| KEYWORD |
Automatica
Incentivizing innovation diffusion in social systems
keywords COMPLEX NETWORKS, GAME THEORY, NETWORK DYNAMICS, OPTIMIZATION, SOCIAL NETWORKS, OPINION DYNAMICS, DATA ANALYTICS
Reference persons ALESSANDRO RIZZO, LORENZO ZINO
Research Groups Automatica
Description Innovation diffusion is a fundamental process for societal growth and development. Hence, understanding how to unlock such a process is key toward devising policies encouraging the adoption of new practices, e.g., sustainable innovations. DIfferent types of mathematical models have been proposed and used to investigate such a probelm.
In this thesis, we study a recently-proposed mathematical model based on network coordination games to investigate such a problem. The proposed model incorporates some key interventions to incentivize the adoption of the innovation: i) providing a direct advantage for adopting it, ii) making people sensitive to emerging trends at the population level, and iii) increasing the visibility of adopters of the innovation, respectively. The goal of this thesis is to investigate the model using analytical methods and simulations to design and optimize the intervention strategies.
For more information, we refer to the following publication:
Lorenzo Zino, Mengbin Ye, Ming Cao, Facilitating innovation diffusion in social networks using dynamic norms, PNAS Nexus, Volume 1, Issue 5, November 2022, pgac229, available at https://doi.org/10.1093/pnasnexus/pgac229
See also https://doi.org/10.1093/pnasnexus/pgac229
Required skills Knowledge of dynamical systems
Knowledge of optimization
Deadline 06/05/2025
PROPONI LA TUA CANDIDATURA