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具有变噪音度的合作多重网络上的部分冲击。

Partial Shocks on Cooperative Multiplex Networks with Varying Degrees of Noise.

机构信息

Information Sciences Institute, University of Southern California, Marina del Rey, California, 90292, USA.

Department of Political Science, University of California, Davis, Davis, California, 95616, USA.

出版信息

Sci Rep. 2018 Sep 11;8(1):13619. doi: 10.1038/s41598-018-31960-y.

Abstract

In many cooperative networks, such as alliance and trade networks, abrupt and intense changes to the state of the system (which we call "shocks"), can substantially change the network. We examine how such shocks affect multiplex networks via an agent-based model, in which agents add, drop, or change ties to increase their utility. At a certain time-point, some agents are "shocked" by changing (increasing or decreasing) the cost associated with tie-formation or tie-maintenance. Our model makes several improvements to previous models, including (a) only a fraction of nodes are shocked to simulate small wars or scattered tariff increases or decreases and (b) agents can make both utility-maximizing decisions and randomly rewire ties to explore the utility landscape. Interestingly, we find that randomly rewiring ties increases the utility of agents, for reasons similar to simulated annealing in physics. Furthermore, we create a novel metric to determine how networks change after a shock and find that the size of a shock and noise significantly changes the network, but only when agents' incentives for tie-formation are sufficiently high. Together, these results suggest that adding more realism to cooperation network models can give nuanced understanding to network shocks.

摘要

在许多合作网络中,如联盟和贸易网络,系统状态的突然和剧烈变化(我们称之为“冲击”)可能会极大地改变网络。我们通过基于代理的模型研究了这种冲击如何通过代理添加、删除或更改关系来增加其效用来影响多重网络。在某个时间点,一些代理通过改变(增加或减少)与关系形成或关系维护相关的成本而受到“冲击”。我们的模型对以前的模型进行了多项改进,包括:(a) 仅对一部分节点进行冲击,以模拟小战争或分散的关税增加或减少;(b) 代理可以做出效用最大化的决策,并随机重新连接关系以探索效用景观。有趣的是,我们发现随机重新连接关系会增加代理的效用,原因类似于物理中的模拟退火。此外,我们创建了一种新的度量标准来确定冲击后网络的变化情况,并发现冲击的大小和噪声会显著改变网络,但只有当代理形成关系的激励足够高时才会发生这种情况。总之,这些结果表明,为合作网络模型增加更多的现实性可以对网络冲击有更细致的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca30/6134050/b30d5ab77cda/41598_2018_31960_Fig1_HTML.jpg

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