With the ideas42 Seminar Series, we invite leading scholars to share their insights and what inspires their exploration into human behavior.
Our New York office was pleased to host Daniel Chen, researcher at Toulouse School of Economics. He has a law degree from Harvard Law School and a PhD in economics from Massachusetts Institute of Technology. His research spans several areas: Law and Development, tracing the incentives that led to what are now viewed as human rights violations; Markets and Morality, how market forces interact with normative commitments; Behavioral Judging, social and psychological, economic and political influences on legal ideas and production of justice; Law and Legitimacy, the role of legitimacy in legal compliance; and Demography of Ideas, the economics of interpretation as a source of normative commitments. After giving a talk to the ideas42 team, Daniel was kind enough to share some of his thoughts on behavioral science:
What drew you to behavioral economics?
What drew me to behavioral economics is a combination of wonderful teachers and a long-standing fascination with human behavior. From an inspiring high school teacher who taught literature with a psychological perspective, to a college professor who exposed me to many of the classic psychology experiments and concepts that inform my empirical work today, to the Russell Sage Foundation graduate behavioral economics summer camp that showed me these ideas can be studied with mathematical tools and field data, I found a way to channel childhood interests in math, psychology, and history into my research in behavioral economics.
What would you say is one of the most surprising discoveries about human behavior?
One discovery that has really surprised me is how much judges can be influenced by behavioral factors. While a longstanding literature has documented behavioral factors as being relevant in lab studies, vignettes, or small scale field data, the advent of large datasets on judicial decisions have made it possible to study many findings in the field that were previously documented in the lab (see, for example, our recent work on the gambler’s fallacy and negatively autocorrelation in judicial decision-making). What has concerned me more recently is the juxtaposition of these findings and the argument that laboratory findings might be observed outside the lab when decision-makers are nearly indifferent between options. Such a perspective may shed light on recent discussions of legitimacy in law and perceived indifference of lawmakers. I call it “revealed preference indifference.” But if a judge is indifferent, there may be adverse downstream consequences for individuals who come to expect recognition and respect. Loss aversion with respect to that reference point can lead to anger. I’ve been thinking about how behavioral economics theory can help operationalize contemporary discussions of legitimacy, law, and recognition-respect.
In your breadth of work, tell us about one of your favorite projects related to behavioral economics and what you see as its unique value.
One of my favorite projects is developing revealed preference methods to reflect on the classic divide between law and economics—the consequentialist view that optimal legal policies should be calculated from costs and benefits vs. the deontological view that there are duties and from the duties one derives what is fair and just. I begin with the behavioral question: Are there deontological motivations, apart from the consequentialist ones? I’ve been exploring this distinction theoretically, experimentally, and empirically in different domains. For example, methods of collecting data in experimental economics and political surveys can be biased if individuals have non-consequentialist motives. Methods to detect these motives with revealed preference can relate to perceived legitimacy of law, sometimes defined as that which motivates legal compliance irrespective of likelihood of sanction. Methods to model these motives can help shed light on EU integration policy, desertion during war, and spurring of Irish desertion in response to the British executions in World War I. A recent literature review in the Journal of Economic Literature suggests that theoretical models do not have a good way to model these motives. Agents choose between quantities (in Chicago models), but do not have preferences over choices separate from preferences over quantities. Or agents choose between acts (in identity models), but do not have preferences over acts separate from preferences over consequences of acts. In my reading, economic models have thus far focused on hypothetical imperatives–preferences over acts because of their consequences–rather than what some like Kant would call categorical imperatives–preferences over acts regardless of their consequences. Distinguishing these motives is challenging in normal circumstances. A new project has been to examine the consequences of consequentialist reasoning on the American judiciary.
What have you learned in applying behavioral insights that has changed the way you work?
What has been critical to working with diverse research teams has been to recognize individuals’ different preferences and talents, which often begins with asking questions. Recognition-respect is pretty important to research collaborations. This can also mean being flexible in following leads, which can often result in later surprises on the deeper conceptual connections when one takes stock.
How do you use behavioral insights in your daily life (or recommend that people use behavioral insights in their daily lives)?
I think that realizing that individuals have different reference points can go toward increasing cooperation, trust, recognition, and respect. Our research group has developed oTree, originally as a programming tool for developing economic experiments, perhaps a tool for eliciting reference points. It’s pretty easy to program, shortening by 90% the time that we previously spent to develop experiments. It’s possible to use in secondary school education (fifteen minutes to program a public goods “app”), and we’re thinking how teaching behavioral economics might increase cooperation, trust, recognition, and respect.
