Homo Entropicus, the emotional agent and societies of Neural Networks

TL;DR

This paper introduces Homo Entropicus, an emotional neural agent framework using information theory to model complex societal behaviors, including opinion dynamics, trust, and polarization, through interacting neural networks.

Contribution

It presents a novel neural network-based agent model with entropic learning algorithms that simulate societal interactions and emergent behaviors.

Findings

Agents can model opinion and trust dynamics.

Polarization can precede or follow affective states.

Simpler algorithms alter societal polarization patterns.

Abstract

A neural network with a learning algorithm optimized by information theory entropic dynamics is used to build an agent dubbed Homo Entropicus. The algorithm can be described at a macroscopic level in terms of aggregate variables interpretable as quantitative markers of proto-emotions. We use systems of such interacting neural networks to construct a framework for modeling societies that show complex emergent behavior. A few applications are presented to investigate the role the interactions of opinions about multidimensional issues and trust on the information source play on the state of the agent society. These include the case of a class of $N$ agents learning from a fixed teacher; two dynamical agents; panels of three agents modeling the interactions that occur in decisions of the US Court of Appeals, where we quantify how politically biased are the agents, how trustful of other agents-judges of other parties, how much the agents follow a common understanding of the law. Finally we address under which conditions ideological polarization follows or precedes affective polarization in large societies and how simpler versions of the learning algorithm may change these relations.