Mathematical modelling of behavior

Summary

Discrete choice models allow for the analysis and prediction of individuals' choice behavior. The objective of the course is to introduce both methodological and applied aspects, in the field of marketing, transportation, and finance.

Content

Learning Prerequisites

Required courses

The course assumes knowledge of basic probability and statistics (random variables, linear regression)

Learning Outcomes

By the end of the course, the student must be able to:

• Model discrete choice

Transversal skills

• Use a work methodology appropriate to the task.
• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Use both general and domain specific IT resources and tools

Teaching methods

The course is a combination of ex-cathedra lectures and practical sessions.

The practical sessions consist in exercices and laboratories. They are organized every week during the semester. The students will estimate the parameters of behavioral models based on real data.

Expected student activities

Every week, the students are supposed to

1. read the appropriate material, according to the schedule (the material for a given week is supposed to be read before the lecture of that week);
2. work on the assignments for the laboratories.

Assessment methods

A graded project must be submitted during the semester.

A written exam is held during the official examination session.

Resources

Bibliography

Ben-Akiva and Lerman (1985) Discrete Choice Analysis, MIT Press.Train (2003) Discrete Choice Methods with Simulation, Cambridge University Press.

Ressources en bibliothèque

• Discrete Choice Analysis / Ben-Akiva
• Discrete Choice Methods with Simulation / Train

Websites

• https://courses.edx.org/courses/course-v1:EPFLx+DiscreteChoiceX+3T2017/course/

Moodle Link

• https://go.epfl.ch/MATH-463