Basic notions of probability calculus, Markov chains and dynamic systems. Fundamentals of Matlab programming.
The student is expected to learn how to formulate and analyze decision problems in the presence of uncertainty.
Introduction to decision analysis. Structure of a decision problem. Influence diagrams. Decision trees. Maximum expected monetary value. Dominant strategies. The value of information. Risk attitude and utility functions. Sequential decision problems. Dynamic programming for deterministic models. Markov decision processes. Finite horizon problems. Infinite horizon problems. Risk averse control.
R. T. Clemen, T. Reilly, “Making Hard Decisions”, Pacific Grove: Duxbury, 2001.
M. L. Puterman, “Markov Decision Processes: Discrete Stochastic Dynamic Programming”, Wiley-Interscience, 1994.
Teaching material provided by the teacher.
Lectures and computer exercises.
Written examination and computer projects. The written test is aimed at verifying that the student has acquired the basics of decision analysis. The project gives the student the possibility to apply the methodologies presented in the course to real world problems
The course will be delivered in person and simultaneously streamed live on Google Meet, at this link:
Registration of the lectures will be available on a shared Google drive.
Further information can be found at http://control.dii.unisi.it/ad/