Tracking the Emergence of Cooperation in Complex Networks [Virtual Talk]

[ENG]

It is well known that social networks fundamentally impact the dynamic processes of the populations they support. Examples include epidemic outbreaks, the diffusion of innovations, opinion formation, and behavioral evolution. However, the link between the observed global dynamics, at the population-wide scale, with the local actions taken by individuals can be elusive. Indeed, networks introduce a complex nature to the problem, leading to self-organization, chaos, and emergent patterns that render linear/mean-field extrapolations inaccurate. In this presentation, I will discuss a computational approach that offers a bridge between local and global dynamics, allowing us to obtain a population-wide description of dynamical processes that is sensitive to both the underlying network structure and the individual dynamics played by individuals. In particular, we will focus on the role of social networks in the evolution of cooperation, studied using Evolutionary Game Theory as a framework.

[ESP]

Es bien sabido que las redes sociales influyen de manera fundamental en los procesos dinámicos de las poblaciones que apoyan. Ejemplos de esto incluyen brotes epidémicos, la difusión de innovaciones, la formación de opiniones y la evolución del comportamiento. Sin embargo, la relación entre las dinámicas globales observadas, a escala de toda la población, y las acciones locales tomadas por los individuos puede ser difícil de entender. De hecho, las redes introducen una naturaleza compleja al problema, lo que lleva a la autoorganización, el caos y patrones emergentes que hacen que las extrapolaciones lineales (o tipo campo medio) sean inexactas. En esta presentación, hablaré sobre un enfoque computacional que ofrece un puente entre las dinámicas locales y globales, lo que nos permite obtener una descripción a nivel de población de los procesos dinámicos que es sensible tanto a la estructura de la red subyacente como a las dinámicas individuales llevadas a cabo por los individuos. En particular, nos centraremos en el papel de las redes sociales en la evolución de la cooperación, estudiada utilizando la Teoría de Juegos Evolutivos como marco de referencia. (Traducido por ChatGPT)

Speaker Bio

Dr. Flavio Pinheiro is an Invited Assistant Professor in Data Science at NOVA IMS (Universidade Nova de Lisboa - Information Management School). Prior to his current position, he worked as a postdoctoral associate at the Collective Learning group in the MIT Media Lab. His research involves a combination of theoretical (computer simulations and mathematical modelling of multi-agent systems) and empirical (data extraction and data analysis) approaches to comprehend the effect of the network structure of socio-economic systems on their functioning. Specifically, his research focuses on understanding how these networks affect the dynamical processes underlying the dissemination of information, diseases, opinions, and behaviors, and how these structures restrict agents' decision-making processes when they overlook a networked system and take actions over its elements. Flavio earned his BSc and MSc in Physics from the University of Lisbon (Portugal) and a Ph.D. in Physics from the University of Minho (Portugal). During his Ph.D., he worked closely with the GAIPS/INESC-ID (Lisbon) and was a Visiting Graduate Student at the MIT Media Lab Macro Connections/Collective Learning Group.