This dissertation is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).
I wrote this dissertation in order to obtain my Master in Physics from the University of São Paulo. This should have some overlap with other repos on my github but less code and (hopefully) better explained.
I've worked my way into writing this dissertation using Markdown+Pandoc+LaTeX/BibTex+what-else-is-needed. For that, I'm grateful for some really nice references! For example:
The code used to generate each figure in the dissertation used julia version 0.6.4 (so definitely out of the date with the most current versions, sorry, maybe adapting to newer versions).
The code for simulations is in a separate repo, here
Sharing the whole codebase is still ongoing, so feel free to ask me if you want anything specific.
This work aims to understand how human beings learn their (moral) opinions from peers in society, and how that generates interesting dynamics at the societal level, e.g. with polarisation when people are trying to agree and learn with one another.
Our1 models are influenced by learning of perceptrons, Moral Foundations Theory and MaxEnt/Entropic Dynamics (a nice introduction is given by Ariel Caticha here). The methods used are broadly (bayesian/entropic) inference methods and statistical physics.
If you'd like to, feel free to send me an email.