Moral foundations in an interacting neural networks society

TL;DR

This paper introduces a neural network-based model inspired by moral foundations theory, demonstrating how social influence and corroboration affect opinion diversity and aligning with empirical data on moral and political cognition.

Contribution

It presents a novel statistical mechanics model of interacting neural networks that captures the influence of corroboration on opinion diversity, linking social influence with moral and political data.

Findings

Higher reliance on corroboration reduces opinion diversity.

The model's predictions align with empirical data on moral dimensions and political affiliation.

Simulations show social influence impacts consensus and disagreement levels.

Abstract

The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighborhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents even when disagreeing. We consider a family of learning algorithms parametrized by \delta, that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter \delta at a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustains less opinion diversity. We discuss predictions of the model and propose possible experimental tests.