We present a dynamic model of network formation where nodes find other nodes with whom to form links in two ways: some are found uniformly at random, while others are found by searching locally through the current structure of the network (e.g., meeting friends of friends). This combination of meeting processes results in a spectrum of features exhibited by large social networks, including the presence of more high- and low-degree nodes than when links are formed independently at random, having low distances between nodes in the network, and having high clustering of links on a local level. We fit the model to data from six networks and impute the relative ratio of random to network-based meetings in link formation, which turns out to vary dramatically across applications. We show that as the random/network-based meeting ratio varies, the resulting degree distributions can be ordered in the sense of stochastic dominance, which allows us to infer how the formation process affects average utility in the network. (JEL D85, Z13)

We report experimental results from long sequences of decisions in environments that are theoretically prone to severe information cascades. Observed behaviour is much different-information cascades are ephemeral. We study the implications of a theoretical model based on quantal response equilibrium, in which the observed cascade formation/collapse/formation cycles arise as equilibrium phenomena. Consecutive cascades may reverse states, and usually such a reversal is self-correcting: the cascade switches to the correct state. These implications are supported by the data. We extend the model to allow for base rate neglect and find strong evidence for overweighting of private information. The estimated belief trajectories indicate fast and efficient learning dynamics. Copyright 2007 The Review of Economic Studies Limited.

We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of 'macro' and 'micro' characteristics of social interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.

We examine the spread of a disease or behavior through a social network. In particular, we analyze how infection rates depend on the distribution of degrees (numbers of links) among the nodes in the network. We introduce new techniques using first- and second order stochastic dominance relationships of the degree distribution in order to compare infection rates across different social networks.

We explore an equilibrium model of games where players' choice behavior is given by logit response functions, but their payoff responsiveness is heterogeneous. We extend the definition of quantal response equilibrium to this setting, calling it heterogeneous quantal response equilibrium (HQRE), and prove existence under weak conditions. We generalize HQRE to allow for limited insight, in which players can only imagine others with low responsiveness. We identify a formal connection between this new equilibrium concept, called truncated quantal response equilibrium (TQRE), and the Cognitive Hierarchy (CH) model. We show that CH can be approximated arbitrarily closely by TQRE. We report a series of experiments comparing the performance of QRE, HQRE, TQRE and CH. A surprise is that the fit of the models are quite close across a variety of matrix and dominance-solvable asymmetric information betting games. The key link is that in the QRE approaches, strategies with higher expected payoffs are chosen more often than strategies with lower expected payoff. In CH this property is not built into the model, but generally holds true in the experimental data.JEL classification numbers: 024, 026

We model network formation when heterogeneous nodes enter sequentially and form connections through both random meetings and network-based search, but with type-dependent biases. We show that there is “long-run integration”, whereby the composition of types in sufficiently old nodesʼ neighborhoods approaches the global type-distribution, provided that the network-based search is unbiased. However, younger nodesʼ connections still reflect the biased meetings process. We derive the type-based degree distributions and group-level homophily patterns when there are two types and location-based biases. Finally, we illustrate aspects of the model with an empirical application to data on citations in physics journals

We examine a simple economic model of network formation where agents benefit from indirect relationships. We show that small-world features-short path lengths between nodes together with highly clustered link structures-necessarily emerge for a wide set of parameters. (JEL: D85, A14, C72) Acknowledgments: Financial support under NSF Grant no. SES-0316493 is gratefully acknowledged, as is support from the Lee Center for Advanced Networking and a SISL/IST fellowship. We thank Fernando Vega-Redondo for the opportunity to present in this session and, along with Matthias Dahm and an anonymous referee, for comments on an earlier version of this paper.

We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of 'macro' and 'micro' characteristics of social interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.