Dear Applied Statistics Workshop Community,
Just a quick reminder, our next meeting is Wednesday, April 17 (12:00 EST).
Connor Jerzak presents "Selecting Optimal Candidate Profiles in Adversarial
Environments Using Conjoint Analysis" (Joint with Kosuke Imai).
<When>
April 17, 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>
Conjoint analysis, an application of factorial experimental design, is a
popular tool in social science research for studying multidimensional
preferences. In such experiments in the political analysis context,
respondents are asked to choose between two hypothetical political
candidates with randomly selected features, which can include partisanship,
policy positions, gender and race. We consider the problem of identifying
optimal candidate profiles. Because the number of unique feature
combinations far exceeds the total number of observations in a typical
conjoint experiment, it is impossible to determine the optimal profile
exactly. To address this identification challenge, we derive an optimal
stochastic intervention that represents a probability distribution of
various attributes aimed at achieving the most favorable average outcome.
We first consider an environment where one political party optimizes their
candidate selection. We then move to the more realistic case where two
political parties optimize their own candidate selection simultaneously and
in opposition to each other. We apply the proposed methodology to an
existing candidate choice conjoint experiment concerning vote choice for US
president. We find that, in contrast to the non-adversarial approach,
expected outcomes in the adversarial regime fall within range of historical
electoral outcomes, with optimal strategies suggested by the method more
likely to match the actual observed candidates compared to strategies
derived from a non-adversarial approach. These findings indicate that
incorporating adversarial dynamics into conjoint analysis may yield unique
insight into social science data from experiments.
<2023-2024 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
*https://jialul.github.io/ <https://jialul.github.io/>*