Introduction to Discrete Choice Models
Mathematical Models for Human Behavior: A comprehensive course exploring predictive analytics and quantitative methods to forecast behavioral patterns.
Mathematical Models for Human Behavior: A comprehensive course exploring predictive analytics and quantitative methods to forecast behavioral patterns.
Dive into the world of behavioral prediction with this course on discrete choice models. Explore methods for forecasting human behavior using advanced mathematical techniques, focusing on disaggregate demand models. The course centers on the logit model, covering model specification, parameter estimation, validation processes, and practical applications. With hands-on exercises using Biogeme software and real-world case studies, you'll gain both theoretical knowledge and practical skills in choice modeling. Ideal for those interested in understanding and predicting complex human decision-making processes.
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What you'll learn
Understand the behavioral assumptions underlying disaggregate choice models
Derive operational models from theoretical foundations
Master the art of model specification for various choice scenarios
Estimate model parameters using real-world choice data
Test and validate different model specifications
Apply estimated models to concrete, real-world problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 7 modules in this course
This course provides a comprehensive introduction to discrete choice models, focusing on methods for predicting human behavior using mathematical techniques. Students will explore the theoretical foundations of choice modeling and learn to develop disaggregate demand models. The curriculum covers the entire process of choice modeling, from model specification and parameter estimation to validation and practical application. A key component of the course is the use of the Biogeme software for hands-on exercises, allowing students to apply their learning to real-world datasets. The course emphasizes the logit model but also introduces other relevant concepts in choice modeling. By the end of the course, students will be equipped with the skills to analyze and predict complex human decision-making processes in various fields such as marketing, transportation, and economics.
Introduction to behavior modeling
Module 1
Theoretical foundations
Module 2
Binary choice
Module 3
Biogeme: an open-source software for estimating choice models
Module 4
Choice with multiple alternatives
Module 5
Testing
Module 6
Forecasting
Module 7
Fee Structure
Instructor
5 Courses
Pioneer in Transportation Systems and Operations Research
Michel Bierlaire, born in 1967 in Namur, Belgium, is a Belgian-Swiss applied mathematician and Full Professor at École polytechnique fédérale de Lausanne (EPFL), where he directs the Transport and Mobility Laboratory since 2006. After earning his Ph.D. in Mathematical Sciences from the University of Namur in 1996, he worked at MIT's Intelligent Transportation Systems Program (1995-1998) developing real-time traffic simulation tools. He joined EPFL in 1998 as a senior scientist, progressing to Associate Professor in 2006 and Full Professor in 2012. His research focuses on transportation modeling, discrete choice models, and operations research, with significant contributions to demand modeling and traffic management systems. He founded the European Association for Research in Transportation and developed Biogeme, an open-source project for discrete choice model estimation. His scholarly output includes over 150 papers in international journals, 4 books, and numerous book chapters and conference proceedings. He has served as director of TraCE Transportation Center (2009-2022) and head of the Civil Engineering Institute (2017-2021), while maintaining leadership roles in various international research organizations
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