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Digital morphogenesis via Schelling segregation

Barmpalias, George and Elwes, Richard and Lewis-Pye, Andy (2014) Digital morphogenesis via Schelling segregation. Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium. pp. 156-165. ISSN 0272-5428

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Identification Number: 10.1109/FOCS.2014.25

Abstract

Schelling's model of segregation looks to explain the way in which particles or agents of two types may come to arrange themselves spatially into configurations consisting of large homogeneous clusters, i.e. connected regions consisting of only one type. As one of the earliest agent based models studied by economists and perhaps the most famous model of self-organising behaviour, it also has direct links to areas at the interface between computer science and statistical mechanics, such as the Ising model and the study of contagion and cascading phenomena in networks. While the model has been extensively studied it has largely resisted rigorous analysis, prior results from the literature generally pertaining to variants of the model which are tweaked so as to be amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory. In \cite{BK}, Brandt, Immorlica, Kamath and Kleinberg provided the first rigorous analysis of the unperturbed model, for a specific set of input parameters. Here we provide a rigorous analysis of the model's behaviour much more generally and establish some surprising forms of threshold behaviour, notably the existence of situations where an \emph{increased} level of intolerance for neighbouring agents of opposite type leads almost certainly to \emph{decreased} segregation.

Item Type: Article
Official URL: http://www.boazbarak.org/focs14/
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Sets: Departments > Mathematics
Date Deposited: 09 Mar 2015 10:30
Last Modified: 13 Mar 2015 12:47
Projects: 613501-10535
Funders: Royal Society University Research Fellowship, Research Fund for International Young Scientists from the National Natural Science Foundation of China, International Young Scientist Fellowship from the Chinese Academy of Sciences, Network Algorithms and Digital Information, Institute of Software, Chinese Academy of Sciences, Marsden grant of New Zealand
URI: http://eprints.lse.ac.uk/id/eprint/61149

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