IEEE Themes - Social Networks: Dynamic Social Interaction Data

550 views
Download
  • Share
#Social network #Thematic #signal #processing #themes #signal processing #information processing #social networks #sensor networks

Manuel Cerbian of MIT Media Lab presentation explores how humans like to draw inspiration from interactions among a successful group of entities, and then apply it to some other population, aiming to increase their performance. Underlying this methodology is the idea that there is something good to learn from them, reckoning that they have found a good way to maximize their collective wellness. This raises interesting questions: Could they do any better? How much? Which changes could lead to an improvement? To shed light on these questions the presenter considers the ideal of a society as an epistatic mathematical function that individuals know and try to maximize by making multiple decisions simultaneously. The presentation considers collective wellness processes taking place on real, large dynamic social networks in which individuals have the potential to interchange information about their decisions. The paper proposes a novel framework to compare the collective potential of different social networks, as well as to study the role of interaction topology. Further, the topology has a large impact on collective potential, and order of interaction seems to have little but non-negligible importance.

The presentation considers collective wellness processes taking place on real, large dynamic social networks in which individuals have the potential to interchange information about their decisions.

Advertisment

Advertisment