Title : Modélisation stochastique et analyse des dynamiques du financement participatif et de l’influence sociale dans les réseaux en ligne
Place : Salle des thèses d’Avignon Université, au centre-ville d’Avignon.
Date : Tuesday, October 20, 2026, at 2:00PM
The presentation will be held in English.
The jury members are:
- Salah EL AYOUBI, Professor — CentraleSupélec, L2S, France (Reviewer)
- Patrick MAILLÉ, Professor — IMT Atlantique, France (Reviewer)
- Tania JIMÉNEZ, Research Engineer — Avignon University, LIA, France (Examiner)
- Francesco DE PELLEGRINI, Professor — Avignon University, LIA, France (Examiner)
- Hamamache KHEDDOUCI, Professor — Université Lyon 1, France (Examiner)
- Yezekael HAYEL, Professor — Avignon University, LIA, France (PhD Supervisor)
- Vineeth S. VARMA, CNRS
Abstract:
Online platforms have become important systems where individuals interact, exchange information, make decisions, and influence each other over time. Crowdfunding platforms, social networks, recommendation systems, and open-source software platforms are examples of such systems. In these systems, decisions are not only based on personal preferences, but also on social interactions, platform visibility, previous actions, and the structure of the underlying network. As a result, repeated interactions between many users can lead to collective dynamics that evolve over time.
This thesis develops stochastic models and analytical methods to study collective dynamics in networked systems. The main motivation comes from crowdfunding campaigns, where contributors arrive over time, observe the campaign state, and decide whether to participate. The thesis also considers related online systems, including competitive influence propagation in social networks, social learning with heterogeneous rationality, and contributor dynamics in open-source software projects.
Overall, this thesis shows how stochastic modeling, network analysis, approximation methods, and game theory can be combined to study collective behavior in online and networked systems. The results contribute to a better understanding of how individual decisions, social influence, platform mechanisms, and network structure shape the evolution of crowdfunding campaigns, influence processes, learning dynamics, and collaborative communities.