This document discusses agent-based modeling (ABM) and provides an overview of the topic. It defines what an agent is and describes some key characteristics of agents. Examples are given of how agents can represent different entities in a traffic control system. The document outlines why ABM is useful, including its ability to capture emergent phenomena and provide a natural description of systems composed of autonomous entities. Several applications of ABM are listed, such as modeling traffic flows, financial markets, and social systems. The document also discusses some examples of generic ABMs, including models of bird flocking behavior and traffic control. It outlines some goals and issues in ABM development and discusses the future of the field.

1. The World of Agent-based model
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Nguyễn Thị Ngọc Anh
ngocanhfami@gmail.com
Hà Nội, 10/3/2016

2. Outline
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• What is ABM?
• What is EBM?
• What is HBM?
• M2SC?

3. What is ABM?
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

4. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

5. What is ABM?
• First: What is an agent?
• An entity that functions continuously and
autonomously in an environment in which
other processes take place and other agents
exist [Shoham 2001]
• General Characteristics:
Autonomy
Pro-activeness
Reactivity
“Social” Ability
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Agent Types in an
Example: Traffic Control• Reactive: Police (enforce laws of road)
• Info-gathering: Media (informs the public of
traffic and accidents in major areas)
• Autonomous: Disruptors (weather / accidents)
• Goal-based: City Planners (would like the least
number of accidents and greatest amount of
flow through parts of town)
• Adaptive: Drivers (may avoid roads that are
known to be overcrowded during certain times
of day)
Utility: Drivers (would like to minimize drive time /

8. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues, tools
• Future of ABM

9. Why Use ABM?
• Captures Emergent Phenomena
• As the components of a system interact with
each other, and influence each other through
these interactions, the system as a whole
exhibits emergent behavior [Roetzheim, 2008]
• This characteristic makes the output of a
system difficult to understand and predict
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10. Emergence Example
• Group of 10 – 40 people
• Each member randomly chooses two people,
person A and person B.
• Members move themselves so that A is
between themselves and B
• Now move so that member is between A and
B.
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11. Why Use ABM?
• Provides a Natural Description of a System
composed of “behavioral” entities
• Describes the system from the perspective of
its constituent units’ activities more so than the
system’s processes
• Heterogeneous units
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12. Heterogeneous
Components of a System
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14. Why Use ABM?
• Flexibility
• What if the appropriate level of description or
complexity is not known ahead of time
• Easy to add / subtract more agents
• Tuning the complexity of the agents
• Changing behaviors, degree of rationality,
rules of interactions, etc
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15. Traditional vs. ABM
simulation
• ABM seeks “adaptive” rather than “optimizing”
nature
• Adapt: seek the rule and behavior set that lead
to new capabilities
• ABM does not emphasize analytical solutions
(more qualitative than quantitative)
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16. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

17. Areas of Application
• Flow: evacuation, traffic
• Financial Markets
• Organizations: organizational design, strategy
• Social science/ natural science
• Ecology
• Biology
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20. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

21. ABM Generic Example -
BOIDS• Separation: steer to avoid
crowding local flockmates
• Alignment: steer towards the
average heading of local
flockmates
• Cohesion: steer to move
toward the average position of
local flockmates
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23. ABM Generic Example –
Traffic Control
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26. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

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31. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

32. Goals
• Validity
• Would like to correctly implement the mode
• Is the model itself an accurate representation
of the “real world”?
• Problem: If there are unexpected results, is
there necessarily a mistake?
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33. Goals
• Usability- Allow yourself and other users
who follow to run the program, interpret its
output, and understand how it works.
Careful with different versions of the model
• Extendability
Allow future users (including yourself) to
adapt the program for new uses
New questions arise from models such as
these
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34. Issues with ABM
• Validity
Every model serves its own unique purpose
Must be built at the right level of description
with the appropriate amount of complexity
• Human agents
Complex Psychology
Irrational Behavior
Subjective Choices
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35. Issues with ABM
• Qualitative vs. Quantitative
Varying degree of accuracy and completeness
in input (data, expertise, etc)
Use qualitative data to learn about the system
• Capturing the behavior of all constituent units
Lower level description can extremely
computationally intense, and time consuming
Heterogeneous units
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36. Outline
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• What is ABM?
• Why use ABM?
• Applications
• Examples
• Tools
• Issues
• Future of ABM

37. Future ABM
• What applications can be applied?
• The protocol for representation ABM?
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38. What is EBM?
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• What is EBM?
• Why use EBM?
• Applications

39. What is HBM?
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• What is HBM?
• Why use HBM?
• Applications

40. Reference
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41. Thank you for your attention
The End
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