摘要翻译：
EVOC（文化进化）是一种计算机文化模型，它使我们能够研究各种因素如何影响思想的多样性和有效性，如文化传播的障碍、领导者的存在和选择，或创新与模仿比率的变化。它由基于神经网络的智能体组成，这些智能体发明行动的想法，并模仿邻居的行动。该模型基于一种文化理论，根据这种理论，通过文化进化的不是模因或人工制品，而是产生它们的世界内部模型，它们的进化不是通过达尔文式的竞争排斥过程，而是涉及创新协议交换的拉马克过程。EVOC显示，随着时间的推移，动作的平均适应度增加，而动作的多样性先增加后减少。行动多样性与种群规模和密度呈正相关，与种群间的障碍呈正相关。慢慢地侵蚀边界，通过培养专业化，然后分享适合的动作，在不牺牲多样性的情况下增加适合度。引入一个在整个人群中广播其行动的领导人增加了行动的适应性，但减少了行动的多样性。增加领导者的数量减少了这种影响。正在努力模拟一个从一种文化移民到另一种文化的代理人在提供新思想的同时仍然适应的条件。
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英文标题：
《Modeling Cultural Dynamics》
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作者：
Liane Gabora
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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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：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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英文摘要：
  EVOC (for EVOlution of Culture) is a computer model of culture that enables us to investigate how various factors such as barriers to cultural diffusion, the presence and choice of leaders, or changes in the ratio of innovation to imitation affect the diversity and effectiveness of ideas. It consists of neural network based agents that invent ideas for actions, and imitate neighbors' actions. The model is based on a theory of culture according to which what evolves through culture is not memes or artifacts, but the internal models of the world that give rise to them, and they evolve not through a Darwinian process of competitive exclusion but a Lamarckian process involving exchange of innovation protocols. EVOC shows an increase in mean fitness of actions over time, and an increase and then decrease in the diversity of actions. Diversity of actions is positively correlated with population size and density, and with barriers between populations. Slowly eroding borders increase fitness without sacrificing diversity by fostering specialization followed by sharing of fit actions. Introducing a leader that broadcasts its actions throughout the population increases the fitness of actions but reduces diversity of actions. Increasing the number of leaders reduces this effect. Efforts are underway to simulate the conditions under which an agent immigrating from one culture to another contributes new ideas while still fitting in.
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PDF链接：
https://arxiv.org/pdf/0811.2551