SEC 1.413 & on Zoom here: https://harvard.zoom.us/j/97692964231?pwd=K05BMEhDNTZtbUhHYkZ5S21qZ2FKQT09#success
Calvin Li, formerly at Uber and the main architect behind their current surge pricing strategies. It will occur at the usual place and the usual time: this Friday, 6/3, at 1 pm ET, in SEC1.413 and streamed on zoom ...
Munyque Mittelmann will be presenting at the EconCS seminar meeting this Friday, 5/13, at 1 pm ET. The seminar will be held Zoom only,accessible at the usual link here. Hope to see you there :).
Ben Edelman (Harvard) will be presenting at the EconCS seminar meeting this Friday, 4/29, at 1 pm ET. The seminar will take place in the usual room, SEC1.413, and will also be streamed on zoom...
Elaine Shi will be presenting at the EconCS seminar meeting this Friday, 4/22, at 1 pm ET. The seminar will be held Zoom only,accessible at the usual link here. Hope to see you there :).
SEC 1.413 & on Zoom here: https://harvard.zoom.us/j/97692964231?pwd=K05BMEhDNTZtbUhHYkZ5S21qZ2FKQT09#success
Speaker: Gianluca Brero
Title: Automated Market Design via Reinforcement Learning
Abstract: In this talk, I will introduce the use of reinforcement learning to design algorithmic markets. First, I will show how (deep) reinforcement learning algorithms can be used to design sequential price mechanisms, where we can assume truthful agents’ behavior. I will then consider the more general class of mechanisms that combine a messaging round with a sequential-pricing...Read more about Automated Market Design via Reinforcement Learning
Yuqing Kong will be presenting at the EconCS seminar meeting this Friday, 3/25, at 1 pm ET. The seminar will be held Zoom only,accessible at the usual link here. Hope to see you there :).
Ruta Mehta will be presenting at the EconCS seminar meeting this Friday, 3/11, at 1 pm ET. The seminar will be held Zoom only,accessible at the usual link here. Hope to see you there :).
SEC 1.413 & on Zoom here: https://harvard.zoom.us/j/97692964231?pwd=K05BMEhDNTZtbUhHYkZ5S21qZ2FKQT09#success
Presenter: Manish Raghavan
Abstract: Online platforms routinely optimize the content they recommend to their users based on behavioral data. At its core, the use of behavioral data is predicated on a revealed preference assumption: users choose what they like; and thus what they choose reveals what they like. And yet, research has repeatedly demonstrated that behavior can be a poor proxy for users’ preferences, exactly because users themselves are conflicted. Indeed, our own intuition tells us that we often make choices in the moment that are inconsistent with our...
