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.
