SummerAcademy2019

Complexity Economics

Theory and Computational Methods


Workshop at the 2019 Summer Academy for Pluralist 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.


General information about the course


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.


Course outline

Extended reading list


Preparatory material for Python


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.


Installation guidelines

Introduction to the Spyder IDE

Test script

Preparatory exercises for Python

Solutions for the preparatory excercises

Preparatory video lectures (a slightly more extensive script is available here)


Lecture slides


1. Introduction and organization


2.Meta-theory and history of complexity economics


3. Scale-free distributions


4. Network theory (a more extensive but work-in-progress script is available here)


5. Dynamical systems


6. Agent-based modelling


7. Distributions and entropy



Python labs


Introduction to the lab sessions


Lab 1: Foundations


Problem set


Solutions to the problem set


Lab 2: Networks


Network data


More extensive script to handle networks in python


Problem set


Solutions to the problem set


Lab 3: Dynamical systems


Problem sets


Script on how to plot in python


Solutions to the problem sets


Lab 4: Agent-based modeling


Problem sets


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