An adaptive computational network model for strange loops in political evolution in society

Julia Anten, Jordan Earle, Jan Treur*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Abstract

In this paper a multi-order adaptive temporal-causal network model is introduced to model political evolution. The computational network model makes use of Hofstadter’s notion of a Strange Loop and was tested and validated successfully to reflect political oscillations seen in presidential elections in the USA over time.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2020
Subtitle of host publication20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II
EditorsValeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Peter M.A. Sloot, Peter M.A. Sloot, Peter M.A. Sloot, Jack J. Dongarra, Sérgio Brissos, João Teixeira
PublisherSpringer
Pages604-617
Number of pages14
Volume2
ISBN (Electronic)9783030504175
ISBN (Print)9783030504168
DOIs
Publication statusPublished - 2020
Event20th International Conference on Computational Science, ICCS 2020 - Amsterdam, Netherlands
Duration: 3 Jun 20205 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12138 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Computational Science, ICCS 2020
CountryNetherlands
CityAmsterdam
Period3/06/205/06/20

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