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New Mexico Computer Science for AllAgent-based modelingBy Irene LeeDecember 27, 2012
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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
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Agent-based modeling paradigm
The “Observer”– instantiates the world The “Turtles”– the agents The “Patches” – the environment
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Agent based modeling phases Setup– instantiation of world Runtime loop – the agents put into
motion. Exit
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Agent-based modeling Abstractions
Agents with rules Environment or space in which they exist Time
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NetLogo is a programming language
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Creating Computer Models with NetLogo
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Creating Computer Models with NetLogo
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Creating Computer Models with NetLogo
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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.
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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*
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• 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
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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
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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