There is an increasing practical need to understand the foundations of machine learning in the presence of incentives: i) data ingested by machine learning algorithms are either owned or generated by self-interested parties, ii) machine learning is deployed to optimize economic systems, like auctions, or to learn how to strategize in economic systems. The workshop will cover topics such as: 1) learning with humans in the loop, 2) learning when data providers have a vested interest in the outcome of the learning process, 3) learning and dynamics as a game theoretic solution concept, 4) analyzing data (econometrics) from strategic interactions.
Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-games2 [at] lists.simons.berkeley.edu (at this address).
Registration is required to attend this workshop. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the workshop. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.