Modeling Conflict Dynamics

This chapter touches the question of how to model conflict. The discussion is limited to inter- and intra- organizational conflicts. The focus is on the behavior of the conflict in time. A working definition of conflict, named starting theory, is given. The presented models are constructed by means of system dynamics tools. A short explanation of system dynamics tools is given. Moreover, fuzzy logic and fuzzy system are introduced. Fuzzy system models human reasoning and decision making, and is integrated in the model of isolated conflict. Three models are presented, namely: the qualitative model, the model of isolated conflict, and, finally, the generic model of isolated conflict with fuzzy system. At the end, the results of a few simulation runs illustrate the use of the model.

Modeling Conflict Dynamics

This chapter touches the question of how to model conflict. The discussion is limited to inter- and intra- organizational conflicts. The focus is on the behavior of the conflict in time. A working definition of conflict, named starting theory, is given. The presented models are constructed by means of system dynamics tools. A short explanation of system dynamics tools is given. Moreover, fuzzy logic and fuzzy system are introduced. Fuzzy system models human reasoning and decision making, and is integrated in the model of isolated conflict. Three models are presented, namely: the qualitative model, the model of isolated conflict, and, finally, the generic model of isolated conflict with fuzzy system. At the end, the results of a few simulation runs illustrate the use of the model.

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.