Estimation of Causality Coefficients in Strategy Maps Using Gravitational Search-Based Learning of Fuzzy Cognitive Maps
Subject Areas : electrical and computer engineeringA. Jahanbeigi 1 , A. Jahanbeigi 2 * , M. Rohani 3
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Keywords: Gravitational search algorithm balanced scorecard strategy map fuzzy cognitive map,
Abstract :
More than two decades ago, the balanced scorecard method was proposed to control and monitor the strategy of organizations. The most important outcome of this method is the strategy map. The causal relations among strategic goals (SGs) are established in this map which can help managers in decision making process. To have a precise strategy map, it is necessary to estimate the strengths of each causal relation correctly. So, the estimation of causal coefficients has attracted research interest in forming strategy maps. In this way, DEMATEL and Delphi are two well-known methods that are based on the experts’ opinion. However, these opinions are not exact in the complex business fields; so, the computational intelligence (CI) algorithms have been employed for more precise estimation of causality coefficients. In this study, the relations among SGs and their coefficients have been provided by the experts of a banking institution as the input of the proposed method. The main purpose of this study is to improve the precision of causal coefficients using a CI-based algorithm. For this purpose, the strategy map is decomposed into multiple fuzzy cognitive maps (FCMs) and then, the gravitational search algorithm (GSA) is employed for FCM training. In this way, two objective functions are used for determining the optimal value of causality coefficients. The first objective function is employed for reducing error in the prediction of SG realization percentages. The second objective function keeps causal coefficients in the intervals determined by the experts. Experimental results show that the total error of proposed model is lower than the expert-based model. In addition, GSA performs better than the following algorithms in finding the global optimum point in this real-world case study: particle swarm optimization and ant colony optimization.
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