1,324 research outputs found
Modeling Contagion of Behavior in Friendship Networks as Coordination Games
It has been shown that humans are heavily influenced by peers when it comes to choice of behavior, norms or opinions. In order to better understand and help to predict society’s behavior, it is therefore desirable to design social simulations that incorporate representations of those network aspects. We address this topic, by investigating the performance of a coordination game mechanism in representing the diffusion of behavior for distinct data sets and diverse behaviors in children and adolescent social networks. We introduce a set of quality measurements in order to assess the adequacy of our simulations and find evidence that a coordination game environment could underlie some of the diffusion processes, while other processes may not be modeled coherently as a coordination game
Stochastic network formation and homophily
This is a chapter of the forthcoming Oxford Handbook on the Economics of
Networks
Long ties accelerate noisy threshold-based contagions
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshold number of adopting neighbors above which adoption occurs (i.e.,
complex contagions). Recent work has argued that highly clustered, rather than
random, networks facilitate spread of these complex contagions. Here we show
that minor modifications to this model, which make it more realistic, reverse
this result: we allow very rare below-threshold adoption, i.e., rarely adoption
occurs when there is only one adopting neighbor. To model the trade-off between
long and short edges we consider networks that are the union of cycle-power-
graphs and random graphs on nodes. Allowing adoptions below threshold to
occur with order probability along some "short" cycle edges is
enough to ensure that random rewiring accelerates spread. Simulations
illustrate the robustness of these results to other commonly-posited models for
noisy best-response behavior. Hypothetical interventions that randomly rewire
existing edges or add random edges (versus adding "short", triad-closing edges)
in hundreds of empirical social networks reduce time to spread. This revised
conclusion suggests that those wanting to increase spread should induce
formation of long ties, rather than triad-closing ties. More generally, this
highlights the importance of noise in game-theoretic analyses of behavior
Behavioral Communities and the Atomic Structure of Networks
We develop a theory of `behavioral communities' and the `atomic structure' of
networks. We define atoms to be groups of agents whose behaviors always match
each other in a set of coordination games played on the network. This provides
a microfoundation for a method of detecting communities in social and economic
networks. We provide theoretical results characterizing such behavior-based
communities and atomic structures and discussing their properties in large
random networks. We also provide an algorithm for identifying behavioral
communities. We discuss applications including: a method of estimating
underlying preferences by observing behavioral conventions in data, and
optimally seeding diffusion processes when there are peer interactions and
homophily. We illustrate the techniques with applications to high school
friendship networks and rural village networks
Social Networks
We survey the literature on social networks by putting together the economics, sociological and physics/applied mathematics approaches, showing their similarities and differences. We expose, in particular, the two main ways of modeling network formation. While the physics/applied mathematics approach is capable of reproducing most observed networks, it does not explain why they emerge. On the contrary, the economics approach is very precise in explaining why networks emerge but does a poor job in matching real-world networks. We also analyze behaviors on networks, which take networks as given and focus on the impact of their structure on individuals’ outcomes. Using a game-theoretical framework, we then compare the results with those obtained in sociology.Random Graph; Game Theory; Centrality Measures; Network Formation; Weak
Social Networks
We survey the literature on social networks by putting together the economics, sociological and physics/applied mathematics approaches, showing their similarities and differences. We expose, in particular, the two main ways of modeling network formation. While the physics/applied mathematics approach is capable of reproducing most observed networks, it does not explain why they emerge. On the contrary, the economics approach is very precise in explaining why networks emerge but does a poor job in matching real-world networks. We also analyze behaviors on networks, which take networks as given and focus on the impact of their structure on individuals’ outcomes. Using a game-theoretical framework, we then compare the results with those obtained in sociology.random graph, game theory, centrality measures, network formation, weak and strong ties
Recommended from our members
Networks in economics: a perspective on the literature
The Oxford Handbook of the Economics of Networks represents the frontier of research into how and why networks they form, how they influence behavior, how they help govern outcomes in an interactive world, and how they shape collective ..
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
- …