Dear Applied Statistics Workshop Community,
Our next meeting will be on November 15 (12:00 EST). Ashesh Rambachan
presents "From Predictive Algorithms to Automatic Generation of Anomalies"
(joint with Sendhil Mullainathan).
<When>
November 15, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Economic theories often progress through the discovery of anomalies.''
Canonical examples of anomalies include the Allais Paradox and the
Kahneman-Tversky choice experiments, which are constructed menus of
lotteries that highlighted particular flaws in expected utility theory and
spurred the development of new theories for decision-making under risk. In
this paper, we develop algorithmic procedures to automatically generate
such anomalies. Our algorithmic procedures take as inputs an existing
theory and data it seeks to explain, and then generate examples on which we
would likely observe violations of our existing theory if we were to
collect data. As an illustration, we produce anomalies for expected utility
theory using simulated lottery choice data from individuals who behave
according to cumulative prospect theory. Our procedures recover known
anomalies for expected utility theory in behavioral economics and discover
novel anomalies based on the probability weighting function. We conduct
incentivized experiments to collect choice data on our algorithmically
generated anomalies, finding that participants violate expected utility
theory at similar rates to the Allais Paradox and Common Ratio Effect.
While this illustration is specific, our anomaly generation procedures are
general and can be applied in any domain where there exists a formal theory
and rich data that the theory seeks to explain.
The most recent draft can be found here
<https://economics.mit.edu/sites/default/files/inline-files/mr_anomalies.pdf>
.
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu