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摘要翻译：
我们讨论了如何通过弥合两个现有但不完整的市场模型之间的差距来构建最小化金融市场模型：一个模型中，一群虚拟交易者基于共同的全局信息做出决策，但缺乏来自其社会网络的局部信息；一个模型中，交易者形成一个动态发展的社会网络，但缺乏基于全局信息的任何决策。我们表明，这两个模型的适当组合--特别是一群既能获得全球信息又能获得当地信息的虚拟交易者--产生的价格回报分布结果更接近于所报告的程式化事实。我们相信，这类模型可以应用于观察到人类集体活动的广泛系统。
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英文标题：
《Computational modeling of collective human behavior: Example of
financial markets》
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作者：
Andy Kirou, Blazej Ruszczycki, Markus Walser and Neil F. Johnson
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最新提交年份：
2008
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Physics 物理学
二级分类：Computational Physics 计算物理学
分类描述：All aspects of computational science applied to physics.
应用于物理学的计算科学的各个方面。
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英文摘要：
We discuss how minimal financial market models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the traders form a dynamically evolving social network but lack any decision-making based on global information. We show that a suitable combination of these two models -- in particular, a population of virtual traders with access to both global and local information -- produces results for the price return distribution which are closer to the reported stylized facts. We believe that this type of model can be applied across a wide range of systems in which collective human activity is observed.
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PDF链接：
https://arxiv.org/pdf/0812.2603
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