Growing Artificial Societies

ECON 496/696

Last modified: 22 April 2013

Professor: Alan G. Isaac Course: Econ 496/696-E01L (3 credits)
Email: Phone: (202)885-3770
Office Hours: by appointment Office: 117 Kreeger Hall


This course is an introduction to agent-based simulation (ABS) as a method for the investigation of complex economic phenomena. Students create "virtual worlds" that shed light on the actual world. Illustrative applications include the following:

  • the causes of economic growth
  • sources of segregation in urban housing
  • how institutional features contribute to inequality in the distribution of wealth
  • the origins of cooperative behavior
  • the characteristics of social dilemmas such as the tragedy of the commons, and the possible "escapes" from these dilemmas

After taking this course, students will be able to:

  • describe historical aspects of how ABS has affected the social sciences
  • describe a variety of ABS research programs across the social-science disciplines
  • describe how agent-based models are constructed and developed
  • provide reasons to use agent-based models in social-science theory and research
  • describe some of the unique insights about economics provided by agent-based methods
  • describe which scientific problems or puzzles are best solved through agent-based modeling, as opposed to other approaches (e.g., statistical or mathematical)
  • design and construct simple agent-based simulations (using Netlogo)
  • use software tools (such as NetLogo and Subversion) that facilitate ABS model development
  • address empirical validation issues in ABS

Graduate students in this course will also be able to explain:

  • why object-oriented programming is widely used for ABS
  • basic principles of object-oriented program design
  • some advantages of running agent-based simulations on a high-performance computing facility
  • how parallel and distributed processing can speed up your ABS

There are no prerequisite programming skills for this course. The main requirements to take this course and perform well are:

  • basic skills in critical thinking and analytical reasoning
  • an interest in learning a few programming tools that will allow you to run and construct simple agent-based models
  • an interest in real-world social phenomena where computational approaches can be usefully applied (e.g., the environment, market microstructure, conflict, social networks, origins of cooperation/civilization, intergenerational transmission of economic status, social dilemmas, epidemics, or other area of application).
  • curiosity about the nature and purpose of computational modeling in the social sciences. (Why not stick to more traditional mathematical methods. What can agent-based simulations teach us about social processes? What are their main strengths and limitations?)
  • motivation to invest some time and effort in learning from case studies, research projects, and demonstrations

The course includes in-depth class discussion of models and code. You are expected to contribute to these discussions. To do so, you will need to keep up with the readings and demos.

This course emphasizes economic applications of ABS. However computational social science is inherently interdisciplinary, so we also draw upon other social sciences. Occasionally I will discuss technical details relevant to specific ABS applications. Some of this material will be optional for undergraduates. Graduate students should display their mastery of technical topics in their submitted projects.

Note: Although agent-based methods have wide commercial use (see, this course is not focused on commercial applications.

Course Prerequisites

This course is specifically designed as an introduction to tools, methods, and applications. Students will learn to run, modify, and develop agent-based models using the NetLogo multi-agent programmable modeling environment. Students should take introductory courses in microeconomics and macroeconomics before attempting this course. However there are no other prerequisites. Specifically, the course does not assume that students have prior experience with computer programming.

I also consider a commitment to upholding the Academic Integrity Code for American University to be prerequisite to participation in this course.

Learning Assistance

A range of services may be available to you through the University to help you meet course requirements. The Academic Support Center ( offers resources and consultations for all students, including those with learning disabilities and ADHD. Disability Support Services ( offers technical support for students with physical, medical or psychological disabilities.

If you are a student with physical or other disabilities and thereby experience difficulty with this course, please do not hesitate to contact me. If you qualify for accommodations because of a disability, please notify me in a timely manner with a letter from the Academic Support Center or Disability Support Services so that we can make arrangements to accommodate your needs.

Student Evaluation of Teaching

Near the end of the course, you will have the opportunity to evaluate this class and your learning experience by completing an Student Evaluation of Teaching. The evaluation contains the same set of questions used in assessing classroom-based courses, with the addition of four questions specifically applicable to online learning. You are strongly encouraged to participate in this evaluation of your educational experience. A high participation rate helps me better understand how to improve and strengthen this course.

Course Organization and Requirements


Students must keep up with all required readings and participate in our class discussion group. There are no exams. Grades are based on:

Participation (15%)
frequent participation in discussions, and evident preparation of the readings
Short Computational Projects (45%)
3 computationally oriented homework projects, worth 15% each, implementing methods developed in the course
Course Project: Code, Paper, and Presentation (40%)
A course-long project, designing and building an original agent-based simulation model, project code and paper due the last day of the course; presentations as scheduled. Undergradautes may work in groups of as many as four students. Graduate students should work in pairs or alone. (Graduate students can consider running their simulation on AU's high performance computing facility.)

Course Project

Your course project will attempt to build and run an original ABS, or to replicate and extend an extant ABS. You will use NetLogo's BehaviorSpace (or some equivalent) to export data from your simulation, which you will analyze (e.g., in a spreadsheet). You will produce a distilled presentation of your work in the form of presentation slides, and you will produce a 15-20 page research paper. I expect that you will share your slides and paper with the class. (Contact me if you have cogent objections.) The collaborative project (paper + code + presentation slides) serves as the final exam.

If you choose to replicate a study, ideally you will verify that your implementation produces the same results as the original. However, many replication projects prove very ambitious for one term. In such cases, I will be perfectly happy if you implement a subset of the model features.

You should spend 30-40 hours on this project, spread throughout the term.


Focus on either use or development of a computational model in an area of your choice (e.g., environmental policy, social hierarchy, economic development, transmission of inequality, historical dynamics, market dynamics). Keep it simple enough to finish! Clear your choice of model with me.

Make sure your code includes detailed, helpful explanatory comments.

I am not asking you to attend to the model aesthetics (shapes, colors, etc). However you may find it useful to use color or labels or shapes to distinguish agents by their attribute values or breed. Focus on making the model as useful to you as possible. Add visual features only when that helps you understand your model.


Your slides should be suitable for a 30 minute oral presentation, figuring roughly 2 minutes per slide. Your slides should cover the same ground as your paper, but much more briefly, and with more of an emphasis on your results. Your use of the ODD framework should be clearly evident, but do not just copy text from your paper: you want a concise set of bullet points, along the lines of an informative outline of your paper.

Discussion of the presentation slides will take place on the last days of course (including the day of the final exam). All students must participate in these discussions. For the purposes of the discussion, you may ask the class to read one journal article that supports your paper.


The research paper will cover four main themes.

  1. a description of the model;
  2. systematic experimentation with the model;
  3. presentation of model results;
  4. your summary of the model's capabilities and what you achieved with it.

The ODD format provides a guide. It tells you what sections you need to have in your paper, and it suggests an order in which to put them. You do not have to adhere to it rigidly. For example, you will probably want to add a "limitations and future extensions" subsection to your Conclusion.

The Railsback and Grimm book presents a number of models using the ODD framework, and you can look at those for good examples of how to do this. For example, see the presentation of the Telemarketer model of chapter 13.

Literature review: You are not required to have an extensive literature review. I would however like you to discuss one paper related to your work. (If your work is a replication or extension, this will usually be the paper originally presenting the model.) In terms of the ODD framework, I suggest that any discussions of the literature come in the Overview section, as a subsection entitled "Review of Some Related Literature" or "Relationship to Smith (2010)".

Citation: I prefer that you use author-date parenthetical citation. As always, you must quote literal quotes and cite paraphrasings.

Class Discussion

Discussions for this course will take place on the Google discussion list ECON496696E2013. You need to sign up for this list. Once you are signed up, you can participate via your browser, or by sending email to


All homework should be typed and submitted to Be sure to include your last name, the assignment number, and the course number in the subject line of your email. (For example: LastName HW#1, Econ 496/696-E01L.)

Homework must be submitted in an approved file format. (Formats are discussed in the Software section of this syllabus.) For computational assignments, you must submit a working program file. (Run it right before sending it: programs that do not run as submitted receive a grade of zero!)

Core Texts

You can order the first two of these online from the American University Bookstore. The third is a free online resource.

Railsback, Steven F. and Volker Grimm (2011)
Agent-based and Individual-based Modeling: A Practical Introduction [railsback.grimm-2011-pup]
Epstein, Joshua M. and Robert Axtell (1996)
Growing Artificial Societies: Social Science From The Bottom Up [epstein.axtell-1996-brookings]
NetLogo User Manual

Supplementary Texts (online or on reserve)

Alkemade, Floortje (2004)

Evolutionary Agent-Based Economics [alkemade-2004-dissertation]

A dissertation addressing the diffustion of information.

Simon, H. A. (1969)

The Sciences of the Artificial.

A seminal work in CSS by one of the founders; covers fundamental concepts and principles.

Taber, C. S. & R. J. Timpone

Computational Modeling. Sage Publications (Thousand Oaks, CA)

A survey of important ideas in CSS, including evaluation criteria for simulation models.

Gilbert, Nigel (2007)
Agent Based Models Sage Publications, Incorporated (Los Angeles, CA) ISBN: 9781412949644


Please install the following software on your personal computer before the first week. For your assignments you will submit a working NetLogo program, along with a PDF of your write up.

NetLogo 5.0
Juniper VPN (VPN facilitates access to some campus resources)

Some students may want to experiment with Subversion. Let me know if you want access to a repository on the AU campus, and I'll arrange to set this up.

TortoiseSVN or Subversion Command-Line Client (include a GUI) (just a command line)

Graduate students may also wish to install the following:

Enthought Python Distribution (Note that this is free for academic use.)
MiKTeX 2.9
(A complete LaTeX distribution; includes the TeXMaker editor and TeXnicCenter IDE.) (If you are not familiar with LaTeX, be sure to read
(A user-friendly interface to MiKTeX.)
Vim 7.3
The best text editor ever, if you start with the 30 minute vimtutor tutorial. (On MS-Windows you can find vimtutor in the Program/Vim menu.)

Topics and Readings

All readings are assigned as preparatory materials for discussion. Required readings are just that: required. Additional recommended readings may or may not be part of our discussions, depending on class interest and the time available; they are primarily for your own interest and are not required.

Week 1: Introduction to Computational Modeling

Lecture: NetLogo Basics
  • overview of course (discussion of syllabus)
  • introduction to NetLogo: Party model
  • introduction to NetLogo: Traffic Basic model
  • introduction to NetLogo: Wolf-Sheep Predation model
  • introduction to NetLogo: the command center
Required Reading (read in the order presented)
Recommended Reading and Resources
Lecture: Introduction to CSS
  • perspectives on CSS
Required Reading
Required Reading (Advanced)
Recommended Reading
  • Gambler's Ruin (static pairs experimental simulation) (You will need 10 pennies for the next few lectures.)
  • Gambler's Ruin (static pairs computer simulation)
Lecture: CSS and ABM
  • the relevance of artificial worlds
  • what is an agent?
  • verification and validation
Required Reading
Additional Required Reading (for graduate students)

Week 2: Introduction to NetLogo Programming

Lecture: Introduction to NetLogo Programming
Required Reading
Recommended Reading
Lecture: Collaborative Programming
Required Reading
  • Lecture Slides: Introduction to Subversion
Recommended Reading
Lecture: Two-Player Gambler's Ruin
  • class simulation vs. computer simulation
  • program design
  • UML activity diagrams
  • iterative processes
  • planning, programming, and collaboration
Required Reading
Recommended Reading
Additional Recommended Reading (for graduate students)
  • Code Example: Plotting Example
  • Gambler's Ruin (static pairs computer simulation: collaborative devlopment)

Week 3: Introduction to Agent-Based Methods

Lecture: N-Player Gambler's Ruin
  • computation in the social sciences
  • a "third way" of doing social science
  • meanings of complexity and emergence.
  • loops and branching
  • script-writing strategies
Required Reading
Recommended Reading
Additional Recommended Reading (for graduate students)
Lecture: N-Player Gambler's Ruin ...
  • verification and validation
  • "docking" (alignment of computational models)
  • Python functions
  • procedural programming
  • how should we represent an "agent" in software?
  • refactoring
  • documenting programs (comments and assertions)
Required Reading
Recommended Reading
Recommended Reading (for Graduate Students)

Week 4: Distribution of Wealth and Income

Lecture: More NetLogo Programming
  • getting data from the simulation
  • analyzing data from the simulation
Required Reading
Additional Required Reading (for graduate students)
  • Butterfly model (analysis)
Lecture: Inheritance and Wealth
Required Reading
  • Lecture Slides on Blinder Model
Required Reading (Graduate Students)
Optional Topic: Inheritance and Chance
Required Reading
Optional Topic: Econophysics and the Distribution of Wealth
Required Reading
Additional Required Reading (for graduate students)
Recommended Reading

Week 5: Cellular Automata and Spatially Situated Agents

Lecture: CA and the Game of Life
Required Reading
Recommended Reading (for Graduate Students)
Lecture: Segregation
Required Reading
Recommended Reading
Lecture: Experimental Design
Required Reading
Recommended Reading

Week 6: More Spatially Situated Agents

Template Models
Required Reading
Required Reading

Networked Agents

Required Reading
Required Reading (Grad)
  • RG's Business Investor Model

Week 7: Modeling Skills

More Sugarscape
Required Reading
Recommended Reading
Lecture: Documenting Your Programs
Required Reading
Recommended Reading
Additional Recommended Reading (for graduate students)
  • NetLogo documentation facilities
  • Python docstrings (and possibly Sphinx)
Lecture: High Performance Computing
Recommended Reading
Other Reading


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Other Possible Topics (Should Time Permit)

Game Theory

Social Dilemmas
  • social dilemmas
  • iterated prisoner's dilemma
  • object oriented programming
Required Reading
Required Reading (Graduate)
Additional Required Reading (for graduate students)
Recommended Reading
  • t.b.a.
Evolution of Cooperation
Required Reading
Additional Required Reading (for graduate students)
Required Reading
Recommended Reading


Market Games and Zero-Intelligence Traders
  • Basic game theory concepts
  • Market games among multiple learning traders
  • Can market structure substitute for trader rationality?
Required Reading