arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.00907nlin.AOcs.MA
收藏

集体智能中的自私算法与Emergence

Selfish Algorithm and Emergence of Collective Intelligence

Korosh Mahmoodi, Bruce J. West, Cleotilde Gonzalez

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

我们提出了一个模型，证明自发出现的集体智能行为从自私的个人代理。代理的行为建模使用我们提出的自私算法（$SA$）与三种学习机制：强化学习（$SAL$），信任（$SAT$）和连接（$SAC$）。这些机制中的每一个都提供了一种截然不同的方式，一个代理可以通过与其他代理玩囚徒困境游戏（$PDG$）来增加个人收益。的$SA$提供了一个推广的自组织时间临界性（$SOTC$）模型，并表明，自我利益的个人可以同时产生最大的社会效益，从他们的决定。$SA$中的机制通过内部动力学进行自调谐，并且不具有预先建立的网络结构。我们的研究结果表明，相互合作的出现，动态网络的出现，以及社会系统在扰动后的适应性和弹性。讨论了$SA$的含义和应用。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑