The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Heterophilious Dynamics Enhances Consensus
Abstract
We review a general class of models for self-organized dynamics based on alignment. The dynamics of such systems is governed solely by interactions among individuals or “agents,” with the tendency to adjust to their “environmental averages.” This, in turn, leads to the formation of clusters, e.g., colonies of ants, flocks of birds, parties of people, rendezvous in mobile networks, etc. A natural question which arises in this context is to ask when and how clusters emerge through the self-alignment of agents, and what types of “rules of engagement” influence the formation of such clusters. Of particular interest to us are cases in which the self-organized behavior tends to concentrate into one cluster, reflecting a consensus of opinions, flocking of birds, fish, or cells, rendezvous of mobile agents, and, in general, concentration of other traits intrinsic to the dynamics. Many standard models for self-organized dynamics in social, biological, and physical sciences assume that the intensity of alignment increases as agents get closer, reflecting a common tendency to align with those who think or act alike. Moreover, “similarity breeds connection” reflects our intuition that increasing the intensity of alignment as the difference of positions decreases is more likely to lead to a consensus. We argue here that the converse is true: when the dynamics is driven by local interactions, it is more likely to approach a consensus when the interactions among agents increase as a function of their difference in position. Heterophily, the tendency to bond more with those who are different rather than with those who are similar, plays a decisive role in the process of clustering. We point out that the number of clusters in heterophilious dynamics decreases as the heterophily dependence among agents increases. In particular, sufficiently strong heterophilious interactions enhance consensus.
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Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Consensus and Cooperation in Networked Multi-Agent Systems
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007) · Proceedings of the IEEE · doi:10.1109/jproc.2006.887293 · Cited by 10,522
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Statistical physics of social dynamics
Castellano, C., Fortunato, S., & Loreto, V. (2009) · Reviews of Modern Physics · doi:10.1103/revmodphys.81.591 · Cited by 4,119
Self-Organization in Biological Systems
Sneyd, J., Theraula, G., Bonabeau, E., Deneubourg, J.-L., & Franks, N. R. (2001) · doi:10.1515/9780691212920 · Cited by 2,949
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Effective leadership and decision-making in animal groups on the move
Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005) · Nature · doi:10.1038/nature03236 · Cited by 2,753
Statistical Mechanics of money
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Cited by
A tutorial on modeling and analysis of dynamic social networks. Part I
Proskurnikov, A. V., & Tempo, R. (2017) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2017.03.002 · Cited by 553
Javili, A., Morasata, R., Oterkus, E., & Oterkus, S. (2019) · Mathematics and Mechanics of Solids · doi:10.1177/1081286518803411 · Cited by 312
A tutorial on modeling and analysis of dynamic social networks. Part II
Proskurnikov, A. V., & Tempo, R. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.03.005 · Cited by 294
Metadata
- Authors
- Motsch, S., & Tadmor, E.
- Venue
- SIAM Review
- Published
- 2014-01
- Volume
- 56
- Issue
- 4
- Pages
- 577-621
- ISSN
- 0036-1445 (ISSN-L)
- 1095-7200
- OpenAlex
- W2076451073
- Cited by
- 482
- References
- 8
- FWCI
- 20.44
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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