CEG8427 : Behavioural Models for Individual Choices

Semester 2 Credit Value: 10
ECTS Credits: 5.0


The aim of the course is to make the student capable of understanding, modelling and forecasting how individuals make decisions.

Understanding how people makes decisions, and what drives their choices is a key questions in several disciplines such as transport, economics, sociology, psychology, marketing, environment.

In particular:

-       The course provides students with knowledge on the fundamentals of the decision-making theory with a particular emphasis on understanding the different views of the decision-making process between the two approaches of perfect and bounded rationality, pros and cons of each approach and the importance to move toward a unified theory of the decision process.

-       The course provides then the knowledge on the mathematical models most commonly used to simulate and forecast individual choices, starting from the very popular discrete choice models (which are based on the neoclassical economic assumption of rational decision makers) and extending them to account for several effect of bounded rationality.

-       Students will learn how to specify and estimate these models and how to evaluate which model provides the best representation of the phenomenon under study.

-       Knowledge on the type of data needed to understand and model individual choices will also be given, as well as knowledge on the valuation measures that can be computed from the models estimated.

-       The theoretical part is supported by an extensive empirical work where students have the possibility to practically estimate the models discussed in the lectures, using real sets of data, and apply them to forecast the demand. The software Biogeme and/or python Biogeme is used for this purpose and thought as part of the practical work.

-       The course also provides information on how to write a report to present and explain results to policy makers or industrial clients.

Outline Of Syllabus

- Why it is important to study decision process and model individual choices
- Understanding and forecasting individual choices
- Examples from various disciplines (transport, health, economics, marketing, urban etc.).

Theory of individual choice:
- Definition of decision maker, choice set, feasibility of alternatives and constraints
- Concept of utility, compensatory decisions and maximisation
- Non compensatory choice and other heuristics
- The role of behaviour, attitudes, goals and social influence in the decision process
- Behaviour changes and preference formation.

Mathematical models:
- Microeconomic and mathematical derivation of the basic discrete choice models (multinomial logit model)
- Modelling preference heterogeneity among individuals, correlation among alternatives and a variety of substitution patterns (the mixed logit model)
- Utility specification and model estimation in practice
- Modelling effect of bounded rationality, such as habit/inertia effect, learning effects, attitude and perceptions (the hybrid choice model)
- How to choose the best model
- When it is appropriate/correct to use each type model
Understanding and using multiple data sources:
- Nature of data: Psychological indicators, Revealed and Stated preference data, Cross sectional, short and long Panel data
- Modelling with multiple data source

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture102:0020:00N/A
Guided Independent StudyAssessment preparation and completion200:3010:00Revision for examination
Guided Independent StudyAssessment preparation and completion115:0015:00Report covering model estimation using a real data set. Report will be evaluated.
Guided Independent StudyAssessment preparation and completion12:002:00Examination
Scheduled Learning And Teaching ActivitiesPractical92:0018:00Computer practical's inc demo's & sessions for completion of assessed work whilst under guidance.
Guided Independent StudyIndependent study135:0035:00Includes background reading and reading of lecture notes for a full understanding of the material.
Teaching Rationale And Relationship

Teaching and learning of this module is done by a combination of lectures, computer demonstrations and practical work, guest lectures, coursework and reading materials. This is in line with the learning outcomes. Lectures, guest lectures and coursework are intended to provide the theoretical background, computer demonstrations allow students to learn the software and the codes to build the mathematical model. Practical work allow students to learn how to link the theory with the practice (how to use the theory in practice) and help developing problem solving skills.

Reading materials helps developing critical, independent and innovative thinking.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Written Examination1202A70Unseen written examination
Other Assessment
Description Semester When Set Percentage Comment
Report2M30Report - approx. 4 pages comprising 1000 words + 2 pages of tables/diagrams
Assessment Rationale And Relationship

The two forms of examinations (written exam and report) are intended to serve two purposes:
(1) testing if students acquired the intended skills in terms of understanding and master the theoretical background (written exam) and being able to apply the theory in practice (report);
(2) allowing students, who have different background, to find in one of the two forms of examination the form they are more familiar with and where they can express themselves in the best way.

Reading Lists