Machine learning is primarily concerned with the design and analysis of algorithms that learn about an entity. Increasingly more, machine learning is being used to design policies that affect the entity it once learned about. This can cause the entity to react and present a different behavior. Additionally, in many environments, multiple learners learn concurrently about one or more related entities. This can bring about a range of interactions between individual learners.
How do the learners and entities interact? How do these interactions change the task at hand? What are some desirable interactions in a learning environment? And what are the mechanisms for bringing about such desirable interactions?
This workshop on Learning in Presence of Strategic Behavior will be held as part of the summers series workshops at Toyotal Technological Institute, Chicago, on August 20-22, 2018.
The main goal of this workshop is to address current challenges and opportunities that arise from the presence of strategic behavior in machine learning. This workshop aims at bringing together members of different communities, including machine learning, economics, theoretical computer science, and social computing, to share recent results, discuss important directions for future research, and foster collaborations.
Location: The workshop takes place at Toyotal Technological Institute on August 20-22, 2018.