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Influence et contagion
L'influence et la contagion dans la cyberculture
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Modeling #Emotion #Influence from Images in Social Networks | #SNA

Modeling #Emotion #Influence from Images in Social Networks | #SNA | Influence et contagion |
luiy's insight:

Images become an important and prevalent way to express users' activities, opinions and emotions. In a social network, individual emotions may be influenced by others, in particular by close friends. We focus on understanding how users embed emotions into the images they uploaded to the social websites and how social influence plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of "who influences whom". Employing a real network from Flickr as experimental data, we study the effectiveness of factors in the proposed model with in-depth data analysis. Our experiments also show that our model, by incorporating the emotion influence, can significantly improve the accuracy (+5%) for predicting emotions from images. Finally, a case study is used as the anecdotal evidence to further demonstrate the effectiveness of the proposed model.

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Rescooped by luiy from Complex systems and projects!

Agents of influence

Models of complex systems have become a staple of business strategy, and now they are showing early promise for improving economic forecasts.

Via Philippe Vallat
luiy's insight:

Self-Aware Agents


Busemeyer is helping to develop the theories needed to create “quantum agents” in future models. These would need to contain additional feedback loops, in which some agents’ actions are informed by the existence and output of other agent-based models.


This approach may be particularly suited to the world of high finance. As investors learn more about complexity theory, they become aware of their status as agents in predictive models, and they also run agent-based models to inform their own decision making—just like Busemeyer’s quantum agents.

Ultimately, though, none of these models will offer iron-clad predictions, because they have to make simplifying assumptions about human behavior. The true test will be whether those assumptions, and the resulting outputs of the models, convince policymakers to act on their advice.


“The way that our computational approach will eventually outrun conventional analytical and numerical methods in economics and finance is by having much more supple and succinct representations of human behavior,” says Axtell. But even then, “we don’t want policymakers to simply take the results of the model completely at face value without any use of their own judgment.”

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