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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Moral foundations in an interacting neural networks society: A statistical mechanics analysis

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Author(s):
Vicente, R. [1] ; Susemihl, A. [2] ; Jerico, J. P. [3] ; Caticha, N. [3]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Inst Matemat & Estat, Dept Appl Math, BR-05508090 Sao Paulo - Brazil
[2] Tech Univ Berlin, Artificial Intelligence Grp, D-10587 Berlin - Germany
[3] Univ Sao Paulo, Inst Fis, Dep Fis Geral, BR-05315970 Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 400, p. 124-138, APR 15 2014.
Web of Science Citations: 5
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 the 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 neighbourhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents when disagreeing. We consider a family of learning algorithms parametrized by 3, 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 8 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 sustain less opinion diversity. We discuss predictions of the model and propose possible experimental tests. (C) 2014 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 07/06122-0 - Invariant Pattern Recognition
Grantee:Alex Kunze Susemihl
Support Opportunities: Scholarships in Brazil - Master