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Agent Based Modeling (ABM)

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?

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Agent Based Modeling (ABM)

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  1. Agent Based Modeling(ABM) Stephen Kinsel

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

  3. 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

  4. 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

  5. Agent Types (DeLaurentis) Mobile Agent Autonomous Agent Adaptive Agent Yes Yes Yes Does it run without continuous user input? 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

  6. 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)

  7. 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

  8. 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.

  9. 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

  10. Heterogeneous Components of a System

  11. 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

  12. 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)

  13. Areas of Application • Flow • evacuation, traffic • Financial Markets • Organizations • organizational design, strategy • Social

  14. 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

  15. 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

  16. ABM Generic Example – Traffic Control

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

  18. 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

  19. 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?

  20. 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

  21. 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

  22. 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

  23. 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

  24. Future of ABM • New Software for Modeling • Research in BDI Architecture

  25. 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

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