Decision Support Models (IST year 2)

Module description

At the completion of the course, the student will: be familiar with distinct decisionmaking strategies and traps in the evaluation of options and in the allocation of
resources in private and public contexts; be familiar with key theoretical and methodological concepts of decisionmaking and decision aid relevant for the best practice of decision engineering; be familiar with models, processes and tools for helping to structure and explore decisions characterized by multiple objectives, uncertainty, complexity and differences of opinion; be familiar with examples of realworld decision analysis and decision conferencing applications in  organizations; be familiar with other topics considered relevant for engineering decisions, covering problem structuring methods, heuristics and biases and group decision and negotiation; have developed skills in decision analysis and modeling;  be able to select and use specialized decision support software in different decision contexts.

The decision making problematic: Definition of the decision problem. Importance of decision making in engineering and management. Characteristics of the decision context. Decision making strategies. Uncertainty and complexity. Value and risk. What is Decision Analysis (DA)? DA objectives. The seven fundamental steps of DA. DA schools of thought and theoretical foundations. The problem of decision aiding. Intervention strategies: From optimization to the learning paradigm. Value and utility analysis. Decision conference and facilitation. Concepts, models, techniques and software for decision support:
1. Decision trees and influence diagrams; case studies; PRECISION TREE.
2. Bayesian networks; case studies; NETICA.
3. Probabilities modeling and risk analysis; case studies; @RISK.
4. Cognitive mapping; case studies; DECISION EXPLORER.
5. Multiple criteria evaluation models; case studies; MACBETH.
6. Resource allocation and negotiation; case studies; PROBE and MACBETH.

Evaluation Methodology
Teaching is mostly organized by groups of models, techniques and software for decision support that can assist different types of decision problems. For each type of decision problem, teaching is based on the presentation of methods, models and techniques to assist decisionmakers, followed by a discussion of real world case studies and of key methodological aspects, and on the use of decision support tools. For some topics students also carry out practical exercises. Evaluation is done through two groupwork assignments and one individual exam. In one groupwork students structure problems characterized by uncertainty, build models and implement them in appropriate software; in another groupwork students build a multicriteria evaluation model to assist a decisionmaker
in a real problem.

Website uses cookie files in order to operate better. Direct use of the site without changing the settings in web browser means that cookies will be placed on your computer. more

The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this.