Chair for Network Dynamics, Center for Advancing Electronics Dresden (cfaed) and Institute for Theoretical Physics, Technical University of Dresden, 01069 Dresden, Germany.
Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen, Germany.
Phys Rev Lett. 2018 Jun 15;120(24):248302. doi: 10.1103/PhysRevLett.120.248302.
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.
大规模连接的出现是许多网络系统正常运行的基础，这些系统的范围从社交网络和技术基础设施到全球贸易网络。渗流理论描述了遵循随机局部规则的网络形成过程，而网络形成的优化模型则假设存在单一的控制机构或单一的全局目标函数。然而，在社会经济网络中，网络的形成往往是由个体的、局部最优的决策驱动的。到目前为止，人们对这些决策如何影响连接性知之甚少。在这里，我们研究了理性主体的个体决策如何从满足其需求的成本最小化出发，从而产生大规模连接。我们证明，由此产生的非线性优化模型的解恰好由局部渗流过程的最终状态给出。这使我们能够系统地分析微观层面上的局部最优决策如何定义宏观层面上的网络结构。
