Risks and Decision

Recommended Prerequisites

Not applicable.

 

Teaching Methods

Lectures combining theoretical explanations with exercise solving and with conceptual and real-case discussions.

Learning Outcomes

Provide students with a deep knowledge and with advanced R&D skills and competences on the subject of risk and decision-making.

Work Placement(s)

No

Syllabus

Paradigms in decision support involving uncertainty/risk, multiple objectives/attributes, and multiple actors/stakeholders.

Modeling uncertainty and risk. Fundamental concepts of probability theory. Framework for risk analysis, assessment and management.

Decision analysis (single-criterion). Framework for decision support under uncertainty/risk. Attitudes toward risk. Utility theory. Decision trees and influence diagrams. Value of information. Bayesian analysis.

Multicriteria analysis. Problem structuring: values, objectives, attributes, achievement/performance scales.

Additive methods. Weight eliciting and interpretation. Reference point methods. Outranking methods.

Sensitivity and robustness analysis. Treatment of uncertainty.

Game theory. Zero-sum and non-zero sum games. Extensive form of a game. Resolution of finite games.

Cooperative and non-cooperative games. Coalition games.

Assessment Methods

Assessment
Written final exam: 100.0%

Bibliography

Vose D, Risk Analysis: A Quantitative Guide, Wiley, 2008
Myerson RB, Game Theory: Analysis of Conflict, Harvard UP, 1997
Clemen RT, Reilly T., Making Hard Decisions with Decision Tools, Duxbury Press, 2003
Pratt JW, Raiffa H, Schlaifer R, Introduction to Statistical Decision Theory, MIT Press, 2008