Call for applications
PhD Grant 2023-2026
Multi-level modeling of differentiation processes and social dynamics: opinions, norms and values

Objective: to understand, from models confronted with empirical data, how individuals’ opinions and preferences interact with the structuring of social networks and groups to generate collective behaviors and social dynamics.

Keywords: computational social science, opinion dynamics, complex systems modeling, web-mining & deep learning, cognitive science.

Abstract: The formal modeling of social phenomena has known several interdisciplinary contributions since the founding work of John Nash on non-cooperative games with contributions from social psychology, sociology, cognitive economics or anthropology. Stylized theoretical models based on statistical physics and dynamical systems theory have addressed the central question of the articulation between individuals and the collective. These works, which formalized in a stylized way the concepts of opinions, norms and values, have only been confronted with real systems in a qualitative way, due to the lack of large-scale data on these phenomena.

The accessibility of massive data made possible by online social networks has changed this situation. Thus, and despite questions about the social segmentation of users of these platforms, it is nevertheless possible to use them to address several questions concerning social dynamics. For example, it is possible to study the formation of consensus, or polarization around a topic of social interest or to evaluate the phenomena of rumor or fake news diffusion.

This thesis project will aim at developing formalisms that can bridge different approaches to social modeling (game theory, statistical physics, dynamical systems, and complex networks) by integrating in a multilevel way the issues of opinion formation, values and norms, intrinsically linked to the personality of social actors, and the social structures in which they evolve (modeled as complex networks). In particular, we propose to study the dynamics of coupling between the processes of morphogenesis of the intrinsic properties of social actors and those of the interaction networks they form, through theoretical and empirical approaches.

The person recruited for this project will be in charge of developing new formalisms for the study of opinion dynamics, at the interface between statistical physics, game theory, agent models, complex network modeling and data science. These models will be fed by knowledge from the main theories of the individual (cognitive sciences) and the social (sociology/anthropology) and will be validated by relying on the solid mass of data and reconstructions from the macroscopes of the Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF) on topics such as French politics, climate and the COVID-19 pandemic.

This thesis is part of an interdisciplinary project funded by the 80PRIME call of the CNRS, which aims to better understand the long-term societal impacts of crises such as pandemics or global warming on our societies, by modeling the processes of collective attention, evolution of values and opinions and more globally, of change of interaction mode in society.

Education and skills expected: Master’s degree in Applied Mathematics, Physics, Computer Science or related. Mastery of programming languages allowing the realization of extensive simulations and the analysis of massive data. Interest in an interdisciplinary approach.

How to apply: Send your CV, cover letter, transcript (L1-M2) and two letters of recommendation to the co-supervisors before May 31, 2023. Selection will be based on an interview after selection on file by June 2023.

David Chavalarias (directeur), CNRS, ISC-PIF & CAMS, ; Laura Hernandez (co-directrice), LPTM, CNRS-CY Cergy-Paris Université,

Place of work : Institut des Systèmes Complexes de Paris Île-de-France, 75013, The person recruited will also benefit from the status of member of the LPTM.

École Doctorale : formation doctorale « Sciences de la Société » co-accréditée EHESS / ENS-PSL.

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