#  Automated Market Design via Reinforcement Learning 

 



####  calendar\_today Date and Time 

 **April 1, 2022** 

 01:00PM - 02:30PM EDT 

####  pin\_drop Location 

 **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 stage. The rules of the sequential-pricing stage—and in particular the way these rules use messages—determine the way the messaging stage is used. This is a Stackelberg game where the designer is the leader and fixes the mechanism rules, inducing an equilibrium amongst agents (the followers). I will introduce a novel single-agent Stackelberg MDP formulation, where the leader learns to effect a follower equilibrium that optimizes its objective. In the last part of my talk, I will show how the Stackelberg MDP framework can be used to design economic platforms that prevent collusive behaviors of AI pricing algorithms.

 

 



 

 See also:- [ Spring 2022 EconCS Seminars ](/taxonomycalendarseminar/seminars-2022-spring)
 
 

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