Neural Networks and Contagion

We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxima of a potential function

Neural networks and contagion

We analyze local as well as global interaction and contagion in population games, using the formalism of neural networks. In contrast to much of the literature, a state encodes not only the frequency of play, but also the spatial pattern of play. Stochastic best response dynamics with logistic noise gives rise to a log-linear or logit response model. The stationary distribution is of the Gibbs-Boltzmann type. The long-run equilibria are the maxima of a potential function.Nous analysons les phénomènes de contagion apparaissant dans des situtions d’interaction locale ou globale. Nous utilisons les réseaux de neurones pour étudier ces phénomènes. Contrairement à beaucoup de modèles, un état de système décrit la structure spatiale des choix et non pas uniquement la fréquence avec laquelle chaque action est jouée dans la population. Les dynamiques de réponses optimales bruitée à l’aide d’une loi logistique génèrent un modèle de décision logit. La distribution stationnaire du processus est de type Gibbs-Boltzann. Les équilibres de long terme sont les états qui maximisent une fonction de potentiel

1.3 Nasdaq Simulation Model Overview................ 10

Isaac Saias, and others for their valuable comments and suggestions. We thank Paula Lozar for editing help. We acknowledge Nasdaq’s financial support in working on this project. Usual disclaimer applies