Seminars of Interest at Columbia

Monday, October 29th

2:30 pm to 3:45 pm - 1101 IAB
Economic Theory Workshop - Sylvain Chassang (New York University)
Data Driven Regulation: Theory and Application to Missing Bids (with Kei Kawai and Jun Nakabayashi)

Tuesday, October 30th

12:30 pm to 2:00 pm - Uris 307
Macroeconomics Lunch Group - Andy Pham 
Title Not Available

12:30 pm to 1:45 pm - Uris 333
Marketing Seminar - Minkyung Kim (Yale)
A Structural Model of a Multi-tasking Salesforce with Private Information

12:30 pm to 1:30 pm - Uris 327
PhD Student Seminar - Poorya Kabir
Title Not Available

2:15 pm to 3:45 pm 1101 IAB
Industrial Organization and Strategy - Gaston Illanes (Northwestern University)
Title Not Available

4:15 pm to 5:45 pm - 1101 IAB
Money-Macro Workshop - Pedro Teles (Banco de Portugal)
Ramsey Taxation in the Global Economy

Wednesday, October 31st

12:30 pm to 1:45 pm - Uris 326
Finance Free Lunch (Faculty Only) - Margarita Tsoutsoura
Title Not Available

4:15 pm to 5:35 pm - 1101 IAB
Applied Microeconomics: Environment, Health, Labor, and Public Finance Seminar - Adam Isen
Title Not Available

Thursday, November 1st

12:30 pm to 1:45 pm - Uris 142
Finance Seminar - Adi Sunderam (Harvard University)
Title Not Available

12:30 pm to 1:45 pm - Uris 333
Marketing Seminar - Jessica Yu (Stanford)
Title Not Available

12:30 pm to 1:45 pm - Uris 307
Microeconomics Faculty Lunch - Chris Moser
Optimal Paternalistic Savings Policies

Seminars of Interest at NYU

Tuesday, October 30th

2:40 pm to 4:00 pm - NYU Department of Economics, 19 W 4th St., Room 517
Neuroeconomics Colloquium - Alexandre Pouget (University of Geneva)
The Agony of Choice

Article of the Week
Do People Trust Algorithms More Than Companies Realize?
Many companies assume that customers are wary of their algorithms — and they go to great lengths to hide or humanize them. But is it good practice for companies to mask their algorithms in this way? Researchers decided to question this assumption, and a series of studies found that people do not dislike algorithms as much as prior received wisdom might have us believe.