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Agent-based social simulation (or ABSS) [1] [2] consists of social simulations that are based on agent-based modeling, and implemented using artificial agent technologies. Agent-based social simulation is a scientific discipline concerned with simulation of social phenomena , using computer-based multiagent models.
Computational sociology is a branch of sociology that uses computationally intensive methods to analyze and model social phenomena. Using computer simulations, artificial intelligence, complex statistical methods, and analytic approaches like social network analysis, computational sociology develops and tests theories of complex social processes through bottom-up modeling of social interactions.
Social simulation is a research field that applies computational methods to study issues in the social sciences.The issues explored include problems in computational law, psychology, [1] organizational behavior, [2] sociology, political science, economics, anthropology, geography, engineering, [2] archaeology and linguistics (Takahashi, Sallach & Rouchier 2007).
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 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 ...
Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making.
Live, Virtual, & Constructive (LVC) Simulation is a broadly used taxonomy for classifying Modeling and Simulation (M&S). However, categorizing a simulation as a live, virtual, or constructive environment is problematic since there is no clear division among these categories. The degree of human participation in a simulation is infinitely ...
A simulation is a way to implement the model, often employed when the model is too complex for the analytical solution. A steady-state simulation provides information about the system at a specific instant in time (usually at equilibrium, if such a state exists). A dynamic simulation provides information over time.
Simulation-based optimization (also known as simply simulation optimization) integrates optimization techniques into simulation modeling and analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective ...