Course Description
Theory and applications of agent-based models (ABM) for ecosystem science and management. Students will develop an understanding of the conceptual foundation for designing models, coding models, conducting simulation experiments in NetLogo, and analyzing a model to develop a theory for complex adaptive systems. No coding/programming background is required.
Athena Title
Agent Based Modeling Ecology
Prerequisite
Permission of department
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
In this course students will learn: 1. Definitions and examples of complex adaptive systems, emergence, and stigmergy 2. What is agent-based modeling and what it can do 3. How to make model assumptions to meaningfully simplify reality 4. Logic of programming, i.e., functions, programs, and algorithms 5. Practical examples to build models in NetLogo 6. How to work collaboratively with other students on models Students will demonstrate these learning outcomes through: 1. Leading discussion on peer-reviewed articles 2. Turning in coding exercises (with write-ups) both individually and in groups 3. Evaluating each other’s models 4. Completing and sharing their model project via oral presentation and live model demonstration
Topical Outline
1. Introduction to ABM/IBM 2. Introduction to NetLogo 3. ODD protocol (overview, design concepts, and details) 4. ABMs for science 5. Pattern-oriented models 6. Model testing 7. Emergence 8. Sensing and adaptive behavior 9. Stochasticity 10. Model elements 11. Parameterization and calibration 12. Model analysis 13. Sensitivity/Uncertainty 14. What makes a good model? -- Synthesis
Syllabus
Public CV