Mira Frick, an Assistant Professor of Economics at Yale, has been awarded a Sloan Research Fellowship. The two-year, $75,000 fellowships are awarded annually by the Alfred P. Sloan Foundation to early-career scholars across several scientific and technical disciplines in recognition of their distinguished performance and unique potential to become leaders in their fields.
Frick is a microeconomic theorist who joined Yale’s economics faculty in 2017. We spoke with her recently about her innovative work, her path to economics, and her experience at Yale.
What is the focus of your research?
I work on theoretical models of how people make decisions and learn, both individually and in interactive settings. A lot of my recent research has been in the burgeoning field of “learning with misspecified models.” This area, and my work, is motivated by growing evidence from psychology and behavioral economics that, in many economic settings, people’s learning is governed by an incorrect, biased, or simplified view of their environment. In a series of papers with my Yale colleague Ryota Iijima and our coauthor Yuhta Ishii at Penn State, we explore how incorporating such biases alters the predictions of traditional economic models of individual and social learning.
What is an example from your recent work?
To give a simple example: Suppose a consumer is interested in buying some new product, but is uncertain about its quality or long-term safety. One way she might try to learn about this is by observing other consumers’ purchasing decisions. But a potential complication in drawing inferences from other consumers’ decisions is that she may not be entirely correct about others’ tastes, risk attitudes, or similar idiosyncratic characteristics. Traditional economic models of learning tend to abstract away from this complication: they assume that consumers are correctly specified about each other’s characteristics.
However, in a recent paper with Ryota and Yuhta, we show that even vanishingly small misperceptions of other people’s characteristics can have stark consequences and lead to dramatic failures of learning. For example, if consumers even slightly underestimate other people’s risk tolerance, we find that they will eventually grow confident in the product’s highest possible safety level – no matter how safe or unsafe the product actually is. Such highly confident but incorrect beliefs can then obviously lead to very inefficient behavior, such as the widespread adoption of unsafe products.
What are the implications of these types of findings?
That people’s learning is governed by some amount of misperception seems unavoidable: The world is highly complex, and even the most sophisticated person will not be able to correctly perceive all aspects of her environment.
At the same time, as economists, we tend to think of our models as parsimonious approximations of reality, so as long as people’s misperceptions are not too severe, we might feel that it is safe to ignore them. One of the implications of my work with Ryota and Yuhta is that this need not be the case: If people are even slightly wrong about their environment, this can lead to failures of learning that are drastic departures from what traditional economic models would predict. This suggests that we should take people’s learning biases seriously, and it motivates more research, both empirical and theoretical, into which biases are relevant in different economic settings and how they change the predictions of traditional models.
Some of my other papers with Ryota and Yuhta ask in which types of economic environments predictions tend to be especially sensitive to people’s learning biases, and how to compare the welfare costs of different learning biases. The results in these papers can potentially help guide policy interventions aimed at mitigating the adverse impact of learning biases.
What is another concrete example of a learning bias you have explored?
In a recent paper with Ryota and Yuhta, we study a bias we term “assortativity neglect.” People tend to have peers who are similar to themselves – in terms of income, political attitudes, etc. – but they may misperceive their peers to be more representative of society than actual. We characterize how this bias affects behavior in society: For example, it can exacerbate socioeconomic disparities in education investment, by generating a gap between rich and poor people’s perceived returns to education. We also show that assortativity neglect can help to explain empirically documented misperceptions of key population characteristics – for instance, how people come to under- or overestimate income inequality or political attitude polarization in their societies.
Switching gears, what led you to economics?
I followed a slightly non-linear path. Growing up in Germany, I really enjoyed math from a young age – so I majored in math and philosophy as an undergraduate, then continued on to a master’s degree and some PhD studies in math. But at the start of my math PhD, out of curiosity, I decided to take some classes in microeconomic theory on the side. It turned out to be a great decision: I fell in love with the way economic theory allows one to apply rigorous and elegant mathematical reasoning to better understand human behavior and interactions – so much so that I ended up switching to an economics PhD and pursuing a career in economic theory. I haven’t looked back since!
How has your experience been as a young professor at Yale?
The economics department at Yale is an exceptionally stimulating and supportive environment, and I feel extremely lucky to be able to start my career here. The department and the Cowles foundation have a long history of excellence in economic theory. Our theory group is one of the largest in the country, with an unusually well-balanced composition in terms of research interests and career stages. I think we even have an almost perfect gender balance – which is very rare in economic theory, where women are still quite under-represented. Our group organizes 4 or 5 different research events every week, and two large conferences per year. This creates an incredibly vibrant research climate, and brings our faculty and PhD students together in a close-knit community that I really enjoy being a part of.
That sounds like a great environment for PhD students as well…
Yes, it absolutely is! I think one thing that stands out about Yale is how much the faculty care about mentoring and advising. As a junior faculty member, I’ve benefited a lot from this myself: my senior colleagues go above and beyond to offer feedback and support. Similarly, the amount of time and effort that our faculty devote to training and advising PhD students is quite exceptional. From countless opportunities to get feedback on early-stage ideas to multiple rounds of job market practice talks, I see our students grow tremendously throughout their time in our PhD program.