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An agent-based model ABM is a class of computational models for simulating the actions and interactions of autonomous agents both individual or collective entities such as organizations or groups with a view to assessing their effects on the system as a whole. It combines elements of game theorycomplex systems read more, emergencecomputational sociologymulti-agent systemsand evolutionary programming.

Monte Carlo methods are used to introduce randomness. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used on non-computing related scientific domains including biologyecology and social science.

Agent-based models are a kind of microscale model [3] that simulate the simultaneous operations and interactions of multiple agents in an attempt to re-create and predict the appearance of complex phenomena. The process is one of emergencewhich some express as "the whole is greater than the sum of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems.

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Or, macro-scale state changes emerge from micro-scale agent behaviors. Or, simple behaviors meaning rules followed by agents generate complex behaviors meaning state changes at the whole system level. Individual agents are typically characterized as boundedly rationalpresumed to be acting in what they perceive as their Stuy interests, such as reproduction, economic benefit, or social status, [4] using heuristics or simple decision-making rules.

ABM agents may experience "learning", adaptation, and reproduction. Most agent-based models are composed of: 1 numerous agents specified at various scales typically referred to as agent-granularity ; 2 decision-making heuristics; 3 learning rules or adaptive processes; 4 an interaction topology ; and 5 an environment.

ABMs are typically implemented as computer simulationseither as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will affect the system's emerging overall behavior. The idea of agent-based modeling was https://amazonia.fiocruz.br/scdp/blog/culture-and-selfaeesteem/what-is-the-theme-of-darkness-and.php as a relatively simple concept in the late s.

Since it requires computation-intensive procedures, it did not become widespread until the s.

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The history of the agent-based model can be traced back to the Von Neumann machinea theoretical machine capable of reproduction. The device von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The click was then built upon by von Neumann's friend Stanislaw Ulamalso a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices Investjent termed cellular automata. Another advance was introduced by the mathematician John Conway. He constructed the well-known Game of Life.

Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the form of a 2-dimensional checkerboard.

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The Simula programming language, developed in the mid s and widely implemented by the early s, Case Study Of Ibm s Wise Investment the first framework for automating step-by-step agent simulations. One of the earliest agent-based models in concept was Thomas Schelling 's segregation model, [6] which was discussed in his paper "Dynamic Models of Segregation" in Though Schelling originally used coins and graph paper rather than computers, his models embodied the basic concept of agent-based models as autonomous agents interacting in a shared environment with an observed aggregate, emergent outcome. In the early s, Robert Axelrod hosted a tournament of Prisoner's Dilemma strategies and had them interact in an agent-based manner to determine a winner.

Axelrod would go on to develop many other agent-based models in the field of political science that examine phenomena from ethnocentrism to the dissemination of culture. He tried to model the reality of lively biological agents, known as artificial lifea term coined by Christopher Langton. The first use of the word "agent" and a definition as it is currently used today is hard to track down. One candidate appears to be John Holland and John H.

Miller's paper "Artificial Adaptive Agents in Economic Theory", [8] based on an earlier conference presentation of theirs. At the same time, during the s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical Organization Theory CMOT. Epstein and Robert Axtell to simulate and explore the role of social phenomena such as check this out migrations, pollution, sexual reproduction, combat, and transmission of disease and even culture.

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Through the mids, the social sciences thread of ABM began to focus on such issues as designing effective teams, understanding the communication required for organizational effectiveness, and the behavior of social networks. Samuelson is a good brief overview of the early history, [12] and Samuelson and Samuelson and Macal trace the more recent developments. As ofthese three organizations collaborate internationally. More recently, Ron Sun developed methods for basing agent-based simulation on models of human cognition, known as cognitive social simulation.]