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Selfish Algorithm and Emergence of Collective Intelligence
arXiv - CS - Multiagent Systems Pub Date : 2020-01-03 , DOI: arxiv-2001.00907
Korosh Mahmoodi, Bruce J. West and Cleotilde Gonzalez

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.

中文翻译:

自私算法与集体智慧的出现

我们提出了一个模型来证明自私个体代理的集体智能行为的自发出现。代理的行为使用我们提出的自私算法($SA$)和三种学习机制进行建模:强化学习($SAL$)、信任($SAT$)和连接($SAC$)。这些机制中的每一种都提供了一种截然不同的方式,代理可以通过与其他代理玩囚徒困境博弈 ($PDG$) 来增加个人收益。$SA$ 提供了自组织时间临界性 ($SOTC$) 模型的概括,并表明自利的个人可以同时从他们的决定中产生最大的社会利益。$SA$ 中的机制由内部动态自行调整,没有预先建立的网络结构。我们的结果证明了相互合作的出现、动态网络的出现以及社会系统在扰动后的适应和恢复能力。讨论了 $SA$ 的含义和应用。
更新日期:2020-01-06
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