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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

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new mexico computer science for all

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: a tool for studying complex adaptive systems

Agent-based Modeling of Complex Adaptive Systems

Using agent-based modeling (ABM) tools, we are able to model complex adaptive systems.

An example: termites model

The 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 paradigm
Agent-based modeling paradigm
  • The “Observer”– instantiates the world
  • The “Turtles”– the agents
  • The “Patches” – the environment
agent based modeling phases
Agent based modeling phases
  • Setup– instantiation of world
  • Runtime loop – the agents put into motion.
  • Exit
agent based modeling abstractions
Agent-based modeling Abstractions
  • Agents with rules
  • Environment or space in which they exist
  • Time
modeling and computational science
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.
slide11

Model Classification Scheme*

  • 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

Increasing complexity, detail and specificity

*This classification scheme was proposed by J. Roughgarden.

slide12

A Progression for Learning about Modeling

Use

Modify

Create

  • 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.
slide13

Preparation for STEM futures

Scientific Inquiry / Critical thinking skills

Students as creators and young researchers

Understanding the use of computers in STEM fields

Preparation for future endeavors in computing

Building an understanding of complex systems

slide14

Preparation for Computer Science

Concepts that modelers must understand to deconstruct

and eventually write agent based models are:

1) states

2) variables

3) data structures

4) rules, logic and control structures, Boolean operations

5) iteration and recursion

6) functions, procedures, subroutines

7) syntax of programming

8) interface design

9) data analysis (import/export and plot data)

10) parallelism.