The suite of open source software tools known as “oTree” makes it simple to conduct behavioral experiments online or in laboratories. (The word “Tree” in the name refers to decision trees, and the prefix “o” stands for “open.”) Without the need for sophisticated programming, researchers can easily build and run games on oTree that test all kinds of hypotheses about human decision-making. This grant funds a project by Toulouse economics professor Daniel Chen to expand oTree’s capabilities. Planned improvements include handling large-scale experiments, supporting continuous-time games, integrating oTree with other open source tools, improving documentation, diversifying its users and funders, and enhancing its long-term sustainability.  

This grant supports a project by Caltech economist Colin Camerer to use insights from neuroscience to develop better predictions and explanations of consumer behavior. Camerer is developing, testing, and applying neuro-economic models of how people switch between behaviors that are habit-driven or routine on the one hand and behaviors that are goal-seeking and deliberative on the other—with particular focus on measuring the differences in price elasticities associated with one type of behavior vs. the other. Camerer will test the predictions of his model against a meta-analysis of previous results as well as in a field experiment using vending machines to measure economic variables, including price and quantity responses, and psychological variables, including response times and attention patterns.

The Simons Institute for the Theory of Computing at the University of California, Berkeley regularly devotes a semester to a given research topic, inviting interested researchers to make progress on the selected topic by either visiting regularly or taking up residence. This grant supports a semester at the Simons Institute devoted to advancing the theory and practice of data privacy. Funds will support visitors, events, and projects covering three themes: foundations of data privacy; interactions with other areas, such as statistics and geometry; and socio-technical aspects of data privacy—including modern privacy regulation, practical deployment challenges, and fairness, accountability, and transparency (FAT) issues. Program participants will include 23 senior visitors, 8 postdoctoral fellows, and over 20 graduate students. Expected outputs from this grant include a series of academic papers published by collaborating attendees and a white paper that describes findings and their implications for policy and practice.

An “order book” is a list of various traders’ buy or sell instructions for a given financial instrument.  A stock exchange uses such an order book to keep track of how many shares are being bid or offered at each potential price point.  That information, in turn, determines the actual price quoted by the exchange at any moment in time.  This grant funds work by John Liechty and Mark Flood to study the behavior of traders when they send messages to a financial exchange for inclusion in an order book. The researchers will model how trader behavior depends on available information and attentiveness, exploring how asymmetries in these qualities can have dramatic effects.  Liechty and Flood will also focus very specifically on whether detailed order-book data could help financial regulators predict or mitigate systemic market failures. 

Funds from this grant support research by Josh Wright, working in concert with economists Sendhil Mullainathan of the University of Chicago and Susan Athey of Stanford, to test innovative new machine learning techniques in economics field experiments. The group intends to investigate whether machine learning can improve randomly controlled trials in two ways. First, can machine learning enhance the assignment of subjects to control and treatment groups in ways that can lower necessary sample size without sacrificing rigor? Second, can machine learning techniques expand our ability to identify and analyze heterogenous treatment effects? Wright and his team will deploy state-of-the-art machine learning techniques in a series of actual economic field experiments and then share their findings via conferences, talks, and papers.