Instructors: Giorgio Ricchiuti
For the most theoretical models, the research of an elegant analytical solution has led to the acceptance of stringent and ad hoc hypotheses, leading to results often not supported by empirical evidence. Of course, analytical solutions are clear necessary to reach clear policy suggestions. Therefore, relaxing these hypotheses could achieve complex results in line with realty but at the cost of having less clear cut economic insights. Computational economics allows the construction of a bridge between a theory now in plaster and a growing literature that sees in the simulations the solution of everything but whose results are often driven by a black box. This course aims at analyzing what could be the role of computation to improve our understanding of economic phenomena, providing an introduction to scientific programming and computational methods in economics, combining a "hands on" approach with a theoretic oriented perspective.