Agent Based Modeling (ABM)

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Agent Based Modeling(ABM)

• Stephen Kinsel

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Outline

• What is ABM?
• Why use ABM?
• Applications
• Examples
• Good Modeling Practices
• Issues
• Future of ABM

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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)
• General Characteristics
• Autonomy
• Pro-activeness
• Reactivity
• Social Ability

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What is ABM?

• Simulation modeling technique where a system is
  modeled as a collection of agents and the
  relationships between them.
• Agents individually asses its situation in the
  environment and make decisions on the basis of a
  set of rules.
• - Bonabeau

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Agent Types (DeLaurentis)
Mobile Agent
Autonomous Agent
Adaptive Agent
Yes
Yes
Does it run without continuous user input?
Yes
Can it change its behavior based on past
experiences
Does it move?
No
Agent
Does it have a set solution path?
Reactive Agent
Require Assistant User?
Yes
Does it collect, filter classify information?
Yes
No
Does it care about the utility value?
Interface Agent
Utility Agent
Yes
Yes
No
Info-gathering Agent
Agents can possess more than one property
Goal-based Agent
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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 / distance)

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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)
• This characteristic makes the output of a system
  difficult to understand and predict

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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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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
  systems processes
• Heterogeneous units

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Heterogeneous Components of a System
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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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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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Areas of Application

• Flow
• evacuation, traffic
• Financial Markets
• Organizations
• organizational design, strategy
• Social

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ABM Generic Example - BOIDS
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 Reynolds
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ABM Generic Example - Evacuation
Stampede Situation

• People become injured when they collide at a
  certain speed
• As a consequence, leaving the room becomes
  difficult.

Stampede Situation w/ Column

• A column in front of the door can avoid injuries.
  
• It can increase the outflow well with less / no
  injured people

Helbing, Farkas, Vicsek
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ABM Generic Example Traffic Control
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Good Modeling Practices

• Choosing the language that is right for you and
  the problem
• Goals of Good AB Programming
• Project Management

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Some Recommended Programs / Languages

• StarLogo
• Programmable modeling environment for new
  programmers
• Swarm
• For advanced programmers
• Wide variety of tools
• Languages
• Basic
• Easy to learn and use, but suitable for small
  projects
• Pascal
• Designed to be a first language for serious
  programmers, and easy to learn and is structured
  to encourage good programming habits
• C, C
• Most commonly used among serious programmers.
• Allows easy conversion between separate computers
• OO languages make really large projects easier to
  program

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Goals (Axelrod)

• Validity
• Would like to correctly implement the model
• 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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Goals (Axelrod)

• 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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Project Management (Axelrod)

• How can I achieve these goals?
• Use long names for almost all variables
• List all the variables at the start of the
  program
• Write helpful comments
• Fully label output
• Develop upwardly compatible programs
• Document versions of each code
• Use commercial programs for most data analysis
• Check microdynamics
• Communication with other users

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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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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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Future of ABM

• New Software for Modeling
• Research in BDI Architecture

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• References
• Shoham, Yoav, BDI agents From Theory to
  Practice, 1993
• Bonabeau, Eric, Agent-based modeling Methods and
  techniques for simulating human behavior, 2002
• Roetzheim, William H., Enter the Complexity Lab,
  Where Chaos Meets Complexity, 1994
• Reynolds, Craig W., Flocks, herds and schools A
  distributed behavioral model, 1987
• Helbing, Dirk Farkas, Illes Vicsek, Tamas,
  Simulating Dynamical Features of Escape Panic,
  2000
• Axelrod, Robert, The Complexity of Cooperation,
  1998