Fuzzy cognitive mapping in factor elimination: A case study for innovative power and risks

Abstract

Factor or criteria prioritization is essential for decision making and planning. In most areas in decision making, integrating the related literature yields an exuberance of criteria which leads a robust decision. Yet, an excess number of criteria may handicap decision making or evaluations in terms of computational time and complexity. In these circumstances, decreasing the number of factors in exchange for a negligible amount of knowledge can emancipate the decision maker yet does not affect the quality of the decision. This elimination can be conducted through qualitative methods such as interviews or quantitative methods. However, quantitative methods are more trustworthy since qualitative methods can be deceptive due to the perceptions of the interviewee. Furthermore, working with larger groups is more prone to neutrality in terms of group thinking. On the subject of innovative power and risks, the literature offers 48 criteria depending on the industry, size or demographics of related companies. Prioritizing and working with these criteria for their decision making applications becomes computationally expensive, especially when embedded in more complex algorithms. In this study, 48 criteria will be reduced using Fuzzy Cognitive Maps and it is believed to provide a sufficient number of criteria with a negligible loss of information and comparisons will be conducted.

Knowledge discovery using maximization of the spread of influence in an expert system

“…Another difficulty encountered in the design of the ES knowledge acquisition unit is connected with the subject of KA, which can be divided into three main categories, namely, natural, technological, and human activity systems. Socio‐economic systems fall into the last category and have the following specific characteristics: multiple connections of the system; system integrity; qualitative nature of the parameters, which thereby do not have a physical dimension (Altay & Kayakutlu, ) and their multiplicity; nonobviousness of the “decision → results” and “factors → result” causalities; ambiguity in the system element interaction principle; the existence of significant relations, defining the system; communication with the outside world; uncertain status of the existence of certain elements of the system (collective psychological phenomena); the emergence of new factors related to the evolution of the system over time; and a large results time lag, making it difficult to obtain the system response. …”

“…• qualitative nature of the parameters, which thereby do not have a physical dimension (Altay & Kayakutlu, 2011) and their multiplicity;…”

A new method for identifying the central nodes in Fuzzy Cognitive Maps using Consensus Centrality Measure

“…The degree centrality measure has been used to analyze the structure of social FCMs by characterising their most important nodes (Ozesmi and Ozesmi, 2004;Strickert, 2009). Altay and Kayakutlu (2011) utilized the degree centrality measure of a node in FCMs to prioritize and rank the factors (criteria) for decision making in complex applications and used this rank to reduce the excess number of criteria in order to make it a realistic and robust decision making process.…”