Exploration beyond bandits
Speaker: Eric Schulz
Abstract: Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will argue that cognitive science can turn its lens to more complex scenarios to study exploration in real-world domains and online games. I will show in one large data set of online food delivery orders and across many online games how current cognitive theories of learning and exploration can describe human behavior in the wild, but also how these tasks demand us to expand our theoretical toolkit to describe a rich repertoire of real-world behaviors such as empowerment and fun.