DHB: Financial Markets as an Empirical Laboratory to Study an Evolving Ecology of Human Decision Making

该项目利用来自金融市场的丰富数据集来研究不断发展的人类决策生态。社会主体的分散行为是如何导致全球社会秩序的？这种秩序又是如何随着时间而变化的？在市场背景下进行这项研究有两个主要原因：首先，市场在组织人类行为方面发挥着重要作用，并且因其本身而有趣。其次，这些金融数据集的丰富性为寻找决策策略集中的模式提供了独特的机会，使研究它们之间的相互作用并在进化的时间跨度中跟踪它们成为可能。本项目研究的数据来自五家不同的证券交易所，它们的交易和价格形成规则各不相同，这使得研究机构在决定行为方面的作用成为可能。它们包括数百亿条详细记录，使得以高度的统计精度和时间分辨率识别和区分人类行为模式成为可能。大多数数据集包括代理的行为（购买、出售或取消订单）以及价格和交易量。这使得研究代理人行为与市场反应之间的相互作用成为可能。它们中的许多都有标识符，用机构和交易账户标记每个操作。这使得对战略行为异质性的研究和分类成为可能。数据集涵盖了很长的时间跨度，长达11年，这使得研究策略的演变和相互作用的时间尺度比我们预期的策略变化的时间尺度更长。调查人员也有公共新闻公告的记录，这使得研究信息到达和代理人反应之间的关系成为可能。他们称他们对这些数据建模的方法为经验行为建模。通过观察与感兴趣现象直接相关的环境中的行为，并描述其关键规律，他们构建了简约的智能体模型，其组成部分基于经验。然后，他们使用这些模型对感兴趣的经济现象做出准确的定量预测。通过在我们可以直接观察异质主体行为的环境中构建这样的模型，我们相信我们可以建立的模型既能对市场的运作提供实际的见解，又能对人类决策策略的本质和进化产生更深入的见解。金融策略经历了选择、继承和创新，因此为研究文化演变提供了一个理想的环境（尽管无可否认受到限制）。虽然详细的机制与生物学上的机制完全不同，但研究人员认为这种相似性仍然很强。通过与生物学家密切合作，研究人员应该能够确定这些想法是否对社会科学具有定量预测价值。如果它们在金融市场（策略选择非常多，利他主义等复杂因素很少）没有用处，那么它们在其他地方也不太可能有用。更广泛的影响。该项目采用了一种新的方法，综合了行为经济学、基于主体的建模、进化生物学、生态学和统计物理学的思想。这些方法成功的令人信服的论证可能对整个社会科学的智能体建模产生广泛的影响。该项目的其他影响包括教育和指导本科生、研究生和博士后研究人员。

This project takes advantage of rich data sets from financial markets to study an evolving ecology of human decision making. How do the decentralized actions of social agents result in global social order, and how does this change through time? There are two central reasons for doing this study in the context of markets: First, markets play an important role in organizing human behavior and are interesting for their own sake. Second, the richness of these financial data sets provides a unique opportunity to look for patterns in sets of decision making strategies, making it possible to study their interactions and track them across evolutionary spans of time.The data this project studies are from five different stock exchanges, with a diversity of rules for trading and price formation, making it possible to study the role of institutions in determining behavior. They include tens of billions of detailed records, making it possible to identify and differentiate patterns of human behavior with a high degree of statistical precision and temporal resolution. Most of the data sets include the actions of agents (orders to buy, sell, or cancel) as well as prices and trading volume. This makes it possible to study the interaction between agent behavior and market response. Many of them have identifiers labeling each action with its institution and trading account. This makes it possible to study and classify the heterogeneity of strategic behavior. The data sets range over long spans of time, as long as eleven years, making it possible to study the evolution and interaction of strategies on time scales that are longer than those on which we expect strategies to change. The investigators also have a record of public news announcements, making it possible to study the relationship between information arrival and agent response. They call their approach to modeling these data empirical behavioral modeling. By observing behavior in a context that is directly related to the phenomenon of interest, and characterizing its key regularities, they construct parsimonious agent models whose components are empirically grounded. They then use these models to make accurate quantitative predictions of the economic phenomena of interest. By constructing such models in a setting where we can directly observe heterogeneous agent behavior, we believe we can make models that both give practical insights into the functioning of markets and yield deeper insights into the nature and evolution of human decision making strategies. Financial strategies undergo selection, inheritance and innovation, and thus provide an ideal (though admittedly restricted) setting in which to study cultural evolution. While the detailed mechanisms are all quite different from those in biology, the investigators believe the analogy nonetheless remains strong. By working in close collaboration with biologists the investigators should be able to determine whether these ideas have quantitative predictive value for social science. If they are not useful in financial markets, where the selection of strategies is strong and complicating factors such as altruism are minimal, they are unlikely to be useful elsewhere.Broader impacts. This project uses a new approach that synthesizes ideas from behavioral economics, agent-based modeling, evolutionary biology, ecology, and statistical physics. A convincing demonstration of the success of these methods could have a broad impact on agent modeling throughout social science. Other impacts of this project include educating and mentoring undergraduates, graduate students and postdoctoral researchers.