CMPLXSYS 530 - Computational Modeling of Complex Systems

Course information

Course time & location

Tuesdays and Thursdays, 1 - 2:30pm
Angell Hall G115
The course will be in person, but 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, from 2:20-3pm on Tuesdays and Thursdays (but we can set up other times as needed if those don’t work)
GSI: Chethan Prakash
- Email: chethanp@umich.edu
- Office Hours
  - Regular office hours: 703 Weiser Hall
    - Time TBD
  - By appointment in 715A Weiser Hall

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: Get comfortable with python if you haven’t used it much! You can use Google Colab to get started or use your own python installation if you prefer. See the Additional useful info section below for tutorials and more info!

Lecture 2: Introduction to Agent-Based Models
- 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)

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

Lab 1: Introduction to ABMs, NetLogo, and Python
- Readings
  - Wilensky and Rand, Chp. 3-4

Intro to the PyCX library for simulating complex systems models in Python
- Download from the PyCX GitHub
- 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!
- Interesting links & extra things

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
- Readings
  - Sayama, Chp. 11
  - Think Complexity, Chp. 6
- Miscellaneous interesting things

Lecture 5: Cellular automata dynamics
- Readings
  - Sayama, Chp. 12
  - Think Complexity, Chp. 7

Lab 2: Cellular Automata and 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
- Readings

Lecture 7: Random networks, dynamics on networks and of networks
- Readings
  - Sayama, Chp. 16
  - Think Complexity, Chp. 4 and 5

Lecture 8: Adaptive networks, mean-field approximation of networks –>
- Readings
  - Sayama, Chp. 18

Lab 3: Network Dynamics

4. Parameter sweeps, sampling, and sensitivity analysis

Lecture 9: Sampling, visualization, uncertainty
- 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
- 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: Analyzing Model Outputs

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

Lab 4: Parameter sampling and sensitivity

5. Parameter estimation from data

Topics covered: parameter estimation, MCMC, parameter identifiability & uncertainty, model comparison (e.g. AIC/BIC/etc)

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

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.
- 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

Lecture 16: Model selection and the AIC

6. Advanced/additional topics

Potential Topics:
- decision/game theory
- chaos and fractals
- classic example complex systems models (Kuramoto oscillators, Sugarscape, etc.)
- more advanced environments (e.g. putting ABMs on GIS)
- active subspaces, dimension reduction
- model selection
- clustering, PCA
- machine learning and complex systems
- webpage scraping, basics of API’s, pulling data from various places (social media, google, government sources)

Lecture 17 and 18: Machine learning and AI
- Readings
  - Artificial intelligence in medicine and the disclosure of risks, 2020. AI Soc. 2021; 36(3): 705–713.
  - Obermeyer et al 2019, Dissecting racial bias in an algorithm used to manage the health of populations
  - A nice series of articles on mechanistic interpretability

Lecture 19: ABM Environments and Mapping/GIS
- 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 20: Game & decision theory
- Readings

Additional useful info

Useful links and extra readings will be posted here. (Some of these are a bit outdated now but keeping them just in case they’re helpful!)

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

ABM Resources

Modules/packages for agent-based modeling
- PyCX module for Python (we will use this!) (reference for PyCX)
- 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