Abstract

Cooperation has been recognized as a fundamental driving force in many natural, social, and economic systems. We investigate whether, given a complex-networked system in which agents (nodes) interact with one another according to the rules of evolutionary games and are subject to failure or death, cooperation can prevail and be optimized. We articulate a control scheme to maximize cooperation by introducing a time tolerance, a time duration that sustains an agent even if its payoff falls below a threshold. Strikingly, we find that a significant cooperation cluster can emerge when the time tolerance is approximately uniformly distributed over the network. A heuristic theory is derived to understand the optimization mechanism, which emphasizes the role played by medium-degree nodes. Implications for policy making to prevent or mitigate large-scale cascading breakdown are pointed out.

Original languageEnglish (US)
Article number045101
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume86
Issue number4
DOIs
StatePublished - Oct 11 2012

Fingerprint

Complex Networks
Tolerance
Evolutionary Game
games
Driving Force
Vertex of a graph
complex systems
death
Breakdown
economics
breakdown
Maximise
Economics
Heuristics
optimization
thresholds
Optimization

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability

Cite this

Optimizing cooperation on complex networks in the presence of failure. / Chen, Yu Zhong; Lai, Ying-Cheng.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 86, No. 4, 045101, 11.10.2012.

Research output: Contribution to journalArticle

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