英文标题：
《Opinion Dynamics and Price Formation: a Nonlinear Network Model》
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作者：
Marco D\'Errico, Gulnur Muradoglu, Silvana Stefani, Giovanni Zambruno
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最新提交年份：
2014
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英文摘要：
  Opinions and beliefs determine the evolution of social systems. This is of particular interest in finance, as the increasing complexity of financial systems is coupled with information overload. Opinion formation, therefore, is not always the result of optimal information processing. On the contrary, agents are boundedly rational and naturally tend to observe and imitate others in order to gain further insights. Hence, a certain degree of interaction, which can be envisioned as a network, occurs within the system. Opinions, the interaction network and prices in financial markets are then heavily intertwined and influence one another. We build on previous contributions on adaptive systems, where agents have hetereogenous beliefs, and introduce a dynamic confidence network that captures the interaction and shapes the opinion patterns. The analytical framework we adopt for modeling the interaction is rooted in the opinion dynamics problem. This will allow us to introduce a nonlinear model where the confidence network, opinion dynamics and price formation coevolve in time. A key aspect of the model is the classification of agents according to their topological role in the network, therefore showing that topology matters in determining how of opinions and prices will coevolve. We illustrate the dynamics via simulations, discussing the stylized facts in finance that the model is able to capture. Last, we propose an empirical validation and calibration scheme that makes use of social network data.
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中文摘要：
观点和信仰决定了社会制度的演变。这一点在金融领域尤为重要，因为金融系统日益复杂，信息过载。因此，意见形成并不总是最佳信息处理的结果。相反，代理人是非常理性的，自然倾向于观察和模仿他人以获得进一步的见解。因此，系统内会发生一定程度的交互，可以将其想象为一个网络。然后，金融市场中的观点、互动网络和价格严重交织在一起，相互影响。我们建立在先前对适应性系统的贡献之上，在适应性系统中，代理具有异质信念，并引入了一个动态信任网络，该网络捕获交互并塑造意见模式。我们用于建模交互的分析框架植根于观点动力学问题。这将允许我们引入一个非线性模型，其中信心网络、观点动态和价格形成在时间上共同作用。该模型的一个关键方面是根据代理在网络中的拓扑角色对代理进行分类，因此表明拓扑在决定观点和价格如何协同进化方面很重要。我们通过模拟来说明动态，讨论模型能够捕捉到的金融领域的程式化事实。最后，我们提出了一个利用社交网络数据的实证验证和校准方案。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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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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