Evidential Reasoning Approach

MAUT measures complete preferences rmder rmcertainty. However, because preferences may not always be completely specified (internal uncertainty), methods have been developed to deal with value intervals, qualitative estimations and incom-pletes in judgements. Examples of such methods are PRIME (Preference Ratios In Multi-attribute Evaluation) (Sale Hamalainemi, 2001) and ER (Evidential Reasoning) approach (Yang Xu, 2002), among others. 

Chin, K.-S., Wang,Y.-M., Poon, G. K. K. Yang, J.-B. 2009. Failure mode and effects analysis using a group-based evidential reasoning approach. Computers Operations Research 36 1768-1779. 

Liu, J., Yang, J.B., Wang, J. and Sii, H. (2005), Engineering System Safety Analysis and Synthesis using the Fuzzy Rule-based Evidential Reasoning Approach, Quality and Reliability Engineering International, Vol. 21, No. 4, pp. 387 11. 

Yang, J.B., Liu, J., Wang, J., Sii, H.S. and Wang, H.W (2006), A Belief Rule-base Inference Methodology using the Evidential Reasoning Approach - RIMER , IEEE Transactions on sterns, Man. and Cybernetics-PartA,Vo. i6, No.2, pp.266- 285. 

Wang et al. 2013. Acddent analysis model based on bayesian network and evidential reasoning approach. Journal of loss prevention in the process industries 26 (1) 10-21. 

Yang, J.B. (2001). Rule and utility based evidential reasoning approach for multi attribute decision analysis under uncertainties. European Journal of Operational Research, 131(1), pp. 31-61. 

Multisource image classification II An empirical comparison of evidential reasoning and neural network approaches. Canadian Journal of Remote Sensing, 12, 277-302. 

IDS is a general-purpose multi-criteria decision analysis tool based on the methodology called the Evidential Reasoning (ER) approach (Yang Xu, 2002). The software was developed to deal with multi-criteria problems having both quantitative and quaUtative information with uncertainties and subjectivity - thus IDS can be used to resolve both the external uncertainty (in data) and internal uncertainty (imprecision in judgments) as discussed in Chapter 2 of this paper. 

Quantifying Uncertainty in Safety Cases Using Evidential Reasoning. This paper introduces an evidence-based approach to deal with uncertainty in human judgment on safety case development and assessment. 

One realistic way to analyse a system with unavailable data is to employ subjective assessment using the combination of fuzzy logic and Evidential Reasoning (ER). Compared to the traditional fuzzy inference mechanism (i.e. max-min fuzzy operations), an ER approach has the advantage of avoiding the loss of useful information in its inference processes hence, it can be suitable for modelling complex systems. 

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