# Behavioural Models for Individual Choices

Understanding how people make decisions and what drives their choices is key in several disciplines, including:

- Transport
- Economics
- Sociology
- Psychology
- Marketing
- Environment

You will learn:

- About the fundamentals of the decision-making theory about the mathematical models most commonly used to simulate and forecast individual choices
- How to apply models to forecast the demand for different policy scenarios
- About data collection and survey methods
- To use real sets of data, and apply them to forecast the demand using Python Biogeme or Panda Biogeme

## Learning Outcomes

At the end of the module a student will be able to:

- Understand the basic concept of microeconomic theory and bounded rationality
- Understand the link between the mathematical models and the phenomenon they are able to reproduce, i.e. pros and cons (their strengths and weaknesses) of each model
- Understand the nature of the data that can be used to estimate and forecast individual choices and being able to select the right data for the right phenomenon and/or problem under study.
- Select and use the appropriate mathematical model to study individual choices and forecast the impact of policies, arguing the pros and cons of the models employed.
- Use the software to estimate both standard and advanced DCMs
- Formulate and analyses advanced problems that involved individuals’ choices to identify optimal, sustainable and equitable solutions.
- Interpret and compared the results based on statistical analysis -Argue concerning the usefulness of the different models for a specific problem
- Read and discuss some papers where some advanced discrete choice models at their choice is estimated.
- For each specific problem under study (involving individual choice), identify the adequate model structure (among those discussed in the lectures) to simulate the phenomenon and justify the theoretical reasons behind the choice.
- Build (i.e. specify, estimate and evaluate) the mathematical models discussed in the lectures to simulate individual choices in a variety of contexts.
- Using statistical tests and theoretical consideration to evaluate the best model (among those discussed in the lectures).
- Using the models estimated (among those discussed in the lectures) to forecast the demand under various policies.
- Using the models estimated (among those discussed in the lectures) to compute the demand elasticity and valuation measures.
- Identify if a dataset is adequate for the simulation of the phenomenon and/or problems under study
- Write a methodologically sound report containing a description of the phenomenon, treatment of data, a theoretical description of the model specifications (including both standard and advanced DCMs), including discussion on the microeconomic and the identification properties, estimation and a critical discussion of the results.

For more information on this course, please contact llahub@newcastle.ac.uk or enquire here

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