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第404行： − 参考文献表明，当一个系统中的个体表现出不同程度的理性时，改善整个系统的理性可能是无标度网络出现的进化原因。他们通过对一个最初的随机网络施加进化压力来模拟一系列经典博弈，从而使网络收敛到纳什均衡，同时允许重新连接来证明这一点。在这个过程中，网络变得越来越无标度。+ 第692行：
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→Other ways of characterizing evolving networks
概率 1-p-q-r: 添加一个节点。
概率 1-p-q-r: 添加一个节点。
==Other ways of characterizing evolving networks==
==Other ways of characterizing evolving networks 描述演化网络的其他方法==
In addition to growing network models as described above, there may be times when other methods are more useful or convenient for characterizing certain properties of evolving networks.
In addition to growing network models as described above, there may be times when other methods are more useful or convenient for characterizing certain properties of evolving networks.
===Convergence towards equilibria===
===Convergence towards equilibria 趋向均衡===
In networked systems where competitive decision making takes place, game theory is often used to model system dynamics, and convergence towards equilibria can be considered as a driver of topological evolution. For example, Kasthurirathna and Piraveenan <ref>{{cite journal
In networked systems where competitive decision making takes place, game theory is often used to model system dynamics, and convergence towards equilibria can be considered as a driver of topological evolution. For example, Kasthurirathna and Piraveenan <ref>{{cite journal
在竞争性决策发生的网络系统中，博弈论经常被用来建立系统动力学模型，趋向均衡可以被认为是拓扑进化的驱动力。例如，Kasthurirathna 和 Piraveenan 参考{ cite journal
在竞争性决策发生的网络系统中，博弈论经常被用来建立系统动力学模型，趋向均衡可以被认为是拓扑进化的驱动力。例如，Kasthurirathna 和 Piraveenan 参考文献表明，当一个系统中的个体表现出不同程度的理性时，改善整个系统的理性可能是无标度网络出现的进化原因。他们通过对一个最初的随机网络施加进化压力来模拟一系列经典博弈，从而使网络收敛到纳什均衡，同时允许重新连接来证明这一点。在这个过程中，网络变得越来越无标度。
参考{ cite journal
|last1=Kasthurirathna|first1=Dharshana
|last1=Kasthurirathna|first1=Dharshana
|volume=In Press |date=2015}}</ref> have shown that when individuals in a system display varying levels of rationality, improving the overall system rationality might be an evolutionary reason for the emergence of scale-free networks. They demonstrated this by applying evolutionary pressure on an initially random network which simulates a range of classic games, so that the network converges towards Nash equilibria while being allowed to re-wire. The networks become increasingly scale-free during this process.
|volume=In Press |date=2015}}</ref> have shown that when individuals in a system display varying levels of rationality, improving the overall system rationality might be an evolutionary reason for the emergence of scale-free networks. They demonstrated this by applying evolutionary pressure on an initially random network which simulates a range of classic games, so that the network converges towards Nash equilibria while being allowed to re-wire. The networks become increasingly scale-free during this process.
{} / ref
{} / ref
==Applications==
==Applications==