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New Mexico Computer Science for All. Agent-based modeling By Irene Lee December 27, 2012. Agent-based modeling: a tool for studying complex adaptive systems. Agent-based Modeling of Complex Adaptive Systems - PowerPoint PPT Presentation
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New Mexico Computer Science for AllAgent-based modelingBy Irene LeeDecember 27, 2012
Agent-based Modeling of Complex Adaptive Systems
Using agent-based modeling (ABM) tools, we are able to model complex adaptive systems.
An example: termites modelThe model consists of agents, an environ- ment, and interactions between agents and environment.The system is adaptive and changes over time. ABM generates “emergent” patterns.
Agent-based modeling: a tool for studying complex adaptive systems
Agent-based modeling paradigm
The “Observer”– instantiates the world The “Turtles”– the agents The “Patches” – the environment
Agent based modeling phases Setup– instantiation of world Runtime loop – the agents put into
motion. Exit
Agent-based modeling Abstractions
Agents with rules Environment or space in which they exist Time
NetLogo is a programming language
Creating Computer Models with NetLogo
Creating Computer Models with NetLogo
Creating Computer Models with NetLogo
Modeling and Computational Science
•A model is a representation of the interaction of real-world objects in a complex system.
•The goal is to gain an understanding of how the model’s results relate to real-world phenomena.
•Random factors built into the model and variables changed by the user cause different results to be generated when the model is run repeatedly.
Idea Models e.g. Model of Predator and Prey
Minimal Models for Systems e.g. Model of Wolves and Caribou
Systems Models / Large scale ? e.g. Model of every Wolf and Caribou in 5
square mile section of Yellowstone
*This classification scheme was proposed by J. Roughgarden.
Increasing complexity, detail and specificity
Model Classification Scheme*
• learning about models and modeling• conduct experiments by changing variables,
collecting data, and analyzing results.
• deconstruct models into agents, behaviors, environment, and interactions.
• develop expertise in evaluating models• coding/decoding skills and sustained
reasoning
• Abstraction of a real-world problem into a computer model suitable for testing hypotheses.
• Evaluation of model, choice of assumptions, and findings.
A Progression for Learning about Modeling
Use
Modify
Create
Scientific Inquiry / Critical thinking skillsStudents as creators and young researchersUnderstanding the use of computers in
STEM fieldsPreparation for future endeavors in
computingBuilding an understanding of complex
systems
Preparation for STEM futures
Concepts that modelers must understand to deconstruct
and eventually write agent based models are: 1) states 2) variables3) data structures4) rules, logic and control structures, Boolean operations5) iteration and recursion6) functions, procedures, subroutines 7) syntax of programming 8) interface design9) data analysis (import/export and plot data)10) parallelism.
Preparation for Computer Science