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Combining sources of evidence with reliability and importance for decision making

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  • Lianmeng Jiao
  • Quan Pan
  • Yan Liang
  • Xiaoxue Feng
  • Feng Yang

Abstract

The combination of sources of evidence with reliability has been widely studied within the framework of Dempster-Shafer theory (DST), which has been employed as a major method for integrating multiple sources of evidence with uncertainty. By the fact that sources of evidence may also be different in importance, for example in multi-attribute decision making (MADM), we propose the importance discounting and combination method within the framework of DST to combine sources of evidence with importance, which is composed of an importance discounting operation and an extended Dempster’s rule of combination. Three evidence combination axioms are proposed and explored to uncover the differences between reliability and importance in evidence reasoning. Furthermore, a general scheme is proposed for combination of sources of evidence with both reliability and importance. An example of car performance evaluation is studied to show the efficiency of the new general scheme. Copyright Springer-Verlag Berlin Heidelberg 2016

Suggested Citation

  • Lianmeng Jiao & Quan Pan & Yan Liang & Xiaoxue Feng & Feng Yang, 2016. "Combining sources of evidence with reliability and importance for decision making," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 87-106, March.
    • Handle: RePEc:spr:cejnor:v:24:y:2016:i:1:p:87-106
    DOI: 10.1007/s10100-013-0334-3
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    References listed on IDEAS

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    4. Tasneem Bani-Mustafa & Nicola Pedroni & Enrico Zio & Dominique Vasseur & Francois Beaudouin, 2020. "A hierarchical tree-based decision-making approach for assessing the relative trustworthiness of risk assessment models," Journal of Risk and Reliability, , vol. 234(6), pages 748-763, December.

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