摘要翻译：
创造力既有好处，也有缺点。在一个社会群体中，并不需要所有成员都具有创造性才能从创造性中受益；有些人只是模仿或享受他人创造性努力的成果。什么比例应该是有创意的？本文包含了一个非常初步的调查，这个问题进行了利用计算机模型的文化进化称为EVOC（文化进化）。EVOC是由基于神经网络的智能体组成，这些智能体通过（1）通过修改现有的想法来发明新的想法，(2)模仿邻居的想法来进化出适合于行动的想法。当30%到40%的人是有创造力的时候，理想的想法适合的比例就会出现。当创造者发明的迭代次数在50%或以下时，社会行为的平均适应度是创造者与模仿者比例的正函数；否则，当创建者和模仿者的比例超过大约30%时，动作的适应度开始下降。对于所有水平或创造力，一个群体中想法的多样性与创造性主体的比率呈正相关。
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英文标题：
《How Creative Should Creators Be To Optimize the Evolution of Ideas? A
  Computational Model》
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作者：
Stefan Leijnen, Liane Gabora
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  There are both benefits and drawbacks to creativity. In a social group it is not necessary for all members to be creative to benefit from creativity; some merely imitate or enjoy the fruits of others' creative efforts. What proportion should be creative? This paper contains a very preliminary investigation of this question carried out using a computer model of cultural evolution referred to as EVOC (for EVOlution of Culture). EVOC is composed of neural network based agents that evolve fitter ideas for actions by (1) inventing new ideas through modification of existing ones, and (2) imitating neighbors' ideas. The ideal proportion with respect to fitness of ideas occurs when thirty to forty percent of the individuals is creative. When creators are inventing 50% of iterations or less, mean fitness of actions in the society is a positive function of the ratio of creators to imitators; otherwise mean fitness of actions starts to drop when the ratio of creators to imitators exceeds approximately 30%. For all levels or creativity, the diversity of ideas in a population is positively correlated with the ratio of creative agents.
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PDF链接：
https://arxiv.org/pdf/0911.2390