Emergent Collective Properties in Societies of Neural Networks - Simões, Lucas Silva (2018)
This project deals with the study of the social learning dynamics of agents in a society. For that we employ techniques from statistical mechanics, machine learning and probability theory. Agents interact in pairs by exchanging for/against opinions about issues using an algorithm constrained by available information. Making use of a maximum entropy analysis one can describe the interacting pair as a dynamics along the gradient of the logarithm of the evidence. This permits introducing energy like quantities and approximate global Hamiltonians. We test different hypothesis having in mind the limitations and advantages of each one. Knowledge of the expected value of the Hamiltonian is relevant information for the state of the society, inducing a canonical distribution by maximum entropy. The results are interpreted with the usual tools from statistical mechanics and thermodynamics. Some of the questions we discuss are: the existence of phase transitions separating ordered and disordered phases depending on the society parameters; how the issue being discussed by the agents influences the outcomes of the discussion, and how this reflects on the overall organization of the group; and the possible different interactions between opposing parties, and to which extent disagreement affects the cohesiveness of the society.