Agent-Based Model
Implementation
Lecture 1
DR. NAZIA N. ARBAB
R U TG E R S U N I V E R S I T Y
F E B R UA R Y 2 8 T H , 2 0 1 7
Contents
1. Introduction
2. Types of Models
• Inductive
• Deductive
• Abductive
3. Introduction to Netlogo
4. Netlogo Model Exploration
Introduction
Reasoning
• Investigation
• Drawing conclusion
• Predictions
• Phenomena explanation
Knowledge can be derive in different ways using different model-based
reasoning
•Inductive
•Deductive
•Abductive
Introduction
What is model
Construction of behavior and rules, representation of occurrence, structure of
phenomenon, simplification of reality.
Any other?
Examples of models:
Weather models, Land use models, Biodiversity models, Environmental models
Traffic Models
Use of Model
• Testing Hypotheses
• Scenario Analysis
• Drawing Conclusions
• Investigations of observations that are difficult to observe or available (data limitations)
• Any other use?
Deductive Reasoning
•
•
•
•
•
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General to the more specific.
Also known as "top-down" approach
Narrow in nature.
Testing or confirming hypotheses.
Example:
Controlled experiments to derive
observations for comparisons with
the predicted behavior.
Inductive Reasoning
• Specific observations to broader
generalization and theories.
• Also referred as a "bottom up"
approach.
• To investigate patterns and
phenomenon observation.
• Formulating tentative hypotheses to
explore, and developing some general
conclusions or theories.
Abductive Reasoning
Abductive reasoning typically begins with an incomplete set of observations and proceeds to the
most possible explanation for the set.
In general, abductive reasoning is the logical process (inference) where one chooses a hypothesis
that would best fit the given observations and uncertainty.
Examples
Decision making with incomplete information
Medical diagnose based upon symptoms.
Agent-based model
Agent-based Modeling
Individual behaviors result into emergent properties
"Agents" have particular behavior based upon rules under certain assumptions.
The simulation of such system has observable properties.
what level (scale) do we describe the entities of the system?
Individuals
Households
Communities
Municipalities
What are the agents? What are their attributes and their interaction with other agents and
their environment?
Agent-based model (Macal and North,
2006)
What is an agent?
– A discrete entity with its own goals and behaviors
– Autonomous, with a capability to adapt and modify its behaviors
Assumptions
– Some key aspect of behaviors can be described .
– Mechanisms by which agents interact can be described.
– Complex social processes and a system can be built “from the bottom up.”
Examples
– people, insects, swarms
– Robots, systems of collaborating robots
– Agents are diverse and heterogeneous
Introduction to NetLogo
Multi-agent programmable modeling environment suitable for complex system simulations.
Enable to explore the micro-level behavior of individuals and resulting macro-level patterns that
emerge from the interaction of many individuals (agents).
NetLogo Elements
NetLogo world made of following agents:
Patches, Turtles, observer , links
NetLogo interface
Tabs: Interface, Information, Code
Buttons
Sliders
Switches
Monitors
Plots
Model Exploration
Model Run and observations
Individual behavior
Emerging properties
NetLogo (Dispersal model)
Examples from NetLogo Model Library
Suggested Reading
Steven Railsback and Volker Grimm
http://www.railsback-grimm-abm-book.com/book-contents.html
References
Charles M. Macal and Michael J. North (2006). “Introduction to Agent-based Modeling and
Simulation.” MCS LANS Informal Seminar, Argonne National Laboratory, Argonne, IL 60439 USA
http://www.mcs.anl.gov/~leyffer/listn/slides-06/MacalNorth.pdf