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Complex patterns: from physical to social interactions
Umeå University, Faculty of Science and Technology, Department of Physics.
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Interactions are what gives us the knowledge of the world around us. Interactions on all levels may fundamentally be seen as an exchange of information and a possible response of the same. Whether it is an electron in an electrical field or a handsome dude in a bar responding to a flirtation---interactions make things happen. In this sense we can see that objects without the capability of interacting with each other also are invisible to each other. Chains of pairwise interacting entities can serve as mediators of indirect interactions between objects. Nonetheless, in the limit of no interactions, we get into a philosophical debate whether we actually may consider anything to exist since it can not be detected in any way. Interactions between matter tend to be organized and show a hierarchical structure in which smaller sub-systems can be seen as parts of a bigger system, which in turn might be a smaller part of an even bigger system. This is reflected by the fact that we have sciences that successfully study specific interactions between objects or matter---physics, chemistry, biology, ecology, sociology,... What happens in a situation where all length scales are important? How does the structure of the underlying network of interactions affect the dynamical properties of a system? What network structures do we find and how are they created? This thesis is a physicist's view of collective dynamics, from superconductors to social systems and navigation in city street networks.

Place, publisher, year, edition, pages
Umeå: Fysik , 2006. , 60 p.
Keyword [en]
Complex networks, collective dynamics, statistical physics, random graphs
National Category
Other Physics Topics
Identifiers
URN: urn:nbn:se:umu:diva-801ISBN: 91-7264-090-1 (print)OAI: oai:DiVA.org:umu-801DiVA: diva2:144602
Public defence
2006-06-02, N430, Naturvetarhuset, Umeå Universitet, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2011-04-26Bibliographically approved
List of papers
1. Correlations in networks associated to preferential growth
Open this publication in new window or tab >>Correlations in networks associated to preferential growth
2005 (English)In: Physica Scripta, ISSN 0031-8949, E-ISSN 1402-4896, Vol. 71, no 6, 680- p.Article in journal (Refereed) Published
National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-5183 (URN)10.1088/0031-8949/71/6/018 (DOI)2-s2.0-21544448600 (Scopus ID)
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2017-12-14Bibliographically approved
2. Scaling determination of the nonlinear I-V characteristics for two-dimensional superconducting networks
Open this publication in new window or tab >>Scaling determination of the nonlinear I-V characteristics for two-dimensional superconducting networks
2004 (English)In: Physical Review B Condensed Matter, ISSN 0163-1829, E-ISSN 1095-3795, Vol. 69, no 6, 064515- p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5184 (URN)
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2017-12-14Bibliographically approved
3. Networking genetic regulation and neural computation: directed network topology and its effect on the dynamics
Open this publication in new window or tab >>Networking genetic regulation and neural computation: directed network topology and its effect on the dynamics
2004 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, ISSN 1063-651X, E-ISSN 1095-3787, Vol. 70, no 6, 061908- p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:umu:diva-5185 (URN)10.1103/PhysRevE.70.061908 (DOI)
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2017-12-14Bibliographically approved
4. Networking the seceder model: group formation in social and economic systems
Open this publication in new window or tab >>Networking the seceder model: group formation in social and economic systems
2004 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, ISSN 1063-651X, E-ISSN 1095-3787, Vol. 70, no 3, 036108- p.Article in journal (Refereed) Published
Abstract [en]

The seceder model illustrates how the desire to be different from the average can lead to formation of groups in a population. We turn the original, agent based, seceder model into a model of network evolution. We find that the structural characteristics of our model closely match empirical social networks. Statistics for the dynamics of group formation are also given. Extensions of the model to networks of companies are also discussed.

National Category
Physical Sciences
Identifiers
urn:nbn:se:umu:diva-5186 (URN)10.1103/PhysRevE.70.036108 (DOI)
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2017-12-14Bibliographically approved
5. Modelling the dynamics of youth subcultures
Open this publication in new window or tab >>Modelling the dynamics of youth subcultures
2005 (English)In: JASSS: Journal of Artificial Societies and Social Simulation, ISSN 1460-7425, E-ISSN 1460-7425, Vol. 8, no 3Article in journal (Refereed) Published
Abstract [en]

What are the dynamics behind youth subcultures such as punk, hippie, or hip-hop cultures? How does the global dynamics of these subcultures relate to the individual's search for a personal identity? We propose a simple dynamical model to address these questions and find that only a few assumptions of the individual's behaviour are necessary to regenerate known features of youth culture.

Place, publisher, year, edition, pages
Guildford, U.K.: SimSoc Consortium, 2005
Keyword
Youth Culture, Adolescence, Multiagent Systems, Complex Networks, Social Networks
Identifiers
urn:nbn:se:umu:diva-5187 (URN)
Available from: 2006-05-12 Created: 2006-05-12 Last updated: 2017-12-14Bibliographically approved
6. A network-based threshold model for the spreading of fads in society and markets
Open this publication in new window or tab >>A network-based threshold model for the spreading of fads in society and markets
2005 (English)In: Advances in Complex Systems, ISSN 0219-5259, Vol. 8, no 2-3, 261-273 p.Article in journal (Refereed) Published
Abstract [en]

We investigate the behavior of a threshold model for the spreading of fads and similar phenomena in society. The model is giving the fad dynamics and is intended to be confined to an underlying network structure. We investigate the whole parameter space of the fad dynamics on three types of network models. The dynamics we discover is rich and highly dependent on the underlying network structure. For some range of the parameter space, for all types of substrate networks, there are a great variety of sizes and life-lengths of the fads -- what one see in real-world social and economical systems.

Place, publisher, year, edition, pages
Singapore: World Scientific Pub. Co., 2005
Identifiers
urn:nbn:se:umu:diva-12405 (URN)10.1142/S0219525905000439 (DOI)
Available from: 2007-04-03 Created: 2007-04-03 Last updated: 2017-12-14Bibliographically approved
7. Searchability of networks
Open this publication in new window or tab >>Searchability of networks
2005 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, ISSN 1063-651X, E-ISSN 1095-3787, Vol. 72, no 4, 046117-046125 p.Article in journal (Refereed) Published
Abstract [en]

We investigate the searchability of complex systems in terms of their interconnectedness. Associating searchability with the number and size of branch points along the paths between the nodes, we find that scale-free networks are relatively difficult to search, and thus that the abundance of scale-free networks in nature and society may reflect an attempt to protect local areas in a highly interconnected network from nonrelated communication. In fact, starting from a random node, real-world networks with higher order organization like modular or hierarchical structure are even more difficult to navigate than random scale-free networks. The searchability at the node level opens the possibility for a generalized hierarchy measure that captures both the hierarchy in the usual terms of trees as in military structures, and the intrinsic hierarchical nature of topological hierarchies for scale-free networks as in the Internet.

Place, publisher, year, edition, pages
American Physical Society, 2005
Identifiers
urn:nbn:se:umu:diva-5249 (URN)10.1103/PhysRevE.72.046117 (DOI)
Available from: 2006-08-31 Created: 2006-08-31 Last updated: 2017-12-14Bibliographically approved

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