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