An Agent-Based Multi-Scale Wind Generation Model Enrique Kremers, Norbert Lewald (European Institute for Energy Research); Oscar Barambones, Jose Maria Gonzalez de Durana (Universidad del Pais Vasco). Kremers, E., Lewald, N. IASTED EuroPES 2009, Palma de Mallorca, Spain, September 7 – 9. (2009).

This paper presents an agent-based model for simulating wind power systems on multiple time scales. The aim is to generate a flexible model that allows us to simulate the output of a wind farm. The model is developed using multiparadigm modelling, combining different approaches such as agent-based modelling, discrete events and dynamic systems.
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Business is a field for experiments. But it’s best to run them on simulation models Timofey Popkov.

Modeling allows you to decrease implementation costs and risks while still in the planning stage. It can also be the best way to optimize existing processes.
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IRS POST-FILING PROCESSES SIMULATION MODELING: A COMPARISON OF DES WITH ECONOMETRIC MICROSIMULATION IN TAX ADMINISTRATION Arnold Greenland, Erica Layne Morrison, David Connors, John L. Guyton, Michael Sebastiani, 2007 Winter Simulation Conference (WSC’07), Washington D.C., December 9-12, 2007

IRS Office of Research Headquarters measures and models taxpayer burden, defined as expenditures of time and money by taxpayers to comply with the federal tax system. In this research activity, IRS created two microsimulation models using econometric techniques to enable the Service to produce annual estimates of taxpayer compliance burden for individual and small business populations. Additionally, a Discrete Event Simulation (DES) model was developed to represent taxpayer activities and IRS administration in postfiling processes.
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From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools Andrei Borshchev and Alexei Filippov. The 22nd International Conference of the System Dynamics Society, July 25 - 29, 2004, Oxford, England

This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled.
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A Spatio Temporal Simulation Model for Evaluating Delinquency and Crime Policies Sergio E. Quijada, Juan F. Arcas, Cristian Renner, and Luis Rabelo. 2005 Winter Simulation Conference (WSC’05), December 4-7, Orlando, FL, USA

System Dynamics, has been useful for a variety of disciplines; however, it has limitations in showing a geographical representation of the models under study. This paper proposes a methodology based on layered vectors which allows the use a city’s census information to feed a Geographic Information System (GIS). The GIS objects implemented into System Dynamics and located at coordinates X.Y.Z become the entry parameters for the simulation.
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