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French version |
The issues from 1 to 148 are also available on line at NUMDAM and at Revues.org for the following issues |
- Results for criterions:
- Keyword: Social influence
2 matches
| Title | Statistical physics of collective phenomena in social and economic sciences |
| Author | NADAL Jean-Pierre, GORDON Mirta B. |
| Keywords | Collective phenomena, Discrete choices with externalities, Emergence, Schelling, Social influence, Statistical physics |
| Topics | Dynamical Systems, Economy - Econometrics, Modelling, Sociology |
| Abstract | This article shows how statistical physics may contribute to the modelling of collective phenomena in economics and social science. The main topic here is the study of the global (aggregate) behavior of a large population, when the agents make choices under social influence. We present several examples, starting from pioneering works in economics and sociology, such as those of T. Schelling whose approach has all the flavour of physicists' approaches. |
| Number | 172, Winter 2005, special issue: Models and mathematical methods in the social sciences: contributions and limits |
| Language | French |
| Title | Social influence and diffusion of innovation |
| Author | STEYER Alexandre, ZIMMERMANN Jean-Benoit |
| Keywords | Diffusion, Diffusion curve, Innovation, Learning, Networks, Power law, Social influence, Structure |
| Topics | Diffusion, Dynamical Systems, Networks, Social Sciences |
| Abstract | The notion of diffusion holds a central place in any social system, because it is at the heart of individuals behaviour or representation phasing, hence of the co-ordination of their actions. The idea at the origin of the notion of diffusion is that inter-individual interactions are the driving forces of the evolution of individuals' behaviours, beliefs and represen-tations. Our approach in this paper is based on social influence networks. Agents are embedded in network structures where the influence advance depends on cumulative effects. First we draw the foundations of a diffusion model based on social influence networks. Then we study the way of propagation of influence trough "avalanches" giving a central importance to the network topology. We consider the noise produced by those avalanches as a characteristic of the social structure that can contribute, by learning effect, to transform the network structure, hence the dynamics of the diffusion. We then explain why peculiar "critical" diffusion curves do emerge characterized by a power law instead of the exponential form of traditional diffusion curves. |
| Number | 168, Winter 2004, special issue: Social networks |
| Language | French |
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