论文标题
自私算法和集体智慧的出现
Selfish Algorithm and Emergence of Collective Intelligence
论文作者
论文摘要
我们提出了一个模型，以证明自私的个人代理人自发出现集体智能行为。代理商的行为是使用我们提出的自私算法（$ sa $）的三种学习机制来建模的：加强学习（$ sal $），信任（$ sat $）和连接（$ sac $）。这些机制中的每一种都提供了一种明显不同的方式，代理可以通过与其他代理商玩囚犯的困境游戏（$ pdg $）来增加个人利益。 $ sa $提供了自组织的时间关键（$ SOTC $）模型的概括，并表明自私的人可以同时从决策中获得最大的社会利益。 $ SA $中的机制是由内部动力学自我调整的，而没有预先建立的网络结构。我们的结果表明，相互合作的出现，动态网络的出现以及扰动后社会系统的适应和弹性。讨论了$ SA $的含义和应用。
We propose a model for demonstrating spontaneous emergence of collective intelligent behavior from selfish individual agents. Agents' behavior is modeled using our proposed selfish algorithm ($SA$) with three learning mechanisms: reinforced learning ($SAL$), trust ($SAT$) and connection ($SAC$). Each of these mechanisms provides a distinctly different way an agent can increase the individual benefit accrued through playing the prisoner's dilemma game ($PDG$) with other agents. The $SA$ provides a generalization of the self-organized temporal criticality ($SOTC$) model and shows that self-interested individuals can simultaneously produce maximum social benefit from their decisions. The mechanisms in the $SA$ are self-tuned by the internal dynamics and without having a pre-established network structure. Our results demonstrate emergence of mutual cooperation, emergence of dynamic networks, and adaptation and resilience of social systems after perturbations. The implications and applications of the $SA$ are discussed.
