Influence et contagion
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Influence et contagion
L'influence et la contagion dans la cyberculture
Curated by luiy
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GLEAMviz.org – The GLEAM Simulator system | #dataviz #complexity #prediction

GLEAMviz.org – The GLEAM Simulator system  | #dataviz #complexity #prediction | Influence et contagion | Scoop.it
luiy's insight:

The GLEAM Simulator system consists of the GLEAM Server and the GLEAMviz Client application.

 

The GLEAM Server uses GLEAM as the engine to perform the simulations. This server runs on high-performance computers managed by the GLEAM project.

 

The GLEAMviz Client is a desktop application through which users interact with the GLEAM Server. It provides a simple, intuitive and visual way to set up simulations, develop disease models, and evaluate simulation results using a variety of maps, charts and data analysis tools.

 

 

 Visualisation and analysis

 

GLEAMviz offers three types of visualization. The first shows the spread of the infection on a zoomable 2D map while charts show the number of new cases at various levels of detail.

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Time varying networks and the weakness of strong ties | #patterns #rumor #SNA

Time varying networks and the weakness of strong ties | #patterns #rumor #SNA | Influence et contagion | Scoop.it

In most social and information systems the activity of agents generates rapidly evolving time-varying networks. The temporal variation in networks' connectivity patterns and the ongoing dynamic processes are usually coupled in ways that still challenge our mathematical or computational modelling. Here we analyse a mobile call dataset and find a simple statistical law that characterize the temporal evolution of users' egocentric networks. We encode this observation in a reinforcement process defining a time-varying network model that exhibits the emergence of strong and weak ties. We study the effect of time-varying and heterogeneous interactions on the classic rumour spreading model in both synthetic, and real-world networks. We observe that strong ties severely inhibit information diffusion by confining the spreading process among agents with recurrent communication patterns. This provides the counterintuitive evidence that strong ties may have a negative role in the spreading of information across networks.

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#CollectiveIntelligence in Videogames | #ludique #simulation

#CollectiveIntelligence in Videogames | #ludique #simulation | Influence et contagion | Scoop.it

Via Claude Emond
luiy's insight:

“Some games (both single-player and massive multiplayer) already include complex economic systems as part of their design mechanic. It would be easy to embed real-world, real-time financial data (such as commodity prices) within those games. Companies could track how players react to the data, then aggregate the reactions to predict real-world economic events accordingly. And, as massive multiplayer games with rich, dynamic economies become increasingly popular, opportunities to learn from player behavior will be enhanced accordingly.” (Edery, 2006)

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Claude Emond's curator insight, March 5, 2014 7:45 PM

Let's play I love bees ! :)

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Selection Effects in Online #Sharing: Consequences for Peer Adoption | #contagion

luiy's insight:

Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decision process. In online systems, product designers can define how peer exposure mechanisms work: adoption behaviors can be shared in a passive, automatic fashion, or occur through explicit, active sharing. The consequences of these mechanisms are of substantial practical and theoretical interest: passive sharing may increase total peer exposure but active sharing may expose higher quality products to peers who are more likely to adopt. 


We examine selection effects in online sharing through a large-scale field experiment on Facebook that randomizes whether or not adopters share Offers (coupons) in a passive manner. We derive and estimate a joint discrete choice model of adopters' sharing decisions and their peers' adoption decisions. Our results show that active sharing enables a selection effect that exposes peers who are more likely to adopt than the population exposed under passive sharing. 
We decompose the selection effect into two distinct mechanisms: active sharers expose peers to higher quality products, and the peers they share with are more likely to adopt independently of product quality. Simulation results show that the user-level mechanism comprises the bulk of the selection effect. The study's findings are among the first to address downstream peer effects induced by online sharing mechanisms, and can inform design in settings where a surplus of sharing could be viewed as costly.

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