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  Hysteretic percolation from locally optimal individual decisions

Schröder, M., Nagler, J., Timme, M., & Witthaut, D. (2018). Hysteretic percolation from locally optimal individual decisions. Physical Review Letters, 120(24): 248302. doi:10.1103/PhysRevLett.120.248302.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-98EE-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C9A2-3
Genre: Journal Article

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Schröder, Malte1, Author              
Nagler, J., Author
Timme, Marc1, Author              
Witthaut, Dirk1, Author              
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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 Abstract: The emergence of large-scale connectivity underlies the proper functioning of many networked systems, ranging from social networks and technological infrastructure to global trade networks. Percolation theory characterizes network formation following stochastic local rules, while optimization models of network formation assume a single controlling authority or one global objective function. In socioeconomic networks, however, network formation is often driven by individual, locally optimal decisions. How such decisions impact connectivity is only poorly understood to date. Here, we study how large-scale connectivity emerges from decisions made by rational agents that individually minimize costs for satisfying their demand. We establish that the solution of the resulting nonlinear optimization model is exactly given by the final state of a local percolation process. This allows us to systematically analyze how locally optimal decisions on the microlevel define the structure of networks on the macroscopic scale.

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Language(s): eng - English
 Dates: 2018-06-142018-06-15
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1103/PhysRevLett.120.248302
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Title: Physical Review Letters
Source Genre: Journal
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Pages: 6 Volume / Issue: 120 (24) Sequence Number: 248302 Start / End Page: - Identifier: -