conventional decision matrix.
# It accepts data of different formats with various types of uncertainties as inputs, such as single numerical values, probability distribution, and subjective judgments with belief degrees.
# It allows all available information embedded in different data formats, including qualitative and incomplete data, to be maximally incorporated in assessment and decision making processes.
# It allows assessment outcomes to be represented more informatively.

The ER approach is implemented in a software tool called [http://www.e-ids.co.uk Intelligent Decision System] .

References

External links

* [http://www.e-ids.co.uk Intelligent Decision System] Software for evidence based MCDA, assessments and evaluations
* [http://www.terry.uga.edu/mcdm/ International Society on Multiple Criteria Decision Making]


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