Methods and Models for Decisions
A.Y. 2018/2019
Learning objectives
This course aims to
- discuss the aspects that characterise a complex decision
- present practical case studies, in particular concerning public works
- present mathematical methods to deal with complexity
- present the resulting mathematical models
- discuss limitations and errors typical of those methods and models
- discuss the aspects that characterise a complex decision
- present practical case studies, in particular concerning public works
- present mathematical methods to deal with complexity
- present the resulting mathematical models
- discuss limitations and errors typical of those methods and models
Expected learning outcomes
Undefined
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
First semester
ATTENDING STUDENTS
Course syllabus
NON-ATTENDING STUDENTS
Introduction to complex decision problems.
Case studies.
Formal definition of complex decision problems.
Mathematical Programming:
Karush-Kuhn-Tucker conditions
Multi-objective Programming:
the Pareto case;
multi-attribute utility theory;
Analytic Hierarchy Process;
Electre Methods.
Uncertain Programming:
decision making under ignorance;
decision making under risk;
decision theory.
Game theory:
generalities;
zero-sum games;
symmetric games.
Group decision theory.
Descriptive models:
transportation system models;
queueing theory;
discrete-event simulation;
system dynamics.
Case studies.
Formal definition of complex decision problems.
Mathematical Programming:
Karush-Kuhn-Tucker conditions
Multi-objective Programming:
the Pareto case;
multi-attribute utility theory;
Analytic Hierarchy Process;
Electre Methods.
Uncertain Programming:
decision making under ignorance;
decision making under risk;
decision theory.
Game theory:
generalities;
zero-sum games;
symmetric games.
Group decision theory.
Descriptive models:
transportation system models;
queueing theory;
discrete-event simulation;
system dynamics.
Course syllabus
Introduction to complex decision problems.
Case studies.
Formal definition of complex decision problems.
Mathematical Programming:
Karush-Kuhn-Tucker conditions
Multi-objective Programming:
the Pareto case;
multi-attribute utility theory;
Analytic Hierarchy Process;
Electre Methods.
Uncertain Programming:
decision making under ignorance;
decision making under risk;
decision theory.
Game theory:
generalities;
zero-sum games;
symmetric games.
Group decision theory.
Descriptive models:
transportation system models;
queueing theory;
discrete-event simulation;
system dynamics.
Case studies.
Formal definition of complex decision problems.
Mathematical Programming:
Karush-Kuhn-Tucker conditions
Multi-objective Programming:
the Pareto case;
multi-attribute utility theory;
Analytic Hierarchy Process;
Electre Methods.
Uncertain Programming:
decision making under ignorance;
decision making under risk;
decision theory.
Game theory:
generalities;
zero-sum games;
symmetric games.
Group decision theory.
Descriptive models:
transportation system models;
queueing theory;
discrete-event simulation;
system dynamics.
Professor(s)