The course wants to build a specific expertise on the tools for the management of the decision processes, both from the point of view of methods and applications. The aim is to provide students with analytical skills to address decision-making problems through the use of models. Some topics and real-life cases will be deepened by teamwork.

The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:

• Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance

• Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment

• Interact in a professional, responsible, effective and constructive way in a working environment, also motivating group members

Understand context. The course distinguishes between the expert in decision systems (the purpose is to build this competence in the students) and the real decision maker. So the first aim is to develop the ability of analyzing decision-making problems - actors, objectives, information, conflicts, available data, negotiation, ... - from the formal point of view, building a suitable model of it.

Design solutions. The course define the most appropriate methodologies to address the (previuosly analyzed) decision-making problems, extracting the most effective techniques from the set of tools presented (and discussed with the students).

Interact in a professional way. The course try to recognize decision-making contexts, in public and private environments, in which it is better to use formal methods rather than qualitative tools to support the decision maker .

• A framework for decision problems. Classification of the models. A catalog of examples.
• Deterministic models using a single criterion. Linear programming. Models on networks.
• Non-deterministic models. Risk attitude. Decision tree.
• Models for multi-objective problems. The Multi-Attribute Utility Theory: utility functions, sensitivity analysis.
• Analytic Hierarchy Process: pairwise comparisons, eigenvector, rank reversal, a priori analysis.
• Outline of Electre methods: concordance-discordance. Rating models with Electre Tri.
• Group decisions. Voting systems. Borda and Condorcet methods.
• Group works are proposed to the students on case studies drawn from different sectors

Basic info regarding Linear Algebra.

Having already taken a course of Operations Research will help understanding.