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Michel Bierlaire

The logit model is the workhorse of choice modelers. But it has some limitations. In particular, some assumptions used to derive it may not be consistent with the behavioral reality. It may lead to erroneous forecast. We illustrated using the so-called "red bud-blue bus" paradox, and Multivariate Extre Value models, addressing some of these issues, are introduced.

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The logit model is the workhorse of choice modelers. But it has some limitations. In particular, some assumptions used to derive it may not be consistent with the behavioral reality. It may lead to erroneous forecast. We illustrated using the so-called "red bud-blue bus" paradox, and Multivariate Extre Value models, addressing some of these issues, are introduced.

The sampling procedure used to collect choice data has a critical impact on the model estimation procedure. We introduce classical sampling procedures, and analyze in details the implications for model estimation.

In our quest to address the limitations of the logit model, we introduce a new family of models, based on "mixtures". We define what mixtures are, how they can be calculated. We investigate several important modeling assumptions that they can cover.

Random utility relies on the rationality assumption for the decision-makers. We show that human beings are not always consistent with this assumption, and may exhibit apparent irrationality. Hybrid choice models are able to capture subjective dimensions of the choice process, using variables that are called "latent variables".

Choices evolve over time. Individuals learn, develop habits. In order to capture that, it is necessary to observe individuals over time, and to collect so-called "panel data". The introduction of the time dimension into choice models has some econometrics implications, that we describe in detail.

Who needs choice models, when machine learning algorithms are so powerful and pervasive? In this last chapter, we introduce the similarities and differences between machine learning and discrete choice, and we discuss some potential limitations of machine learning in the context of the analysis of choice data.

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What's inside

Learning objectives

  • Multivariate extreme value models
  • Sampling issues
  • Mixtures
  • Latent variables
  • Panel data
  • Discrete choice and machine learning

Syllabus

Week 1. Multivariate Extreme Value Models
Week 2. Sampling
Week 3. Mixtures
Week 4. Latent variables
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Week 5. Panel data
Week 6. Machine learning

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Introduces complex modeling techniques used in econometrics and statistics for choice modeling
Makes econometric concepts accessible to practitioners who may be new to the field
Emphasizes the use of sampling procedures in choice data collection and analysis, a critical aspect often overlooked in practice
Provides a solid foundation for researchers and practitioners who want to develop advanced choice models for various applications
Requires a strong foundation in econometrics and statistics, which may be a barrier for some learners
Taught by Michel Bierlaire, a renowned expert in transportation modeling, providing credibility to the course content

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Selected Topics on Discrete Choice with these activities:
Review mathematical statistics
Brush up on your mathematical statistics skills to strengthen your foundation for this course.
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  • Review basic probability and random variables.
  • Practice solving problems involving hypothesis testing and confidence intervals.
Read 'Discrete Choice Analysis' by Kenneth Train
Gain a comprehensive understanding of the theoretical foundations and practical applications of discrete choice analysis.
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  • Purchase or borrow the book.
  • Read the book thoroughly, taking notes and highlighting important concepts.
  • Complete the exercises and problems at the end of each chapter.
Follow tutorials on multivariate extreme value models
Expand your understanding of multivariate extreme value models through guided tutorials.
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  • Search for online tutorials on multivariate extreme value models.
  • Follow the tutorials step-by-step and take notes on key concepts.
  • Apply the concepts to solve practice problems.
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Solve exercises on sampling techniques
Reinforce your understanding of sampling techniques through practice drills.
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  • Find practice problems on sampling techniques.
  • Attempt to solve the problems on your own.
  • Check your solutions against answer keys or consult online forums for guidance.
Network with professionals in the field of discrete choice modeling
Connect with experts and practitioners in discrete choice modeling to gain insights and broaden your professional network.
Browse courses on Discrete Choice Modeling
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  • Attend industry events and conferences.
  • Join professional organizations related to discrete choice modeling.
  • Reach out to professionals on LinkedIn or other networking platforms.
Attend a workshop on machine learning for discrete choice analysis
Expand your knowledge of machine learning techniques for discrete choice analysis.
Browse courses on Machine Learning
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  • Search for workshops on machine learning for discrete choice analysis.
  • Register for a workshop that fits your schedule and interests.
  • Attend the workshop and actively participate in discussions and exercises.
Contribute to open-source projects on discrete choice modeling
Gain practical experience in discrete choice modeling by contributing to open-source projects.
Browse courses on Discrete Choice Modeling
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  • Search for open-source projects related to discrete choice modeling.
  • Identify a project that aligns with your interests and skills.
  • Contribute to the project by fixing bugs, adding features, or improving documentation.
Develop a simulation model for a choice process
Deepen your understanding of choice processes by creating a simulation model.
Browse courses on Discrete Choice Modeling
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  • Identify the key factors influencing the choice process.
  • Develop a mathematical model to simulate the process.
  • Implement the model using a programming language.
  • Test and refine the model using real-world data.

Career center

Learners who complete Selected Topics on Discrete Choice will develop knowledge and skills that may be useful to these careers:
Transportation Planner
Transportation Planners play a vital role in the design and implementation of transportation systems. They develop plans to improve transportation infrastructure, such as roads, highways, and public transportation systems, in order to ensure the safe, efficient, and environmentally sustainable movement of people and goods. This course on Selected Topics on Discrete Choice can be particularly useful for Transportation Planners as it provides a deeper understanding of the factors that influence travel behavior, which is essential for developing effective transportation plans.
Traffic Engineer
Traffic Engineers design and implement solutions to improve the flow of traffic and enhance the safety of roadways. They use their knowledge of traffic engineering principles to analyze traffic patterns, identify problem areas, and develop and evaluate solutions to improve traffic flow and safety.
Survey Researcher
Survey Researchers design, conduct, and analyze surveys to collect data on a variety of topics. They use their knowledge of sampling techniques and statistical methods to ensure that their surveys are accurate and reliable.
Market Research Analyst
Market Research Analysts conduct research to understand consumer behavior and identify market trends. They use their knowledge of research methods and statistical analysis to collect and interpret data on consumer preferences, attitudes, and behaviors.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their knowledge of statistical methods and data analysis techniques to extract insights from data and communicate their findings to stakeholders.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries, including transportation, manufacturing, and healthcare. They use their knowledge of optimization techniques and decision science to develop and implement solutions that improve efficiency and profitability.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and develop investment strategies. They use their knowledge of financial modeling and risk management to make investment decisions and provide advice to clients.
Econometrician
Econometricians use statistical methods to analyze economic data and test economic theories. They use their knowledge of econometrics to develop and implement models that can be used to forecast economic trends and evaluate the impact of economic policies.
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. They use their knowledge of statistical methods and data analysis techniques to extract insights from data and communicate their findings to stakeholders.
Data Scientist
Data Scientists use their knowledge of data science techniques and machine learning to extract insights from data and develop predictive models.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software engineering principles to create software that meets the needs of users.
Computer Scientist
Computer Scientists research and develop new computing technologies and applications. They use their knowledge of computer science principles to design and implement solutions to complex problems.
Mathematician
Mathematicians develop and apply mathematical theories and techniques to solve problems in a variety of fields, including science, engineering, and finance.
Physicist
Physicists study the fundamental laws of nature and the behavior of matter and energy. They use their knowledge of physics to develop new technologies and solve problems in a variety of fields, including medicine, engineering, and environmental science.
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. They use their knowledge of statistics and biology to design and analyze studies, and to develop new statistical methods for analyzing biological and medical data.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Selected Topics on Discrete Choice.
Provides a comprehensive overview of discrete choice models, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of econometrics, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of deep learning, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of Bayesian reasoning and machine learning, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of pattern recognition and machine learning, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of data analysis using regression and multilevel/hierarchical models. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of mathematical statistics and data analysis, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of reinforcement learning, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of the econometrics of count data, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of machine learning, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a comprehensive overview of statistical methods for the social sciences, including both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about this important topic.
Provides a detailed treatment of the analysis of discrete choice models. It valuable resource for anyone who wants to learn more about this important topic.

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