Community formation in agent based models of societies - Pereira, Felippe Alves (2020)
In this work we present an agent based model for community for- mation on societies from the dynamics for opinion exchange and dis- trust between agents. The development framework relies on Proba- bility Theory, Machine Learning and MaxEnt principles, from which we derive a new form of Entropic Dynamics for Information Process- ing Systems, in particular for simple Neural Networks, the Entropic Learning Dynamics. The resulting theory and model for agents interactions are an- alyzed in a few scenarios, chosen due to their intuitive nature and connection with possible real scenarios. We started the analysis with the properties of systems with 2 agents interacting under different trust and opinion initial conditions, and showed that the dynamics is not trivial nor leads to results with absurd interpretations. Then, we analyzed the properties of societies with many agents, varying the distribution of opinions and distrust, as well as the subjects they could discuss, and found different situations leading to consensus, polarization and even frustrated state like a spin glass. Finally, we applied the model to study the behavior of judges due the availability of data regarding the influence of political party ideology in the voting patterns of judges in the U.S Court of Appeals. In this application, although just a caricature aiming just to provide a quantitative tool for experts in the field, we tried to mimic the typical situations a panel of three judges would be submitted, attributing to agents representing judges a common knowledge of the Law, a Party bias, a Personality and exposing them to different distrust scenarios. The only scenario capable of reproducing the available data had to consider similar contributions of the Law, Party bias and Personality, as well as having Courteous and Certain judges, who extended the courtesy of attributing low distrust to agents of the opposing political party.