ABSTRACT / Adoption processes in socio-technological systems have been widely studied both empirically and theoretically. In this talk, I will focus on network approaches that aim at capturing and modeling the fundamental mechanisms behind the social dynamics of adoption. The adoption of ideas, or behaviors, can be described both as an exploration dynamics on a network of ideas that individuals can adopt (or discover), and as contagion dynamics on a network of individuals influencing one another. I will start by considering an individual (or a community) and model the dynamics of discovery as movements over an a priori existing network of interlinked concepts. Starting from the empirically observed dynamics of correlated novelties, I will present a reinforced-random-walk model [1] in which the exploration of an individual of the space of possible discoveries has the byproduct of influencing also the strengths of their connections. I will then move to interacting discovery processes [2] to investigate how social interactions contribute to the collective emergence of new ideas, and explorers can exploit opportunities (possible discoveries) coming from their social contacts. In the second part of the talk, I will consider a single idea, or behavior, and model its transmission from one individual to another, in an epidemic-like fashion. Recent evidence has shown that mechanisms of complex contagion can effectively capture the fundamental rules of social reinforcement and peer pressure characterizing social systems. Along this line, I'll discuss the idea of complex recovery [3], in which the social influence mechanism acts on the recovery rule rather than on the infection one. Yet, in social systems, interactions can occur in groups of three or more nodes that cannot be simply described in terms of dyads. Thus, I will expand the pairwise representation given by graphs in favor of a non-pairwise one. With this in mind, I will generalize two models of social contagion [4] and norm emergence [5], showing how the inclusion of these higher-order group interactions can dramatically alter the dynamics and lead to the emergence of novel phenomena, such as discontinuous transitions, critical mass effects, and minority takeover.
[1] I. Iacopini, S. Milojević, and V. Latora, “Network dynamics of innovation processes”, Phys. Rev. Lett. 120, 048301 (2018)
[2] I. Iacopini, G. Di Bona, E. Ubaldi, V. Loreto, and V. Latora, “Interacting discovery processes on complex networks”, Phys. Rev. Lett. 125, 248301 (2020)
[3] I. Iacopini, B. Schäfer, E. Arcaute, C. Beck, and V. Latora, “Multilayer modeling of adoption dynamics in energy demand management”, Chaos 30, 013153 (2020)
[4] I. Iacopini, G. Petri, A. Barrat, and V. Latora, “Simplicial models of social contagion”, Nat. Commun. 10, 2485 (2019)
[5] I. Iacopini, G. Petri, A. Baronchelli, A. Barrat, “Vanishing size of critical mass for tipping points in social convention”, arXiv:2103.10411 (2021)
BIO / Iacopo is a physicist with a strong interest in applied sciences. His research deals with Complex Networks, Data Science and their application to the study and modeling of social and urban systems. He joined the Department of Network and Data Science at CEU as a James S. McDonnell Foundation Postdoctoral Fellow. Iacopo received his PhD from the School of Mathematical Sciences at Queen Mary University of London, where he worked within the Complex Systems and Networks Research Group. Iacopo also worked at the Centre de Physique Théorique of Aix-Marseille University, at the Urban Dynamics Lab of the Centre for Advanced Spatial Analysis (CASA), UCL, and at The Alan Turing Institute as part of the enrichment doctoral scheme. Previously, he worked at the ISI Foundation in Turin and as a data science intern at United Nations UPU in Bern.