Self-organized criticality in a model of collective bank bankruptcies

The question we address here is of whether phenomena of collective bankruptcies are related to self-organized criticality. In order to answer it we propose a simple model of banking networks based on the random directed percolation. We study effects of one bank failure on the nucleation of contagion phase in a financial market. We recognize the power law distribution of contagion sizes in 3d- and 4d-networks as an indicator of SOC behavior. The SOC dynamics was not detected in 2d-lattices. The difference between 2d- and 3d- or 4d-systems is explained due to the percolation theory.Comment: For Int. J. Mod. Phys. C 13, No. 3, six pages including four figure

The Structure of Information Pathways in a Social Communication Network

Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of "distance" in the underlying social network by measuring the minimum time required for information to spread from one node to another -- a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges -- a finding that sheds new light on the relationship between tie strength and connectivity in social networks.Comment: 9 pages, 10 figures, to appear in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08), August 24-27, 2008, Las Vegas, Nevada, US

The structure of information pathways in a social communication network

Social networks are of interest to researchers in part because they are thought to mediate the flow of information in communities and organizations. Here we study the temporal dynamics of communication using on-line data, including e-mail communication among the faculty and staff of a large university over a two-year period. We formulate a temporal notion of “distance ” in the underlying social network by measuring the minimum time required for information to spread from one node to another — a concept that draws on the notion of vector-clocks from the study of distributed computing systems. We find that such temporal measures provide structural insights that are not apparent from analyses of the pure social network topology. In particular, we define the network backbone to be the subgraph consisting of edges on which information has the potential to flow the quickest. We find that the backbone is a sparse graph with a concentration of both highly embedded edges and long-range bridges — a finding that sheds new light on the relationship between tie strength and connectivity in social networks

Self-organized criticality in a model of collective bank bankruptcies

The question we address here is of whether phenomena of collective bankruptcies are related to self-organized criticality. In order to answer it we propose a simple model of banking networks based on the random directed percolation. We study effects of one bank failure on the nucleation of contagion phase in a financial market. We recognize the power law distribution of contagion sizes in 3d- and 4d-networks as an indicator of SOC behavior. The SOC dynamics was not detected in 2d-lattices. The difference between 2d- and 3d- or 4d-systems is explained due to the percolation theory.

Instant foodie: Predicting expert ratings from grassroots

ABSTRACT Consumer review sites and recommender systems typically rely on a large volume of user-contributed ratings, which makes rating acquisition an essential component in the design of such systems. User ratings are then summarized to provide an aggregate score representing a popular evaluation of an item. An inherent problem in such summarization is potential bias due to raters' self-selection and heterogeneity in terms of experiences, tastes and rating scale interpretations. There are two major approaches to collecting ratings, which have different advantages and disadvantages. One is to allow a large number of volunteers to choose and rate items directly (a method employed by e.g. Yelp and Google Places). Alternatively, a panel of raters may be maintained and invited to rate a predefined set of items at regular intervals (such as in Zagat Survey). The latter approach arguably results in more consistent reviews and reduced selection bias, however, at the expense of much smaller coverage (fewer rated items). In this paper, we examine the two different approaches to collecting user ratings of restaurants and explore the question of whether it is possible to reconcile them. Specifically, we study the problem of inferring the more calibrated Zagat Survey ratings (which we dub "expert ratings") from the user-contributed ratings ("grassroots") in Google Places. To achieve this, we employ latent factor models and provide a probabilistic treatment of the ordinal ratings. We can predict Zagat Survey ratings accurately from ad hoc usergenerated ratings by employing joint optimization. Furthermore, the resulting model show that users become more discerning as they submit more ratings. We also describe an approach towards cross-city recommendations, answering questions such as "What is the equivalent of the Per S

Action Learning versus Strategy Learning ∗

This paper seeks to ascertain whether the strategy-learning model of Hanaki, Sethi, Erev, and Peterhansl (2003) better accounts for observed behavior than do the various action-learning models. It does so by measuring the goodness-of-fit of the models’ predictions against published experimental results for such games as Coordination, Prisoner’s Dilemma, and Chicken. The fit is measured via the mean squared deviation (MSD) between the observed behavior and the one predicted by the model. The results show that, for Chicken, the strategy-learning model fits the observed data much better than do the action-learning models. The best action-learning model, on the other hand, fits the observed data well in Coordination. Overall, the strength of the strategy-learning model is best shown in games where alternations between the two stage-game Nash equilibria are often observed in the laboratory experiments