Learning Opinion Dynamics from Data

The foundation of my doctoral thesis is the estimation of agent-based models (ABMs) that simulate opinion dynamics using a likelihood-based method. I establish that the principles governing ABMs can be transformed into equivalent probabilistic generative models that facilitate a well-defined likelihood function. Consequently, I have incorporated these models into an automatic differentiation framework, which simplifies the process and improves the efficiency of performing maximum likelihood estimation through gradient descent techniques.