集体行为的熵基础

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摘要翻译：
在本文中，我们对智能主体群体的集体行为提出了一个全新的观点。我们首先开发了一个高度通用的抽象模型，用于这些智能体由于其决策而可能遇到的未来生活。在这些可能的未来的背景下，我们表明因果熵原理，即代理人在未来的所有道路上遵循使其熵最大化的行为规则，预测了人类和动物群体中个体之间社会互动的许多观察到的特征。我们的结果表明，智能体通常能够通过保持作为一个群体的内聚性来最大化其未来路径熵，这种内聚性导致集体智能结果，这些结果强烈依赖于可能的未来路径数量的分布。我们导出了在离散和连续决策空间中符合最大熵群行为的社会互动规则。我们的分析进一步预测，社会互动可能从根本上基于韦伯对比例刺激的反应定律，支持了许多为这种刺激-反应机制找到神经基础的研究，并为集体行为模拟研究中线性相加的“社会力量”这一常见假设提供了新的基础。
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英文标题：
《The entropic basis of collective behaviour》
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作者：
Richard P. Mann and Roman Garnett
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最新提交年份：
2014
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

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英文摘要：
  In this paper, we identify a radically new viewpoint on the collective behaviour of groups of intelligent agents. We first develop a highly general abstract model for the possible future lives that these agents may encounter as a result of their decisions. In the context of these possible futures, we show that the causal entropic principle, whereby agents follow behavioural rules that maximise their entropy over all paths through the future, predicts many of the observed features of social interactions between individuals in both human and animal groups. Our results indicate that agents are often able to maximise their future path entropy by remaining cohesive as a group, and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of future paths that are possible. We derive social interaction rules that are consistent with maximum-entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber's law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus-response mechanism, and providing a novel basis for the common assumption of linearly additive 'social forces' in simulation studies of collective behaviour.
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PDF链接：
https://arxiv.org/pdf/1409.6895

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关键词：Quantitative interactions Mathematical distribution epidemiology entropic 智能 collective future 反应