Machine Learning, Critical Mathematical Economics and Progressive Policy

We study mathematical models of the economy, e.g. the Keen model and its
extension by Grasselli from the perspective of the state. Here we are
interested in the question whether economic breakdown and crises can be
prevented by an optimal state intervention with low costs. The
calculation of an optimal policy is a classical problem of machine
learning, especially reinforcement learning, which had quite a success
in providing good policies in a variety of games (e.g. Alpha Zero in
chess) and complex environments (for more information, see e.g. here.)

Principle Investigators: Pavel GurevichHannes Stuke
Members: Johannes Buchner