Social simulation implies two preconditions: determining a population and simulate the information diffusion within it. A population represents a group of interconnected individuals sharing information. In this paper, the population we generate is detailed by socio-cultural features, specifically the way that people tend to link together. To this end, the use of a social network is a little bit restrictive: people are linked by only one relationship. Multidimensional Social Networks (MSN) model 3D social networks where each dimension represent a kind of relationship [1]. This architecture allows us to better represent the diversity of humans relations but also define distinctive rules for the message diffusion simulation. The inner idea is that information disseminates differently according to the links through which the information propagates. So, we present in this paper the modeling of our MSN based on social science and a simulation using propagation rules set for each dimension.
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In this article, we present an extension of the frame-based language Objlogq , called Ž CAIN, which allows the homogeneous representation of approximate knowledge fuzzy, . uncertain, and default knowledge by means of new facets. We developed elements to manage approximate knowledge: fuzzy operators, extension of the inheritance mechanisms, and weighting of structural links. Contrary to other works in the domain, our system is strongly based on a theoretical approach inspired from Zadeh's and Dubois' works. We also defined an original instance classification mechanism, which has the ability to take into account the notions of typicality and similarity as they are presented in the psychological literature. Our model proposes consideration of a particular semantics of default values to estimate the typicality between a class and the instance to classify Ž . ITC . In that way, the possibilities of the typicality representation proposed by framebased languages are exploited. To find the most appropriate solution we do not systematically choose the most specific class that matches the ITC but we retain the most typical solution. Approximate knowledge is used to make the matching used during the classification process more flexible. Taking into account additional knowledge concerning heuristics and elements of cognitive psychology leads to the enrichment of the classification mechanism. ᮊ
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