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Previous studies indicate the importance of experience for the performance of teams. Theory suggests that working together allows individuals to (a) improve their knowledge about who knows (and can do) what and (b) facilitates learning to combine individual resources efficiently. Yet it remains elusive how experience translates into interaction patterns in teams. Drawing on unique data of career histories of 800 players and 283,259 passes between these players in 760 English Premier League soccer matches, I propose a new measure for network experience and demonstrate how it relates to network intensity and decentralization in teams. Soccer teams exhibit a higher passing rate when players know each other from before. At the same time, network experience has no effect on the decentralization of team play. Further dyadic analyses confirm these findings. Controlling for selection effects, two players are more likely to pass the ball between each other when they know each other more. Network experience affects the way team members interact and ultimately leads to performance outcomes.

Abstract
The study of international relations by definition deals with interdependencies among countries. One form of interdependence between countries is the diffusion of country-level features, such as policies, political regimes, or conflict. In these studies, the outcome variable tends to be categorical, and the primary concern is the clustering of the outcome variable among connected countries. Statistically, such clustering is studied with spatial econometric models. This article instead proposes the use of a statistical network approach to model diffusion with a binary outcome variable. Using a statistical network instead of spatial econometric models allows for modeling autocorrelation in policy outcomes rather than the corresponding latent variable, and it simplifies the inclusion of temporal dynamics, higher level interdependencies, and interactions between network ties and country-level features. In our simulations, the performance of the Stochastic Actor-Oriented Model (SAOM) estimator is evaluated. Our simulation results show that spatial parameters and coefficients on additional covariates in a static binary spatial autoregressive model are accurately recovered when using SAOM. To demonstrate this model, the paper applies SAOM to original data on the international diffusion of same-sex marriage and gives practical instructions for using such models.

AbstractPrevious research stresses the importance of social networks for obesity. We draw on friendship data from 18,133 adolescents in four European countries to investigate the relationship between individuals' body mass index (BMI) and the BMI of their friends. Our study reveals strong evidence for BMI clustering in England, Germany, the Netherlands, and Sweden; adolescents tend to be friends with others who have a similar BMI. Furthermore, we extend current debate and explore friendship characteristics that moderate the relationship between social networks and BMI. We demonstrate that BMI clustering is more pronounced in (1) strong compared to weak friendships and (2) between adolescents of the same biological sex. These findings indicate thatmore research on social networks and health is needed which distinguishes between different kinds of relationships.

Intro -- Contents -- Chapter 1: Social Simulation for a Digital Society: Introduction -- References -- Part I: Philosophical and Technical Considerations -- Chapter 2: Ontological Politics in a World of Political Ontologies: More Realistic (Human) Agents for the Anthropocene? -- Introduction -- Complex Problems and the Integration Imperative -- Agent-Based Models and Qualitative Inquiry -- A Philosophical and Sociological Critique -- ABM's Methodological Individualism -- Simplification Versus Messy Reality -- Colonisation, Collaboration and Depictions Otherwise -- Conclusion -- References -- Chapter 3: The Art of Iterating: Update-Strategies in Agent-Based Simulation -- Introduction -- Background -- Agent-Based Simulation -- A Discrete Game: Prisoners Dilemma -- A Continuous Game: Heroes & -- Cowards -- Related Research -- A New Terminology -- ABS Properties -- ABS Update-Strategy: Sequential -- ABS Update-Strategy: Parallel -- ABS Update-Strategy: Concurrent -- ABS Update-Strategy: Actor -- ABS Toolkits -- Case Studies -- Prisoners Dilemma -- Heroes & -- Cowards -- Conclusion and Future Research -- References -- Chapter 4: Using Parallel Computing to Improve the Scalability of Models with BDI Agents -- Introduction -- Related Works -- Cognitive Agent Architectures -- Parallelization of a Simulation -- Parallel Cognitive and Emotional Architecture -- Case Study -- Conclusion -- References -- Part II: Applications to Norm Diffusion and Collective Action -- Chapter 5: Information Diffusion as a Mechanism for Natural Evolution of Social Networks -- Introduction -- Model Design -- Link Formation -- Centrality as Influence -- Preferential Attachment -- Equivalent Influence -- Link Decay -- Model Proof of Concept: Social License to Operate -- Model Validation and Discussion -- Concluding Remarks -- References.

Tackling issues that range from disruptive street gangs to online illicit markets, the authors use the insights of network analysis—a sophisticated methodology for illuminating individual and group interconnections—to suggest practical, highly targeted ways to prevent criminal behavior