Agent-Based Model

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Agent-based modelling is the modelling of phenomena as dynamical systems of
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Agent-based modelling (ABM) is the modelling of phenomena as dynamical systems of interacting agents. An '''agent-based model''' is a specific individual based computational model for the computer simulation of [[Complex_System|complex systems]]. ABM can be used as abbreviation for both, Agent-Based Modelling or Agent-Based Model.
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interacting agents. An '''agent-based model''' (ABM) is a specific individual based computational model  
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for the computer simulation of [[Complex_System|complex systems]].  
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== Definition ==
== Definition ==
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An agent-based model is a computational model in which every participant in a system  
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An agent-based model is a computational model in which every participant in a system or process is modelled indivdually. A system is modeled as a collection of autonomous decision-making entities called [[Agent|agents]]. Each agent acts autonomously, individually assesses its situation, and makes decisions on the basis of a set of internal rules. In agent-based modeling the word 'model' often means to specify 'the rules of the game': to specify exactly what kind of participants exist (the agents and their states) and how they interact (the rules). As Bonabeau says in his paper about [http://www.pnas.org/cgi/content/full/99/suppl_3/7280 agent-based modeling] "at the simplest level, an agent-based model consists of a system of agents and the relationships between them". ABMs are the equivalent to games in [http://en.wikipedia.org/wiki/Game_theory game theory]. A strategic game in game theory consists of a set of players, a set of moves (or strategies) available to those players, and a specification of payoffs for each combination of strategies. An ABM consists of a set of (simplified) agents, a set of possible basic behaviors or  elementary interactions, and a set of rules which specifies the evolution or development of the system.
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or process is modelled indivdually.
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In agent-based modeling the word 'model' often means to specify 'the rules of the game':  
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to specify exactly what kind of participants exist (the agents and their states) and how  
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they interact (the rules). As Bonabeau says in his paper about  
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[http://www.pnas.org/cgi/content/full/99/suppl_3/7280 agent-based modeling]
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"at the simplest level, an agent-based model consists of a system of agents and the relationships  
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between them". ABMs are the equivalent to games in [http://en.wikipedia.org/wiki/Game_theory game theory].
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A strategic game in game theory consists of a set of players, a set of moves (or strategies) available  
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to those players, and a specification of payoffs for each combination of strategies.
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An ABM consists of a set of (simplified) agents, a set of possible basic behaviors or   
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elementary interactions, and a set of rules which specifies the evolution or development  
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of the system.
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Contrary to general [[Multi-Agent System|multi agent systems]],
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Contrary to general [[Multi-Agent System|multi agent systems]], an ABM describes the interactions among individual [[Agent|agents]] and their environment in a specific situation which leads to particular organizational patterns and [[Emergence|emergent properties]]. Model in 'agent-based model' is used in the sense of an abstract representation of a concrete system from a particular viewpoint. ABMs can be used to explain collective human behavior in the social sciences, to understand [[Complex System|complex systems]], and they enable the user to run large scale virtual experiments without altering the corresponding real [[System|system]].
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an ABM describes the interactions among individual [[Agent|agents]] and their environment
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in a specific situation which leads to particular organizational patterns and  
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[[Emergence|emergent properties]]. Model in 'agent-based model' is used in the  
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sense of an abstract representation of a concrete system from a particular viewpoint.
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ABMs can be used to explain collective human behavior in the social sciences, to  
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understand [[Complex System|complex systems]], and they enable the user to run  
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large scale virtual experiments without altering the corresponding real [[System|system]].
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== Ingredients ==
== Ingredients ==
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Rules define the behavior of the agents.
Rules define the behavior of the agents.
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== Purpose and Results ==  
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== Purpose, Results and Applications ==  
What can be learned from an agent based model?  
What can be learned from an agent based model?  
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* [[Adaptation|Adaptive behavior]], which means agents grow smarter over time in response to the specific conditions and requirements of their environment
* [[Adaptation|Adaptive behavior]], which means agents grow smarter over time in response to the specific conditions and requirements of their environment
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ABMs can simulate almost any interactive system: a stockmarket, a habitat, a pandemics, any social or political system, or a business supply-chain. Agent-based simulations can be used to model
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* animal societies, ecological systems and food webs
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* physiological systems and organisms
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* social or politcal systems
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* financial systems and stockmarkets
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* economic systems and business supply-chains
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* robotic systems
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* traffic and transportation systems, pedestrian dynamics
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* P2P and other computational systems
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* pandemics
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* combats and wars
== The art of agent-based modeling ==
== The art of agent-based modeling ==
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: "The real art of model-building is in selecting the subset of the system that you want to model. Do not attempt to model the entire system. Throw away as much detail about the system as possible. If you build a model that includes all of the information in the system, then the model is no more helpful to you than the system itself".
: "The real art of model-building is in selecting the subset of the system that you want to model. Do not attempt to model the entire system. Throw away as much detail about the system as possible. If you build a model that includes all of the information in the system, then the model is no more helpful to you than the system itself".
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In agent-based modeling less is more. The art is to find the essential ingredients of the model.
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The difficulty is to find the right balance between oversimplification and overelaboration, between too much and too little detail. In agent-based modeling less is more. The art is to find the essential ingredients of the model.
George Johnson says in his book "Fire in the Mind" (Vintage, 1996):
George Johnson says in his book "Fire in the Mind" (Vintage, 1996):
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* [[Segregation Model]] created in 1971 from Thomas Schelling
* [[Segregation Model]] created in 1971 from Thomas Schelling
* [[Tribute Model]] and [[Dissemination Model]] from Robert Axelrod
* [[Tribute Model]] and [[Dissemination Model]] from Robert Axelrod
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An issue of the JASSS mentions 10 fundamental and famous models, see [http://jasss.soc.surrey.ac.uk/12/1/6.html#appendixB]
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* Schelling's (1971) model of spatial segregation
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* Axelrod's (1997) model of dissemination of culture
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* Epstein and Axtell's (1996) Sugarscape
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* Miller and Page's (2004) standing ovation model
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* Arthur's (1989) model of competing technologies
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* Axelrod's (1986) metanorms models
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* Takahashi's (2000) model of generalized exchange
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* Kinnaird's (1946) truels
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* Axelrod and Bennett's (1993) model of competing bimodal coalitions
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* Joyce et al.'s (2006) model of conditional association
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== Models and Plays ==
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Can agent based models and traditional plays
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(for example Shakespeare's plays) be considered as two
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extremes on one scale? In both we witness the outcome of a
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small number of agents or actors interacting
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with each other in a particular environment
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according to certain rules and intentions.
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Shakespeares' plays are basic "models" for the
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complexity which arises through "love in hate"
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(Romeo and Juliet), "hesitation in action" (Hamlet)
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or "striving against destiny" (Macbeth). In
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Shakespeares' words "Though this be madness,
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yet there is method in't."
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Can plays be considered as a kind of model for systems
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with complex BDI agents and abstract rules?
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If a developer programs an object-oriented system,
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he determines in detail what every object should do,
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step by step, in every possible situation.
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What happens if we write advanced programs for agent-
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oriented systems, would they look like plays from
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a playwright? Or would we have to program
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beliefs, desires, emotions and intentions?
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In plays, the actors are driven by basic emotions
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and intentions. One theme is "love in hate" or
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"desire in vengeance" which appears in "Romeo and
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Juliet". Among others, the "desire in vengeance"
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theme can also be found in Hamlet: Ophelia and
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Hamlet love each other, but Hamlet kills her
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father accidentally, she kills herself, then her
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brother Laertes takes revenge, and in the end
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they are all dead.
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The playwright usually describes one of many
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possible plots or outcomes for a certain theme,
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here for instance what could happen if love and
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hate collide. The basic motives are as simple as
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the rules in basic ABMs. This is why I said
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that ABMs and plays can be considered as two
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extremes on one scale:
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ABMs:
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* simple agents, driven by basic rules
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* outcome (the sequence of actions) is not determined
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Plays:
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* complex actors, driven by basic emotions and intentions
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* outcome (the sequence of actions) is determined
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== Literature ==
== Literature ==

Latest revision as of 22:39, 16 March 2011

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