Do Ideas, Products, Messages, and Behaviors Really Spread Just Like Viruses?

Wednesdays@NICO Seminar, Noon, April 9, 2014, Chambers Hall, Lower Level

Julia Poncela-Casasnovas, McCormick School of Engineering and Applied Science

Do Ideas, Products, Messages, and Behaviors Really Spread Just Like Viruses?

Abstract
Adoption of innovations, whether new ideas, technologies, or products, is crucially important in knowledge societies. Studies of adoption of innovations have generally focused on products with little societal impact (such as online apps) and, even if large-scale and real-world based, on heterogeneous populations. These limitations have so far hindered the development and testing of a mechanistic understanding of the adoption process. In this work, we experimentally study the adoption by critical care physicians of a medical innovation that complements current protocols for the diagnosis of life-threatening bacterial infections. We show through computational modeling of the experiment that infection spreading models – which have been formalized as generalized contagion processes – are not consistent with the experimental data. Instead, we find that a new “persuasion” model inspired by opinion models is better able to reproduce the empirical data, providing insight into the mechanism of innovation adoption within this homogeneous population of highly-trained professionals. Using our model, we also propose an intervention scheme and show its possible impact on increasing the rate and robustness of innovation adoption in the real-world.

Bio
I am a physicist by training, and I got my Ph.D. at University of Zaragoza (Spain) in 2010. There I did a computational study on the interplay between network structure and the outcome of cooperation dynamics, and I also developed some models of growing networks based on an Evolutionary Preferential Attachment mechanism, in which the growth was entangled with the dynamics. My research interests include Statistical Physics, Non-Linear Physics and Complex Systems in general, and more specifically, different dynamical processes on top of complex topologies, such as human behavior on social networks. I joined the Amaral Lab on November 2010 as a postdoc, and ever since I have been working on analyzing several social systems using computational and statistical methods. Apart from the one I will be talking about, other of my projects is a study of an online community for people that want to control their weight, where I am trying to understand the correlations between network structure and individual’s health outcomes.