CMPLXSYS 530 - Computer Modelling of Complex Systems

Course information

Course time & location

Tuesdays and Thursdays, 4pm - 5:30pm
Weiser 747

UPDATE: Starting Tuesday 3/17/20, all classes and office hours will be held online via Bluejeans. The meeting information for joining via Bluejeans is included on Canvas. Office Hours on Thursday 3/12/20 will also be held via Bluejeans, from 3:30-4:30.

Instructor & office hours

Prof. Marisa Eisenberg (she/her)
Email: marisae@umich.edu
Office Hours
- Tuesdays 2pm – 3pm, Thursdays 3pm - 4pm
- 711 Weiser Hall. Sometimes I may hold office hours in the School of Public Health (5166 SPH II). I’ll send an announcement to the class if this is the case.

Syllabus

Syllabus - includes course overview, grading information, prerequisites, etc.

HW/Lab information

HW/Lab guidelines - guidelines on how to turn in your HW assignments, what needs to be turned in, and the grading rubric we will use. Note that assignments are turned in via Canvas!
- Lab 1: Introduction to ABMs, NetLogo, and Python
- Lab 2: Cellular Automata & Intro to Networks
- Lab 3: Dynamics on and of Networks
- Lab 4: Parameter sampling and sensitivity - please see update to the lab as of 4/2/20!
Final project information - information on the course final project (will be updated as we go)

Course topics

Topics are subject to change and rearrangement! (We may also not get through all the topics listed)

Lecture slides, links to assignment instructions, and readings will be posted in each topic as we get to them.

1. Introduction to agent-based models (ABMs)

Topics covered: overview of basic concepts, coding intro, why model?, intro to ABMs and their pros/cons, philosophy of complex systems, emergent behavior/phenomena, classic examples in biological, social, and physical systems, etc.

Lecture 1: Introduction and overview (1/9/20)
- Slides - what are models, why model, emergent behavior, complex systems
- Readings

Lecture 2: Introduction to Agent-Based Models (1/14/20)
- Slides - how to choose a modeling framework, intro to ABMs
- Emoji simulator: forest fire model, blank model, more models!
- Readings
  - Think Complexity, Chp. 1

Lecture 3/Lab 1: Examples & Features of Agent-Based Models (1/16/20)
- Slides (continued from last time)
- Quick intro to NetLogo
- Lab 1: Introduction to ABMs, NetLogo, and Python
- Readings
  - Wilensky and Rand, Chp. 2
  - Considerations and Best Practices in Agent-Based Modeling to Inform Policy (gives a nice introduction to the PARTE framework for thinking about ABMs)

Lecture 4/Lab 1: Case study: Modeling the Ancestral Puebloan communities of Long House Valley (the Artificial Anasazi Project (AAP)) / Continue Lab 1 (1/21/20)
- Slides
- Continue Lab 1
- Readings
  - Papers on the AAP (see also the references in the slides)
    - Axtell et al., PNAS, 2002. Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley
    - An interesting replication study that examines some of the issues and underpinnings of the AAP: Janssen, JASSS, 2009. Understanding Artificial Anasazi
  - Broader reading on ABMs (for Lab 1): Wilensky and Rand, Chp. 3-4

Intro to the PyCX library for simulating complex systems models in Python (1/23/20)

2. Cellular automata

Topics covered: overview of basic cellular automata (CA) concepts, emergent behavior, classes of CA, examples of CA (e.g. Conway’s Game of Life), coding and analysis methods for CA, applications

Lecture 5: Intro to cellular automata (1/28/20)
- Slides
- Readings
  - Sayama, Chp. 11
  - Think Complexity, Chp. 6

Lecture 6: Cellular automata dynamics (1/30/20, wrap up 2/4/20)
- Slides
- Code
- Readings
  - Sayama, Chp. 12
  - Think Complexity, Chp. 7

Lab 2 (Posted 2/6/20, due 2/18/20)
- Lab 2: Cellular Automata & Intro to Networks
- A collection of 1-dimensional cellular automata
- Basic commands for networks in NetworkX/Python and NetLogo
- NetworkX tutorial (from the NetworkX documentation)
- Network data from Mark Newman

3. Networks

Lecture 7: Introduction to Networks (2/4/20, finish up 2/11/20)
- Slides
- Readings

Lab 2 (posting here also, since it includes networks – posted 2/6/20, due 2/18/20)
- Lab 2 Instructions
- A collection of 1-dimensional cellular automata
- Basic commands for networks in NetworkX/Python and NetLogo
- NetworkX tutorial (from the NetworkX documentation)
- Network data from Mark Newman

Lecture 8: Random networks, dynamics on networks and of networks (2/13/20 and 2/18/20)
- Slides
- Code
- Readings
  - Sayama, Chp. 16
  - Think Complexity, Chp. 4 and 5

Lecture 9: Adaptive networks, mean-field approximation of networks (2/20/20 and 2/25/20)

Lab 3 (Posted 2/27/20, due 3/10/20)
- Lab 3: Dynamics on and of Networks

4. Parameter sweeps, sampling, and sensitivity analysis

Lecture 10: Sampling, visualization, uncertainty (3/10/20)
- Slides
  - Example code - Latin hypercube sampling with an SIR network model
- Readings
  - PARTE (Properties, Actions, Rules, Time, Environment) framework
  - ODD (Overview, Design concepts, and Details) Protocol
  - Yang Y, Roux AV, Auchincloss AH, Rodriguez DA, Brown DG. A spatial agent-based model for the simulation of adults’ daily walking within a city . American journal of preventive medicine. 2011 Mar 1;40(3):353-61.

Lecture 11: Sensitivity analysis (3/12/20)
- Slides
- Readings
  - Marino, Simeone, et al. “A methodology for performing global uncertainty and sensitivity analysis in systems biology.” Journal of theoretical biology 254.1 (2008): 178-196.
  - A nice table of different sensitivity analysis methods
  - The wikipedia page is also pretty comprehensive!
  - Active nonlinear testing

Lab 4 (Posted 3/31/20, due 4/9/20)
- Lab 4: Parameter sampling and sensitivity - please see update to the lab as of 4/2/20!

5. Advanced/additional topics

Topics: Uncertainty, inference robustness, model comparison, MCMC, working with micro- and macro-level data and ABMs, more advanced environments (e.g. GIS), game theory

Lecture 12: Model Analysis (3/24/20)
- Slides

Lecture 13: Game & decision theory (3/26/20)
- Slides
- Readings
  - Nicky Case’s The Evolution of Trust Explorable
  - Axelrod & Hamilton, The Evolution of Cooperation

Lecture 14: Introduction to parameter estimation (4/2/20)
- Thinking about the challenges of linking data with highly complex models, and what these approaches can (and can’t) tell you
- Slides

Lecture 15: Parameter estimation with ABMs and complex systems models
- Sampling based methods, including basics of approximate Bayesian computation, sample importance resampling, MCMC (Markov Chain Monte Carlo), etc.
- Slides
- Readings
  - Menzies NA, Soeteman DI, Pandya A, Kim JJ. Bayesian methods for calibrating health policy models: a tutorial. PharmacoEconomics. 2017 Jun 1;35(6):613-24.
- Example code
  - Maximum likelihood
  - MCMC
Lecture 16: ABM Environments
- Slides
- Mapping tutorials
  - Making 3 Easy Maps in Python - the point map example here may be particularly useful for visualizing agents on a map (where you can define their x,y locations in terms of e.g. latitude and longitude). This uses the module folium, which is one of the common mapping packages.
  - Making maps in Basemap This is another package for mapping, and you can similarly do point maps and plot other features on the map. The syntax for basemap seems pretty straightforward, so this may be a useful package to consider also. There’s an additional tutorial for Basemap here also.

Additional useful info

Useful links and extra readings will be posted here.

Coding and typesetting resources

Overall Coding Resources

NetLogo installation information
Python
- Anaconda distribution for Python
- Tutorials
  - 10-minute Python - very short tutorial with just the basics (often useful if you’ve coded in other things or have used python but not in a while)
  - The Python Tutorial - the official tutorial, gives more details and a good reference. Good to start with if you’ve never used python. Check out the resources section of the course website for more tutorials also.
  - Think Python - the book is a good reference too—you may want to start with Chapters 2 and 3
  - A nice collection of python tutorials
- Spyder - the built in tutorial is pretty good! You can find it under the help menu
- Jupyter/IPython resources
  - Jupyter notebook site, a nice tutorial on using Jupyter notebooks
R and Rmarkdown Resources (if you’re an R person)
- R and RStudio for R
- Rmarkdown: useful for writing up lab assignments quick tour, longer intro, Rmarkdown complete guide

ABM Resources

Modules/packages for agent-based modeling
- PyCX module for Python (we will use this!) (reference for PyCX)
- Mesa framework similar to the commonly used ABM package Repast but for Python (I’ve not used this before but looks interesting)
- SpaDES package for R (I’ve not used this before but looks interesting)

Math Resources

Typesetting math: \(\LaTeX\) resources from Paul Hurtado

Books used in the course

Introduction to the Modeling and Analysis of Complex Systems by Hiroki Sayama
Think Complexity by Allen Downey
Think Python by Allen Downey
An Introduction to Agent-Based Modeling by Uri Wilensky and William Rand (you may need to be on the UM network or connected to the VPN for the download to work)

Interesting papers and extra readings

Ostrom, 1988 - Computer Simulation: the Third Symbol System