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Agent Based Modeling (ABM). Stephen Kinsel. Outline. What is ABM? Why use ABM? Applications Examples Good Modeling Practices Issues Future of ABM. What is ABM?. First: What is an agent? - PowerPoint PPT Presentation
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Agent Based Modeling(ABM)
Stephen Kinsel
• What is ABM?
• Why use ABM?
• Applications
• Examples
• Good Modeling Practices
• Issues
• Future of ABM
Outline
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
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
Agent Types (DeLaurentis)
AgentAgentAgentAgent
Does it move?
Does it have a set solution path?
Does it run without continuous user input?
Does it collect, filter & classify information?
Require Assistant User?
Can it change its behavior based on past experiences
Does it care about the utility value?
Info-gatheringAgent
Info-gatheringAgent
MobileAgent
MobileAgent Autonomous
Agent
AutonomousAgent
InterfaceAgent
InterfaceAgent
ReactiveAgent
ReactiveAgent
AdaptiveAgent
AdaptiveAgent
UtilityAgent
UtilityAgent
Goal-basedAgent
Goal-basedAgent
Yes
Yes
Yes
YesYes
Yes
Yes
No
No
No
Agents can possess more than one property
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)
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
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.
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
Heterogeneous Components of a System
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
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)
Areas of Application
• Flow– evacuation, traffic
• Financial Markets
• Organizations– organizational design, strategy
• Social
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
Reynolds
BOIDS
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
ABM Generic Example – Traffic Control
Good Modeling Practices
•Choosing the language that is right for you and the problem
•Goals of Good AB Programming
•Project Management
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
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?
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
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
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
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
Future of ABM
• New Software for Modeling
• Research in BDI Architecture
ReferencesShoham, 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