This is a tentative syllabus; as we learn more about your interests and background, we may tailor the contents and timeline to better meet our overall class demographics. Notably, you'll initially see some large gaps that we'll fill in as we proceed.
Grading will be based primarily on a research project (80%); for masters and Ph.D. students, this research project will ideally form the kernel of a chapter of your thesis. The remainder of our grade will be determined by three "notebook checks" (30%), which will demonstrate your exploration and experimentation with programming and modeling concepts. (N.B.: 80+30>100, so that you may either "catch up" on missed project points or treat one notebook as optional)
| Date | Readings | Lecture | Notebooks | Practice | Notebook Checks | Project |
|---|---|---|---|---|---|---|
| 1/6/2016 | -Why Model, Epstein (2008) -A Brief History of Simulation, Revisited, Goldsman, Nance, Wilson (2010) |
Why do we model? | Basic Distributions | |||
| 1/8/2016 | -Code Academy Python: Unit 1 -Code Academy Python: Unit 2 |
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| 1/13/2016 | -Think Complexity, Downey: Chapter 1 -Think Python, Downey: Chapter 1 |
History of uncertainty and probability | Monte Carlo | |||
| 1/15/2016 | -Code Academy Python: Unit 3 -Code Academy Python: Unit 4 |
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| 1/20/2016 | -Think Complexity, Downey: Chapter 2 |
Graph basics | Basic Networks | Model Brainstorming - assigned (5%) | ||
| 1/22/2016 | -Code Academy Python: Unit 5 -Code Academy Python: Unit 6 -Code Academy Python: Unit 7 |
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| 1/27/2016 | -Think Complexity, Downey: Chapter 4, 5 |
Types of graphs and graph statistics | SIR | Model Brainstorming - due | ||
| 1/29/2016 | -Code Academy Python: Unit 8 -Code Academy Python: Unit 9 |
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| 2/3/2016 | -Think Complexity, Downey: Chapter 7 |
Spatial basics | Basic grids | |||
| 2/5/2016 | -Code Academy Python: Unit 11 -Code Academy Python: Unit 12 |
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| 2/10/2016 | -Think Complexity, Downey: Chapter 10 |
Types of space and spatial statistics | Schelling | |||
| 2/12/2016 | ||||||
| 2/17/2016 | -Networks, Crowds, and Markets, Chapter 6, Easley & Kleinberg (2010) | Prisoner's Dilemma | ||||
| 2/19/2016 | Notebook Check #1 (10%) | |||||
| 2/24/2016 | -Networks, Crowds, and Markets, Chapter 7, Easley & Kleinberg (2010) | Prisoner's Dilemma, Part #2 | ||||
| 2/26/2016 | Model Proposal (10%) | |||||
| 3/2/2016 | -Networks, Crowds, and Markets, Chapter 8, Easley & Kleinberg (2010) | |||||
| 3/4/2016 | ||||||
| 3/9/2016 | -Social network analysis and agent-based modeling in social epidemiology, El-Sayed (2012) | pandas | ||||
| 3/11/2016 | ||||||
| 3/16/2016 | Storing model results | |||||
| 3/18/2016 | ||||||
| 3/23/2016 | Reading model results | |||||
| 3/25/2016 | Midterm Assessment (25%) | |||||
| 3/30/2016 | -Causality and Statistical Learnings, Gelman | Analyzing model results | ||||
| 4/1/2016 | Notebook Check #2 (10%) | |||||
| 4/6/2016 | ||||||
| 4/8/2016 | Show-and-tell | |||||
| 4/13/2016 | Show-and-tell | |||||
| 4/15/2016 | Show-and-tell | |||||
| 4/20/2016 | No class | Notebook Check #3 (10%) | ||||
| 4/22/2016 | No class | |||||
| 4/27/2016 | No class | |||||
| 4/29/2016 | Final Assessment (40%) |
- Markdown comments at the top of your notebook (10%)
- State the problem or question you are addressing in a single sentence.
- Identify why the problem that you are attempting to answer is amenable to an ABM, Networks, or other computational approach.
- Outline, at a high level, how you will use your model to evaluate your question and the various pieces of your model. Identify a mechanism or process, how you will perform an experiment with its parameters, and what you will measure.
- Code Notebook (80%)
- Brief overview of your model (10%)
- Outline what your model is meant to do and how it will answer your question
- Code Notebook (70%)
- List of the sweep parameters that are important to answering your question in the model and the processes / mechanisms that they are involved in. List of the outcome measures that your parameter sweeps are going to affect (10%)
- Initialization of your parameters and variables (10%)
- Definition of all classes and preliminary code (10%)
- Definition of all constructors and preliminary code (10%)
- List all methods (i.e., setup(), step(), moveAgents(), infectAgents()) and preliminary code for at least 50% of methods (10%)
- Preliminary code of your step method (10%)
- Quality of the comments so that your code is easy to follow and the steps for future work and where they will be implemented be in your code is evident (10%)
- Brief overview of your model (10%)
- Markdown comments at the bottom of your notebook (10%)
- Overview of the results you hope present (5%)
- Hypotheses about what results you will see in the future (5%)
You can find a sample midterm from last year's class here.
- Markdown comments at the top of your notebook (10%)
- State the problem or question you are addressing in a single sentence.
- Identify why the problem that you are attempting to answer is amenable to an ABM, Networks, or other computational approach.
- Outline, at a high level, how you will use your model to evaluate your question and the various pieces of your model. Identify a mechanism or process, how you will perform an experiment with its parameters, and what you will measure.
- Make sure to provide brief background or literature review on any extant research or models you're leaning on.
- Code Notebook (50%)
- Brief overview of your model (5%)
- Outline what your model is meant to do and how it will answer your question
- Code Notebook (45%)
- List of the sweep parameters that are important to answering your question in the model and the processes / mechanisms that they are involved in. List of the outcome measures that your parameter sweeps are going to affect (5%)
- Initialization of your parameters and variables (5%)
- Definition of all classes and code (5%)
- Definition of all constructors and code (5%)
- Definition of all model methods (i.e., setup(), step(), moveAgents(), infectAgents()) (20%)
- Quality of the comments so that your code is easy to follow and the steps for future work and where they will be implemented be in your code is evident (10%)
- Brief overview of your model (5%)
- Analysis of Model Results (40%)
- Description of output or outcome measure (5%)
- Description and justification of analysis method used to test hypothesis (5%)
- Analysis of results and testing of hypothesis (20%)
- Summary paragraph, table, or visualization presenting conclusion on hypothesis (10%)