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Agent Based Modeling and Artificial Life

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Encyclopedia of Complexity and Systems Science

Definition of theSubject

Agent‐based modeling began as the computational armof artificial life some 20 years ago. Artificial life isconcerned with the emergence of order in nature. How do systemsself‐organize themselves and spontaneously achievea higher‐ordered state? Agent‐based modelingthen, is concerned with exploring and understanding theprocesses that lead to the emergence of order throughcomputational means. The essential features of artificial lifemodels are translated into computational algorithms throughagent‐based modeling. With its historical roots inartificial life, agent‐based modeling has becomea distinctive form of modeling andsimulation. Agent‐based modeling is a bottom‐upapproach to modeling complex systems by explicitly representingthe behaviors of large numbers of agents and the processes bywhich they interact. These essential features are all that isneeded to produce at least rudimentary forms of emergentbehavior at the systems level. To understand...

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Abbreviations

Adaptation:

The process by which organisms (agents) change their behavior or by which populations of agents change their collective behaviors with respect to their environment.

Agent‐based modeling (ABM):

A modeling and simulation approach applied to a complex system or complex adaptive system, in which the model is comprised of a large number of interacting elements (agents).

Ant colony optimization (ACO):

A heuristic optimization technique motivated by collective decision processes followed by ants in foraging for food.

Artificial chemistry:

Chemistry based on the information content and transformation possibilities of molecules.

Artificial life (ALife):

A field that investigates life's essential qualities primarily from an information content perspective.

Artificial neural network (ANN):

A heuristic optimization and simulated learning technique motivated by the neuronal structure of the brain.

Autocatalytic set:

A closed set of chemical reactions that is self‐sustaining.

Autonomous:

The characteristic of being capable of making independent decisions over a range of situations.

Avida:

An advanced artificial life computer program developed by Adami [1] and others that models populations of artificial organisms and the essential features of life such as interaction and replication.

Biologically‐inspired computational algorithm:

 Any kind of algorithm that is based on biological metaphors or analogies.

Cellular automata (CA):

A mathematical construct and technique that models a system in discrete time and discrete space in which the state of a cell depends on transition rules and the states of neighboring cells.

Coevolution:

A process by which many entities adapt and evolve their behaviors as a result of mutually effective interactions.

Complex system:

A system comprised of a large number of strongly interacting components (agents).

Complex adaptive system (CAS):

A system comprised of a large number of strongly interacting components (agents) that adapt at the individual (agent) level or collectively at the population level.

Decentralized control:

A feature of a system in which the control mechanisms are distributed over multiple parts of the system.

Digital organism:

An entity that is represented by its essential information‐theoretic elements (genomes) and implemented as a computational algorithm or model.

Downward causation:

The process by which a higher‐order emergent structure takes on its own emergent behaviors and these behaviors exert influence on the constituent agents of the emergent structure.

Dynamic network analysis:

Network modeling and analysis in which the structure of the network, i. e., nodes (agents) and links (agent interactions) is endogenous to the model.

Echo:

An artificial life computer program developed by Holland [36] that models populations of complex adaptive systems and the essential features of adaptation in nature.

Emergence:

The process by which order is produced in nature.

Entropy:

A measure of order, related to the information needed to specify a system and its state.

Evolution (artificial):

The process by which a set of instructions is transmitted and changes over successive generations.

Evolutionary game:

A repeated game in which agents adapt their strategies in recognition of the fact that they will face their opponents in the future.

Evolution strategies:

A heuristic optimization technique motivated by the genetic operation of selection and mutation.

Evolutionary algorithm:

Any algorithm motivated by the genetic operations including selection, mutation, crossover, etc.

Evolutionary computing:

A field of computing based on the use of evolutionary algorithms.

Finite state machine:

A mathematical model consisting of entities with a finite (usually small) number of possible discrete states.

Game of life, life:

A cellular automaton developed by Conway [8] that illustrates a maximally complex system based on simple rules.

Generative social science:

Social science investigation with the goal of understanding how social processes emerge out of social interaction.

Genetic algorithm (GA):

A specific kind of evolutionary algorithm motivated by the genetic operations of selection, mutation, and crossover.

Genetic programming (GP):

A specific kind of evolutionary algorithm that manipulates symbols according to prescribed rules, motivated by the genetic operations of selection, mutation, and crossover.

Genotype:

A set of instructions for a developmental process that creates a complex structure, as in a genotype for transmitting genetic information and seeding a developmental process leading to a phenotype.

Hypercycle:

A closed set of functional relations that is self‐sustaining, as in an autocatalytic chemical reaction network.

Individual‐based model:

An approach originating in ecology to model populations of agents that emphasizes the need to represent diversity among individuals.

Langton's ant:

An example of a very simple computational program that computes patterns of arbitrary complexity after an initial series of simple structures.

Langton's loop:

An example of a very simple computational program that computes replicas of its structures according to simple rules applied locally as in cellular automata.

Learning classifier system (LCS):

A specific algorithmic framework for implementing an adaptive system by varying the weights applied to behavioral rules specified for individual agents.

Lindenmeyer system (L‐system):

 A formal grammar, which is a set of rules for rewriting strings of symbols.

Machine learning:

A field of inquiry consisting of algorithms for recognizing patterns in data (e. g., data mining) through various computerized learning techniques.

Mind–body problem:

A field of inquiry that addresses how human consciousness arises out of material processes, and whether consciousness is the result of a logical‐deductive or algorithmic process.

Meme:

A term coined by Dawkins [17] to refer to the minimal encoding of cultural information, similar to the genome's role in transmitting genetic information.

Particle swarm optimization:

An optimization technique similar to ant colony optimization, based on independent particles (agents) that search a landscape to optimize a single objective or goal.

Phenotype:

The result of an instance of a genotype interacting with its environment through a developmental process.

Reaction‐diffusion system:

A system that includes mechanisms for both attraction and transformation (e. g., of agents) as well as repulsion and diffusion.

Recursively generated object:

An object that is generated by the repeated application of simple rules.

Self‐organization:

A process by which structure and organization arise from within the endogenous instructions and processes inherent in an entity.

Self‐replication:

The process by which an agent (e. g., organism, machine, etc.) creates a copy of itself that contains instructions for both the agent's operation and its replication.

Social agent‐based modeling:

Agent‐based modeling applied to social systems, generally applied to people and human populations, but also animals.

Social network analysis (SNA):

A collection of techniques and approaches for analyzing networks of social relationships.

Stigmergy:

The practice of agents using the environment as a tool for communication with other agents to supplement direct agent‐to‐agent communication.

Swarm:

An early agent‐based modeling toolkit designed to model artificial life applications.

Swarm intelligence:

Collective intelligence based on the actions of a set of interacting agents behaving according to a set of prescribed simple rules.

Symbolic processing:

A computational technique that consists of processing symbols rather than strictly numerical data.

Sugarscape:

An abstract agent‐based model of artificial societies developed by Epstein and Axtell [26] to investigate the emergence of social processes.

Tierra:

An early artificial life computer program developed by Ray [59] that models populations of artificial organisms and the essential features of life such as interaction and replication.

Universal Turing machine (UTM):

An abstract representation of the capabilities of any computable system.

Update rule:

A rule or transformation directive for changing or updating the state of an entity or agent, as for example updating the state of an agent in an agent‐based model or updating the state of an L‑system.

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  1. Center for Complex Adaptive Agent Systems Simulation (CAS2), Decision and Information Sciences Division, Argonne National Laboratory, Argonne, USA

    Charles M. Macal

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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Macal, C.M. (2009). Agent Based Modeling and Artificial Life. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_7

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