Analyzing Risk Impact Factors Using Extended Fuzzy Cognitive Maps

One of the challenges in Risk Analysis and Management (RAM) is identifying the relationships between risk factors and risks. The complexity of the method to analyze these relationships, the time to complete the analysis, the robustness and trustworthiness of the method are important features to be considered. In this paper, we propose using Extended Fuzzy Cognitive Maps (E-FCMs) to analyze the relationships between risk factors and risks, and adopting a pessimistic approach to assess the overall risk of a system or a project. E-FCMs are suggested by Hagiwara to represent causal relationships more naturally. The main differences between E-FCMs and conventional Fuzzy Cognitive Maps (FCMs) are the following: E-FCMs have nonlinear membership functions, conditional weights, and time delay weights. Therefore E-FCMs are suitable for risk analysis as all features of E-FCMs are more informative and can fit the needs of Risk Analysis. In this paper we suggest a framework to analyze risks using E-FCMs and extend E-FCMs themselves by introducing a special graphical representation for risk analysis. We also suggest a framework for group decision making using E-FCMs. Particularly, we explore the Software Project Management (SPM) and discuss risk analysis of SPM applying E-FCMs.

I. Introduction

The most important factors contributing to the risk of failure for any type of organization or system are related to poor performance, time pressure, low quality and high cost. Every year there is evidence of a large number of cases where a project or an organization fails, and the consequences of those failures are sometimes fatal. Notwithstanding all the technological advances and a huge amount of risk analysis approaches, risks associated with a project or a system cannot yet be analyzed precisely and the risk analysis itself still contains high risks.