Coordination failures in network formation.

In this paper, we make an exploratory use of numerical techniques (genetic algorithms and Monte Carlo simulations) to compute efficient and emergent networks in a spatialized version of the connections model of Jackson and Wolinski (1996). This approach allows us to observe and discuss the coordination failures that arise in a strategic network formation context with link-mediated positive externalities to connections and geographically based connection costs. Our results highlight that, depending on the strength of the externalities, emergent and efficient networks may share several structural properties. Nevertheless, emergent networks have too few local and distant connections and are also too less “coordinated” around some central agents than they should.Strategic Network Formation; Efficiency; Stability; Coordination; Small Worlds; Genetic Algorithms; Monte Carlo Simulations.

In search of efficient network structures: the needle in the haystack

Abstract Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of nontrivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced b

Can they beat the Cournot equilibrium? Learning with memory and convergence to equilibria in a Cournot oligopoly

This article analyses the possibility of firms learning collusive solutions in a Cournot quantity game. Starting from the results of Vallée and Yildizoglu (2009) and of Alos-Ferrer (2004), we study the role of random experimenting, social learning (imitation), and (updated) memory in helping firms to discover more collusive market configurations than those of the Cournot equilibrium (CE). We show that long memory and its update is necessary to achieve such configurations