Theory of Knowledge in System Dynamics Models

Having entered into the problem structuring methods, system dynamics (SD) is an approach, among systems’ methodologies, which claims to recognize the main structures of socio-economic behaviors. However, the concern for building or discovering strong philosophical underpinnings of SD, undoubtedly playing an important role in the modeling process, is a long-standing issue, in a way that there is a considerable debate about the assumptions or the philosophical foundations of it. In this paper, with a new perspective, we have explored theory of knowledge in SD models and found strange similarities between classic epistemological concepts such as justification and truth, and the mechanism of obtaining knowledge in SD models. In this regard, we have discussed related theories of epistemology and based on this analysis, have suggested some implications for moderating common problems in the modeling process of SD. Furthermore, this research could be considered a reword of system dynamics modeling principles in terms of theory of knowledge.     

Relative Degree Structural Hole Centrality,CRD−SH: A New Centrality Measure in Complex Networks

In order to assess influential nodes in complex networks, the authors propose a novel ranking method based on structural hole in combination with the degree ratio of a node and its neighbors. The proposed method is a response to the limitations of other proposed measures in this field. The structural hole gives a comprehensive attention of the information about the node topology in relation to its neighbors, whereas the degree ratio of nodes reflects its significance against the neighbors.Combination of the two aforementioned measures summarized in the structural hole leverage matrix demonstrates the importance of a node according to its position in the network structure. So a more accurate method for ranking influential nodes is established. The simulation results over different-scale networks(small networks with less than 30 nodes, medium networks with less than 150 nodes and large networks with more than 1000 nodes) suggest that the proposed method can rank important nodes more effectively and precisely in complex networks specifically in larger ones.     

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.