Agent-based model

An agent-based model ABM is a computational model for simulating the actions together with interactions of autonomous agents both individual or collective entities such(a) as organizations or groups in array to understand the behavior of a system & what governs its outcomes. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo methods are used to understand the stochasticity of these models. especially within ecology, ABMs are also called individual-based models IBMs. A review of recent literature on individual-based models, agent-based models, and multiagent systems shows that ABMs are used in many scientific domains including biology, ecology and social science. Agent-based modeling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the purpose of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or technology science problems.

Agent-based models are a shape of microscale model that simulate the simultaneous operations and interactions of corporation agents in an try to re-create and predict the configuration of complex phenomena. The process is one of emergence, which some express as "the whole is greater than the a object that is caused or portrayed by something else of its parts". In other words, higher-level system properties emerge from the interactions of lower-level subsystems. Or, macro-scale state reorganize emerge from micro-scale agent behaviors. Or, simple behaviors meaning rules followed by agents generate complex behaviors meaning state reorganize at the whole system level.

Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such(a) as reproduction, economic benefit, or social status, using heuristics or simple decision-making rules. ABM agents may experience "learning", adaptation, and reproduction.

Most agent-based models are composed of: 1 many agents spoke at various scales typically forwarded 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 simulations, either as custom software, or via ABM toolkits, and this software can be then used to test how changes in individual behaviors will impact the system's emerging overall behavior.

History

The impression of agent-based modeling was developed as a relatively simple concept in the late 1940s. Since it requires computation-intensive procedures, it did non become widespread until the 1990s.

The history of the agent-based value example can be traced back to the Von Neumann machine, a theoretical machine capable of reproduction. The device von Neumann introduced would undertake precisely detailed instructions to fashion a copy of itself. The concept was then built upon by von Neumann's friend Stanislaw Ulam, also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The belief intrigued von Neumann, who drew it up—creating the number one of the devices later termed cellular automata. Another carry on was reported by the mathematician Game of Life. Unlike von Neumann's machine, Conway's Game of Life operated by simple rules in a virtual world in the hit of a 2-dimensional checkerboard.

The Simula programming language, developed in the mid 1960s and widely implemented by the early 1970s, was the number one framework for automating step-by-step agent simulations.

One of the earliest agent-based models in concept was Thomas Schelling's segregation model, which was discussed in his paper "Dynamic Models of Segregation" in 1971. 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 1980s, Prisoner's Dilemma strategies and had them interact in an agent-based generation to determining a winner. Axelrod would go on to introducing many other agent-based models in the field of political science that inspect phenomena from ethnocentrism to the dissemination of culture. By the unhurried 1980s, Craig Reynolds' create on flocking models contributed to the coding of some of the first biological agent-based models that contained social characteristics. He tried to framework the reality of lively biological agents, required as artificial life, a term coined by Christopher Langton.

The first usage of the word "agent" and a definition as this is the currently used today is hard to track down. One candidate appears to be John Holland and John H. Miller's 1991 paper "Artificial Adaptive Agents in Economic Theory", based on an earlier conference presentation of theirs.

At the same time, during the 1980s, social scientists, mathematicians, operations researchers, and a scattering of people from other disciplines developed Computational and Mathematical organization Theory CMOT. This field grew as a special interest group of The Institute of management Sciences TIMS and its sister society, the Operations Research Society of America ORSA.

The 1990s were especially notable for the expansion of ABM within the social sciences, one notable effort was the large-scale ABM, Sugarscape, developed by

Through the mid-1990s, the social sciences thread of ABM began to focus on such issues as designing powerful teams, understanding the communication so-called for organizational effectiveness, and the behavior of social networks. CMOT—later renamed Computational Analysis of Social and Organizational Systems CASOS—incorporated more and more agent-based modeling. Samuelson 2000 is a good brief overview of the early history, and Samuelson 2005 and Samuelson and Macal 2006 trace the more recent developments.

In the late 1990s, the merger of TIMS and ORSA to form ] TheWorld Congress was held in the northern Virginia suburbs of Washington, D.C., in July 2008, with George Mason University taking the lead role in local arrangements.

More recently, cognitive social simulation. Bill McKelvey, Suzanne Lohmann, Dario Nardi, Dwight Read and others at ]