Investigations of Symmetrical Incomplete Information Spreading in the Evidential Reasoning Algorithm and the Evidential Reasoning Rule via Partial Derivative Analysis

Abstract

Incomplete information causes great uncertainty in decision making. It is a critical task to understand how incomplete information spreads symmetrically in order to make comprehensive and balanced decisions. A better understanding of the spreading of incomplete information can also be used for accurately locating limited resources to reduce incomplete information in the input for the final purpose of reducing incomplete information in the result. In this study, the way in which incomplete information spreads is studied via the evidential reasoning (ER) algorithm and the evidential reasoning rule (the ER rule), which are known for their transparent analytical procedures. Specifically, the partial derivative analysis is conducted using the steps of ER and the ER rule for calculating the contributions made by the beliefs, weights, and reliability to the incomplete information in the result. The major theoretical contribution of this study is the calculation of the contribution of the input to the incomplete information in the output based on partial derivative analysis. A numerical case is studied to demonstrate the proposed derivative analysis, the contribution calculation, and the consequential results.