CMPLXSYS 530 - Computer Modelling of Complex Systems

Course information

Course time & location

Tuesdays and Thursdays, 1 - 2:30pm
Class will mainly be held in person, in Weiser Hall 269
The course will be in person, but some office hours and occasional course meetings may also be held via zoom as needed, at: https://umich.zoom.us/j/95806757765 (password information is on Canvas)

Instructors & office hours

Instructor: Prof. Marisa Eisenberg (she/her)
- Email: marisae@umich.edu
- Office Hours - Weiser Hall 735
  - Typically after class, for an hour on Tuesdays and a half hour on Thursdays (but we can set up other times as needed if those don’t work)
  - Usually in person but sometimes via zoom (link above)
GSI: Conrad Kosowsky
- Email: coko@umich.edu
- Office Hours
  - Monday 3-4pm and Tuesday 10-11am in Weiser Hall 703
  - By appointment next to Weiser Hall 713

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!
Final project information - information on the course final project (will be updated as we go)
Final project proposal template

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
- Slides - what are models, why model, emergent behavior, complex systems
- Emoji simulator: forest fire model, blank model, more models!
- Readings
- To Do: download NetLogo and try out python if you haven’t before! You can use Google Colab to get started or install python on your computer (e.g. the Anaconda distribution). See the Additional useful info section below for tutorials and more info!

Lecture 2: Introduction to Agent-Based Models
- Slides - how to choose a modeling framework, intro to ABMs
- Readings
  - Think Complexity, Chp. 1
  - 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)

Lab 1: Introduction to ABMs, NetLogo, and Python
- Lab 1 Assignment
  - For emoji, you can copy-paste from here: https://emojipedia.org/
  - Quick intro to NetLogo
  - Template code for the Python portion
- Readings
  - Wilensky and Rand, Chp. 3-4

Lecture 3: Case study: Modeling the Ancestral Puebloan communities of Long House Valley (the Artificial Ancestral Puebloan Project (AAP))
- Slides
- 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

Intro to the PyCX library for simulating complex systems models in Python
- Intro to PyCX (pdf version)
- Download from the PyCX GitHub
- PyCX example files
  - PyCX Simulator file - 2019 version - use this if the current version causes issues!
  - PyCX model template script
  - PyCX voter model example
  - PyCX in Google Colab - Unfortunately the GUI part of PyCX doesn’t run in Google Colab, but this demonstrates how to run PyCX files in Colab or other Jupyter notebook types of things.
- Readings
  - Sayama, Chp. 10
  - Sayama, H. (2013). PyCX: a Python-based simulation code repository for complex systems education. Complex Adaptive Systems Modeling, 1(1), 1-10.

Extra Notes: Chaos Game!
- Board notes
- Interesting links & extra things
  - Chaos game simulator - lets you try out chaos games with other shapes and settings, also includes some proofs for why the Chaos Game works
  - Another chaos game example
  - A somewhat systematic exploration of some chaos game simulations
  - Barnsley fern
  - Sierpinski Tetrahedrons
  - Neat fractal landscapes

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 4: Intro to cellular automata
- Slides
- Readings
  - Sayama, Chp. 11
  - Think Complexity, Chp. 6
- Miscellaneous interesting things
  - A neat continuous version of Conway’s Game of Life! (thanks to Chethan Prakash for sharing!) And another video with more explanation of how the model works
  - COVID Cellular Automata model
  - Cellular Automata used for a herd immunity explainer on NPR
  - Visualizing 3D, 4D, and 5D Cellular Automata

Lecture 5: Cellular automata dynamics

Lab 2: Cellular Automata and Intro to Networks
- Lab 2: Cellular Automata & Intro to Networks
- A collection of 1-dimensional cellular automata
- NetworkX tutorial (from the NetworkX documentation)
- Network data from Mark Newman

3. Networks

Lecture 6: Introduction to Networks
- Slides
- Little demo of modularity maximization and betweenness community detection methods (since this came up in class!)
- Readings

Lecture 7: Random networks, dynamics on networks and of networks

Lecture 8: Adaptive networks, mean-field approximation of networks

Lab 3: Network Dynamics
- Lab 3: Dynamics on and of Networks

4. Parameter sweeps, sampling, and sensitivity analysis

Lecture 9: Sampling, visualization, uncertainty
- Slides
  - Example code - Latin hypercube sampling with an SIS 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 10: Sensitivity analysis
- Slides
- Example code - Sensitivity analysis with the SIS network model
- 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

Lecture 11: Model Analysis
- Slides

Lab 4: Parameter sampling and sensitivity
- Lab 4: Parameter sampling and sensitivity
- Google Colab Predator Prey Model

5. Advanced/additional topics

Potential Topics:
- parameter estimation, MCMC
- parameter identifiability & uncertainty
- model comparison (e.g. AIC/BIC/etc)
- decision/game theory
- parallelization in Python
- classic example complex systems models (Kuramoto oscillators, Sugarscape, etc.)
- more advanced environments (e.g. GIS)
- active subspaces, dimension reduction

Lecture 12: Parallel Computing
- Slides
- Example Code
- Additional tools for more advanced parallel computing:
  - https://numba.pydata.org/
  - https://dask.org/

Lecture 13: Introduction to parameter estimation
- Thinking about the challenges of linking data with models, and what these approaches can (and can’t) tell you
- Maximum likelihood methods, optimization basics
- Slides
- Example code - Basic optimization
- Example code - Maximum likelihood
- Likelihood example for a teeny network

Lecture 14: Introduction to Bayesian parameter estimation & MCMC
- Sampling based methods, including basics of approximate Bayesian computation, sample importance resampling, MCMC (Markov Chain Monte Carlo), etc.
- Slides
- Example code - Basic MCMC
- MCMC Mean Field SIR Model
- MCMC Mean Field SIR Model - Identifiability Issues
- ABC Rejection Sampling Example
- 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.
- A big compendium of packages to run MCMC in python

Lecture 15: Identifiability
- Slides

Lecture 16: Game & decision theory

Lecture 17: ABM Environments
- Slides
- Intro to mapping with Folium
- Predator-prey bubble tea example
- 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.

Lecture 18: Model selection and the AIC
- Slides

Lecture 19: Clustering methods
- Slides

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.
  - 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)
- CoMSES ABM Library - potentially useful for project ideas!

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