[Colloquium] Daniel Halpern-Aggregating Preferences with Limited Queries

[Kristin S Czaplewski via Colloquium]

UNIVERSITY OF CHICAGO
COMPUTER SCIENCE DEPARTMENT
PRESENTS

Daniel Halpern
Harvard University
Ph.D Candidate

Friday, October 4th
11:00 am - 12:00 pm
In Person: John Crerar Library Rm 257

Title: Aggregating Preferences with Limited Queries

Abstract: Social choice theory studies how to aggregate individual preferences into a collective decision for society. Traditionally, this assumes full access to each individual’s complete set of preferences. However, modern online platforms promoting civic participation, such as pol.is<https://urldefense.com/v3/__http://pol.is/__;!!BpyFHLRN4TMTrA!88BACmrwlJ7bpKT2vByCY-6iYW7plBPNqwGPon1Meuene_j0MbigVrdtmz2_LJLij_pYxzb1GaGnmb6P6J42ovYp3OCOe7ciNLe8NA$>, aim to solve social choice problems that do not fit neatly into this framework. These platforms aggregate complex preferences over a vast space of alternatives, rendering it infeasible to learn any individual's preferences completely. Instead, preferences are elicited by asking each user a simple query about a small subset of alternatives. Based on a series of works,   this talk will present a simple model for analyzing what is possible in these scenarios, along with a variety of positive and negative results. Specifically, I will show efficient algorithms that produce representative outcomes with limited queries, as well as lower bound limits on what can possibly be learned in information-theoretic sense and when an exponential number of queries may be required.



Bio: Daniel Halpern is a final-year PhD student at Harvard University advised by Ariel Procaccia. He is supported by an NSF Graduate Research Fellowship and a Siebel Scholarship. His research broadly sits at the intersection of algorithms, economics, and artificial intelligence. Specifically, he considers novel settings where groups of people need to make collective decisions, such as summarizing population views on large-scale opinion aggregation websites, using participant data to fine-tune large language models, and selecting panel members for citizens’ assemblies. In each, he develops provably fair solutions to aggregate individual preferences.

[Daniel Halpern.jpeg]


Host: Haifeng Xu

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