@article{Ste-04,
        title = {Influence sociale et diffusion de l'innovation},
        author = {Alexandre Steyer and Jean-Beno\^\it Zimmerman},
        journal = {Math{\'e}matiques \{\&} Sc. Humaines},
        number = 168,
        pages = {43--57},
        volume = 42,
        year = 2004,
        url = {http://www.ehess.fr/revue-msh/pdf/N168R929.pdf},
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p17},
	url = {file://localhost/Users/bertilhatt/Documents/Papers/Steyer/2004/Steyer%202004%20Math%C3%A9matiques%20%20&%20Sc.%20Humaines.pdf},
	modified = {2007-06-11 17:49:27 +0200},
	description = {March 2008},
	abstract = {The notion of diffusion holds a central place in any social system, because it is at the heart of individuals behavior or representation phasing, hence of the coordination 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 representations. 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.},
	biburl = {http://www.bibsonomy.org/bibtex/26abd95a03c65bf072d08794c0d52dce4/bertil.hatt},
	keywords = {Learning Influence Curve Structure Power Innovation Networks Law Diffusion Social and}
}

@unpublished{Plo-96,
        title = {Learning Induced Criticality in Consumers' Adoption Pattern: A Neural Network Approach Learning Induced Criticality in Consumers' Adoption Pattern: A Neural Network Approach Learning Induced Criticality in Consumers' Adoption Pattern: A Neural Network Approach},
        author = {Franck Plourabou{\'e} and Alexandre Steyer and Jean-Beno\^\it Zimmerman},
        note = {working paper},
        year = 1996,
        url = {http://www.vcharite.univ-mrs.fr/GREQAM/pdf/working_papers/1996/96a28s.pdf},
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p118},
	modified = {2007-06-11 17:48:42 +0200},
	description = {March 2008},
	abstract = {The aim of this paper is to lay the foundations of a social influence based approach for the diffusion of an innovation or a technological standard. A model built on the principles of a neural network is proposed and a learning procedure is set up, making the network formation endogenous, the strength of connections among agents being determined by their shared histories. Referring to the concept of criticality developed by physicists, it shall be shown that learning, in a social structure, can lead the network to a critical state, called `learning induced criticality', where some agents are able to exert a macroscopic influence over the network. The distribution of influence spheres' size follows a Pareto law. This approach shows an interesting similarity with that of the social coherence in sociology, whereby individuals within a social structure are led to share a close assessment of a given innovation.},
	biburl = {http://www.bibsonomy.org/bibtex/2a50644674b22dd502391ff4098104371/bertil.hatt},
	keywords = {Learning externality Network Criticality influence Diffusion Adoption Social and}
}

@unpublished{Ste-06,
        title = {Social Networks and Diffusion: Avalanches and Links Evolution},
        author = {Alexandre Steyer and Jean-Beno\^\it Zimmerman},
        note = {working paper Gr{\'e}quam},
        year = 2005,
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p13},
	modified = {2007-06-11 17:49:27 +0200},
	description = {March 2008},
	abstract = {The concept of diffusion is central to every social system, because it underpins the coherence of individuals' behaviour and representations, and hence the coordination of their actions. The idea at the origin of the concept of diffusion is that inter-individual interactions are the driving force behind the evolution of individual's behaviour, beliefs and representations. Our approach in this paper in this paper is based on social influence networks. Agents are embedded in social networks where the advance of influence depends on the propagation of ``avalanches'', giving central importance to the network structure. We consider the noise produced by those avalanches as the characteristic of the social structure that can contribute, through the evolution of links, to transforming the network structure, and hence the dynamics of the diffusion. We then explain why peculiar ``critical'' diffusion dynamics emerge, characterised by a power law distribution, instead of the exponential shape of traditional diffusion curves.},
	biburl = {http://www.bibsonomy.org/bibtex/2dd6d5bcff2977888635ea8f584389d3b/bertil.hatt},
	keywords = {Learning Influence Links Structure Power Networks Law Diffusion Social Evolution and}
}

@unpublished{Der-03,
        title = {Apprentissage social et diffusion de l'innovation : r{\'e}seaux critiques et interm{\'e}diarit{\'e}},
        author = {Fr{\'e}d{\'e}ric Dero\u{\i}an and Alexandre Steyer and Jean-Beno\^\it Zimmerman},
        note = {working paper},
        year = 2003,
        url = {http://durandal.cnrs-mrs.fr/GREQAM/at/at/htm},
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p44},
	modified = {2007-11-09 11:25:27 +0100},
	description = {March 2008},
	abstract = {Le r{\^o}le des r{\'e}seaux sociaux dans la diffusion de l'information demeure une question strat{\'e}gique. Dans des travaux ant{\'e}rieurs, nous avons introduit un apprentissage relationnel, de type hebbien, qui conduit {\`a} un {\'e}tat critique dans lequel certains agents aqui{\`e}rent des positions, purement structurelles, de leaders d'opinion. Dans cet article, nous montrons que l'auto-organisation d'un r{\'e}seau d'influence, par effet de l'apprentissage social, ne constitue pas un ph{\'e}nom{\`e}ne monotone, mais aussi bien du point de vue des caract{\'e}ristiques structurelles du r{\'e}seau que de celui de ses performances en diffusion. Ceci n{\'e}cessite, pour {\^e}tre analys{\'e}, de recourir {\`a} la notion d'interm{\'e}diarit{\'e} qui est inh{\'e}rente au concept de r{\'e}seau. Une analyse relative au r{\^o}le des liens faibles dans les diff{\'e}rents r{\'e}gimes de diffusion devrait alors permettre d'offrir un {\'e}clairage nouveau sur cette dynamique d'{\'e}volution.},
	biburl = {http://www.bibsonomy.org/bibtex/28e4e51ef83d0dfbaafdf3becaa4c16d9/bertil.hatt},
	keywords = {networks diffusion Learning State Innovation Critical Social Intermediarity and}
}

@article{Ell-93,
        title = {Learning, Local Interaction, and Coordination},
        author = {Glenn Ellison},
        journal = {Econometrica},
        number = 5,
        pages = {1047--1071},
        volume = 61,
        year = 1993,
        added = {2007-06-11 17:22:07 +0200},
	read = {Yes},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p100},
	url = {file://localhost/Users/bertilhatt/Documents/Papers/Ellison/1993/Ellison%201993%20Econometrica-2.pdf},
	modified = {2007-06-11 17:39:54 +0200},
	description = {March 2008},
	abstract = {This paper discuses the dynamic implications of learning in a large population coordination game, focusing on the structure of the matching proocess which describes how players meet. As in Kandori, Mailath, and Rob (1993) a combination of experimentation and myopia creates ``evolutionnary'' forces which lead to players to coordinate on the risk dominant equilibrium. To describe play with finite time horizons it is necessary to consider the rates at which the dynamic systems converge. In large populations with uniform matching, play is determined by histtorical factors. In contrast, when players interact with small sets of neighbors it is more reasonnable to assume that evolutionnary forces may determine the outcome.},
	biburl = {http://www.bibsonomy.org/bibtex/2de5c43db7b65894286b4705a285c2dd3/bertil.hatt},
	keywords = {of Learning Coordination Convergence Rates Neighbors and}
}

@article{Der-01,
        title = {Formation of social networks and diffusion of innovations},
        author = {Fr{\'e}d{\'e}ric Dero\u{\i}an},
        journal = {Research Policy},
        number = 1,
        pages = {1--12},
        volume = 1331,
        year = 2001,
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p83},
	modified = {2007-11-09 11:25:29 +0100},
	description = {March 2008},
	abstract = {Some innovations need delay to diffuse, others often fail. The formation of social networks is a possible ecplanation. Considering a population of potential adopters of a technology, we set-up a model composed of inteacting agents. Interaction is conceived as ofluence effects and the network of interpersonal influences is learning step-by-step. The gradual foramtion of the social network leads, after a period of latency, to a collective evaluation of the innovation.},
	biburl = {http://www.bibsonomy.org/bibtex/20678507dcac033f699f19d29409953ce/bertil.hatt},
	keywords = {Interaction Learning Network Diffusion Slow Social Bifurcation and}
}

@article{Bal-00,
        title = {A Noncooperative Model of Network Formation},
        author = {Venkatesh Bala and Sanjeev Goyal},
        journal = {Econometrica},
        number = 5,
        pages = {1181--1229},
        volume = 68,
        year = 2000,
        added = {2007-06-11 17:22:07 +0200},
	rating = {0},
	uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p52},
	modified = {2008-02-07 02:22:24 +0100},
	description = {March 2008},
	abstract = {We present an approach of network formation based on the notion that social networks are formed by individual decision that trade off the costs of forming and maintaining links against the costs of forming and maintaining links against the potential rewards from doing so. We suppose that a link with another agent allows access, in part and in de course, to the benefits available to the benefits available by the latter via its own links.This individual links generate externalities whose value depends on the level of decay or delay associated with indirect links. A distinctinvee aspect of our approach is that the cost of link formation are incurrend only by the person who initiates the link. This allows us to formulate the network formation process as a noncooperative game. We first provide a characterization of the architecture of equilibrium networks. We then study the dynamics of network formation. We find that individual efforts to access benefits affered by others lead, rapidly, to the emergence of an equilibrium social network, under a variety of circumstances. The limiting networks have simple architectures, e.g., the wheel, the star, or generalizations of these networks. In many cases, such networks are also socially efficient.},
	biburl = {http://www.bibsonomy.org/bibtex/20a81df50bf10a679ce485a35dedc8864/bertil.hatt},
	keywords = {games Learning Coordination dynamics Noncooperative Networks and}
}