What Is Agent-Based Approach?

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Abstract. The agent-based approach emphasizes the importance of learning through organism-environment interaction. This approach is part of a recent trend in computational models of learning and development toward studying autonomous organisms that are embedded in virtual or real environments.

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Definition. An agent-based model is a computational model of various processes such as social, economic, and physical. Agents are autonomous, follow specific rules of behavior, and interact. Agents in such models may have zero-intelligence, so that behave randomly, or be purposeful (or goal oriented) and learn.

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Individual-based models, also called agent-based models, are a population and community modeling approach that allows for a high degree of complexity of individuals and of interactions among individuals.

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Whether you're modelling or modeling, you're doing the same thing. The only difference is in the spelling—the one with the single L is preferred in the United States, while the one with two Ls is preferred everywhere else.

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An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) in order to understand the behavior of a system and what governs its outcomes.

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In agent-based modeling (ABM), a system is modeled as a collection of autonomous decision-making entities called agents. Each agent individually assesses its situation and makes decisions on the basis of a set of rules.

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Agent-based modeling allows us to model system behaviors based on the actions and interactions of individuals in the system. Although most ABM research focuses on local rules and behaviors, it is possible that we integrate global influences in the models. ABM represents a novel approach to model intelligent systems.

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Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns.

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Agent-based models are computer simulations used to study the interactions between people, things, places, and time. They are stochastic models built from the bottom up meaning individual agents (often people in epidemiology) are assigned certain attributes.

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At the simplest level, an agent-based model consists of a system of agents and the relationships between them. Even a simple agent-based model can exhibit complex behavior patterns (3) and provide valuable information about the dynamics of the real-world system that it emulates.

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In some cases models are developed in order to study only a certain aspect of the given system; thus the model may have more variables pertaining to certain processes than others. ... In some cases, the rules that govern the updating of the agents' state variables are deterministic, and in other cases they are stochastic.