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The New Coevolution of Information Science and Social Science:
The New Coevolution of Information Science and Social Science:

... interact. Typically, each object represents one individual. These objects have internal data fields that store the specific characteristics of the individuals, things like preferences, endowments, goals and aspirations. The objects also have methods that both modify their internal data as well as de ...
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abductive reasoning as the logic of agent
abductive reasoning as the logic of agent

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... utilize the various techniques in knowledge-based systems. Then, the cognitive processes (perception, memory, language and thought) are investigated. Finally, the main research approaches are presented that may lead to valid intelligent techniques suitable for various applications. Upon completion o ...
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...  introduce the main issues surrounding the design of intelligent agents;  introduce the main issues surrounding the design of a cognitive system; and  introduce a contemporary platform for implementing agents and cognitive systems. ...
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... are capable of modeling dynamical systems. They can be built based on expert knowledge but also learned from data. Acyclic directed graphs are a convenient tool for modeling causality and theoretical links have been proposed between probability and causation, leading to powerful methods for discover ...
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CHAP4
CHAP4

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Basic Marketing, 16e

... AGENT-BASED MODELING ...
< 1 ... 29 30 31 32 33 34 >

Agent-based model

An agent-based model (ABM) is one of 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 theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. Particularly within ecology, ABMs are also called individual-based models (IBMs), and individuals within IBMs may be simpler than fully autonomous agents within ABMs. 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 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 goal 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 engineering problems.Agent-based models are a kind of microscale model 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 emergence from the lower (micro) level of systems to a higher (macro) level. As such, a key notion is that simple behavioral rules generate complex behavior. This principle, known as K.I.S.S. (""Keep it simple, stupid"") is extensively adopted in the modeling community. Another central tenet is that the whole is greater than the sum of the parts. Individual agents are typically characterized as boundedly rational, presumed to be acting in what they perceive as their own interests, such 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) 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) a non-agent 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 affect the system's emerging overall behavior.
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