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The paper analyses the scientific research production of more than a thousand faculty members at Louis Pasteur University, a large and well-ranked European research university. The originality of our approach is that we take into account both individual and collective (laboratory) determinants to explain individual productivity in terms of intensity and(More)
1 The authors thank Herbert Dawid for helpful discussion. The participants of the ACEPOL05 Workshop, ZiF, Bielefeld and of the I-neck meeting, BETA, Strasbourg should also be acknowledged. We are grateful to two anonymous referees whose criticisms and comments have greatly increased the robustness and, hopefully, the readibility of this article. Murat(More)
Une approche bayésienne de l'identification des inventeurs dans les bases de données de brevets Résumé Cet article propose une méthode bayésienne pour traiter le problème de l'identification des personnes dans les grandes bases de données individuelles telles que les données de brevet. L'apport de cette méthode est mis en évidence à travers son application(More)
In this paper we present an original model of sequential problem choice within scientific communities. Disciplinary knowledge is accumulated in the form of a growing tree-like web of research areas. Knowledge production is sequential since the problems addressed generate new problems that may in turn be handled. This model allows us to study how the reward(More)
We examine the exchange of favors when any two individuals in a society interact too infrequently to sustain exchange, but where the threat of losing multiple relationships can sustain exchange. We show that networks of favor exchange that are robust, in that deleted relationships only result in a local loss of favor exchange, are such that all links are "(More)
Rome. We also thank Laurent Bergé for his excellent research assistance. We would also like to thank the Agence Nationale de la Recherche (grant ANR-06-JCJC-0076) and the Aquitaine Region (AAP program) for their financial support. Abstract The literature on social networks argues that they are clustered because agents like closing triangles (since this(More)