Ph.D. Economics
This page contains all information and material for the course, which I teach together with Torsten Heinrich from the University of Oxford. For more general information about the summer school, see the official webpage.
Here are some general information about the course. Please make sure you bring your laptops to the course and you have Python installed, as described below. Also, you should have a look at the preparatory exercises. If you have no prior knowledge in Python or have difficulties in solving the exercises, please watch the preparatory video lectures. Note that no prior reading is required and that the extended reading list contains optional material, which you can read if you want to dig deeper into selected topics.
Here is some preparatory material for the programming labs. Don't worry if you do not have any experience in Python programming. Just have a look at the preparatory video lectures, which do not presuppose any prior knowledge. In case you are in doubt, do the preparatory exercises below and watch the selected chapters of the video lectures (or just have a look at the script). It is, however, very important that you have set up a Python environment on your computer as described below, and that you have watched the preparatory lectures if you had problems in solving the exercises. Also, make sure you download and run the test script to make sure your computer is ready for the course. In case you have any questions, please do not hesitate to contact us any time.
Introduction to the Spyder IDE
Preparatory exercises for Python
Solutions for the preparatory excercises
Preparatory video lectures (a slightly more extensive script is available here)
1. Introduction and organization
2.Meta-theory and history of complexity economics
4. Network theory (a more extensive but work-in-progress script is available here)
Introduction to the lab sessions
Solutions to the problem set
Lab 2: Networks
More extensive script to handle networks in python
Lab 3: Dynamical systems
Script on how to plot in python
Solutions to the problem sets
Lab 4: Agent-based modeling
The Rock-Paper-Scissors example code
Solutions to the problem sets
Lab 5: Agent-based modeling and distributions
Problem sets
Solutions to the problem sets
Some material is password protected for copyright reasons. The password is available upon request