Self-Organizing Innovation Networks: When do Small Worlds Emerge?

In this paper, we present a model of 'collective innovation' built upon the network formation formalism. In our model, agents localized on a circle benefit from knowledge flows from other agents with whom they are directly or indirectly connected. They support costs for direct connections which are linearly increasing with geographic distance. The dynamic process of network formation exhibits prefeRential meeting for close agents (in the relational network and in the geographic metrics). We show how the set of stochastically stable networks selected in the long run is affected by the degree of knowledge transferability. We find critical values of this parameter for which stable \"small world\" networks are dynamically selected.Network Formation, Stochastic Stability, Preferential Meeting, Self-Organization,

Efficiency of network structures: The needle in the haystack

The modelling of networks formation has recently became the object of an increasing interest in economics. One of the important issues raised in this literature is the one of networks efficiency. Nevertheless, for non trivial payoff functions, searching for efficient network structures turns out to be a very difficult analytical problem as well as a huge computational task, even for a relatively small number of agents. In this paper, we explore the possibility of using genetic algorithms (GA) techniques for identifying efficient network structures, because the GA have proved their power as a tool for solving complex optimization problems. The robustness of this method in predicting optimal network structures is tested on two simple stylized models introduced by Jackson and Wolinski (1996), for which the efficient networks are known over the whole state space of parameter values.Networks, Optimal network structure, Efficiency, Genetic Algorithms