The role of the interaction network in the emergence of diversity of behavior

Alan Godoy, Pedro Tabacof and Fernando J Von Zuben

PLOS ONE, 2017, vol. 12, issue 2, 1-32

Abstract: How can systems in which individuals’ inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells’ behaviors in the best cellular automata found—most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0172073 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 72073&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0172073

DOI: 10.1371/journal.pone.0172073

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().