This course on Introduction to NetLogo aims to teach participants about agent-based modeling (ABM) and its applications in understanding complex systems in social sciences and economics. The course covers topics such as genetic algorithms, game theory, network analysis, time series analysis, and the benefits of appropriate representation in modeling. The teaching method includes lectures, examples of emergence like traffic flow, and discussions on the use of ABM compared to other modeling techniques. This course is intended for early graduate students, postdocs, young faculty, and advanced undergraduate students interested in developing quantitative skills for studying complex systems in social sciences and economics.
Overview
Syllabus
Introduction to NetLogo by Bill Rand
Model 1: Example of Emergence - Traffic
Model 2
Model 3: Non - Linear Effect
Model 4
Representation
Benefits of Appropriate Representation
Representation of Complex Systems
A Third Way of Doing Science
Integrative Understanding
Differential Understanding
Why ABM and When to Use It?
When to use ABM?
Medium Numbers
Heterogeneity
Complex but Local Interactions
Rich Environments
Time
Adaptation
Agent-Based Modeling ABM vs. Equation-Based Modeling EBM
ABM and Statistical Modeling
ABM vs. Lab Experiments
ABM vs. Aggregate Computer Modeling
Limitations
Why the Resistance?
Uses of ABM
Description
Explanation
Experimentation
Analogy
Education / Communication
Touchstone
Thought Experiments
Prediction
Complex Systems, Agent-Based Modeling and Psychohistory
Taught by
International Centre for Theoretical Sciences
