Skip to main content

Module

CEG8427 : Behavioural Models for Individual Choices

  • Offered for Year: 2020/21
  • Module Leader(s): Professor Elisabetta Cherchi
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

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 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 effects of bounded rationality.

-Students will learn how to specify and estimate these models, how to evaluate which model provides the best representation of the phenomenon under study, how to apply the models to forecast the demand for different policy scenarios and how to compute user benefits for economic valuation.

-A brief RECAP from the module CEG8435 (Data collection and survey methods) will be provided on the type of data needed to understand and model individual choices.

-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 Python Biogeme or Panda 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

The themes of lectures delivered are (it does not correspond to the exact lecture schedule):

Introduction:
-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:
-RECAP of the data: Psychological indicators, Revealed and Stated preference data, Cross sectional, short and long Panel data
-Modelling with multiple data source
Using the models in practice:
-How to validate the performance of the models estimated
-How to define policy scenarios
-How to apply the models estimated to forecast the demand under different scenarios
-how to use the models estimated for economic valuation: Value of time and user benefits.

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture11:001:001 hr synchronous online lecture
Guided Independent StudyAssessment preparation and completion12:002:00N/A
Structured Guided LearningLecture materials111:0011:00Non-synchronous on-line lectures
Scheduled Learning And Teaching ActivitiesPractical101:0010:003 hrs synchronous online, 4 hrs non-synchronous online
Guided Independent StudyProject work118:0018:00These are Q&A sessions to discuss the lectures.
Guided Independent StudyIndependent study150:0050:00part is in group of 2/3 student, part is independent work
Guided Independent StudyOnline Discussion81:008:00N/A
Total100:00
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 allows 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

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

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

Exams
Description Length Semester When Set Percentage Comment
Written Examination1202A65Unseen written examination
Other Assessment
Description Semester When Set Percentage Comment
Report2M35Report prepared in groups of 2 people where models estimated using a real data set are presented and discussed.
Assessment Rationale And Relationship

The two forms of assessment (take home 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 three form of examination the form they are more familiar with and where they can express themselves in the best way.

Reading Lists

Timetable