GISC - Research topics in socioeconomic systems, game theory and related subjects
Evolutionary game theory in structured populations
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How can cooperative acts evolve among populations of selfish individuals? In this scenario each simulation agent is an individual who interacts with others playing a repeated game, e.g. the Prisoner's Dilemma (PD) or the Public Goods Game (PGG). Using numerical simulations, we measure cooperation levels when the system evolves according to different strategy update rules. We also study how network topologies such as random graphs, social network models, scale-free networks or spatial networks can influence the system dynamics. This game-theoretical framework approximates many socio-economic scenarios that we daily experience when cooperative norms and conventions spread throughout our society. The results of this research line can then be tested in controlled behavioral experiments with human participants.
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Alberto Antonioni
Inference and analysis of mesoscale patterns in signed social networks
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A signed network is a mathematical representation of a social system where relationships can be positive, such as friendship or alliance, or negative, such as rivalry or conflict. These networks capture the coexistence of cooperation and antagonism, often giving rise to complex patterns that go beyond individual interactions. At the mesoscale, groups of nodes can organize into communities, factions, or coalitions. Studying these structures offers insights into how cooperation emerges, why divisions persist, and what shapes the stability of social groups. To analyze them, we draw on tools from graph theory, linear algebra, and statistics, complemented by computational simulations and data analysis. Our approach builds on solid mathematical foundations, such as spectral clustering, and connects them with real-world data to uncover the principles underlying the large-scale organization of complex systems with antagonistic interactions.
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Fernando Díaz Díaz
(Dis)information propagation on Telegram
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Fake news have always propagated in our societies, but the increased connectedness brought by online social media has greatly amplified their reach. But what if disinformation flow on a social network had a distinctive temporal signature that could help us identify it? Or what if we could identify coordinated disinformative campaigns as deviations from a model of spontaneous/organic coordination? Here we aim to at least partially answer these questions on the network of Telegram channels, which has been vastly under-studied. The project can thus involve a mix of large data analysis, mathematical modeling of temporal networks, and numerical simulations.
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