Analyzing Protocols of Information Granularity Allocation to Compute Missing Values in Intuitionistic Reciprocal Preference Relations

In the field of Granular Computing, a fundamental approach focuses on the management of incomplete intuitionistic reciprocal preference relations through the allocation of information granularity and the execution of a corresponding optimization procedure. It facilitates the transformation of numerical models into their granular counterparts, thereby providing a more accurate representation of reality to recover missing information. In decision-making contexts that involve intuitionistic reciprocal preference relations, this approach has proven essential in advancing procedures for estimating incomplete information. Nevertheless, while several protocols for information granularity allocation have been proposed, only one has been actively implemented thus far: the protocol based on a uniform and symmetric allocation of information granularity. To address this limitation, the objective of this study is to assess the effectiveness of existing protocols for allocating information granularity in the estimation of missing values in incomplete intuitionistic reciprocal preference relations. Numerical tests are included to demonstrate the efficacy of the protocols.