From SD and DE to Practical Agent Based Modeling Reasons Techniques Tools 04

From SD and DE to Practical Agent Based Modeling Reasons Techniques Tools 04

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From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools Andrei Borshchev & Alexei Filippov XJ Technologies www.xjtek.com and St.Petersburg Technical University 21 Polytechnicheskaya street St.Petersburg 194021 Russia Tel: +7 812 2471674 Fax: +7 812 2471639 [email protected] [email protected] Abstract 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. Commonly understood examples are used throughout the paper; all models are specified in the visual language supported by AnyLogic TM tool. We view and present Agent Based modeling not as a substitution to older modeling paradigms but as a useful add-on that can be efficiently combined with System Dynamics and Discrete Event modeling. Several multi-paradigm model architectures are suggested. Keywords: multi-paradigm modeling, agent based modeling, system dynamics, AnyLogic 1. Simulation Modeling: Abstraction Levels, Major Paradigms To make sure we all agree on terms we use please take a look at Figure 1. Modeling is a way of solving problems that occur in the real world. It is applied when prototyping or experimenting with the real system is expensive or impossible. Modeling allows you to optimize systems prior to implementation. Modeling includes the process of mapping the problem from the real world to its model in the world of models, – the process of abstraction , – model analysis and optimization, and mapping the solution back to the real system. We can distinguish between analytical and simulation models. In analytical , or static, model the result functionally depends on the input (a number of parameters); it is possible to implement such model in a spreadsheet. However, analytical solution does not always exist, or may be very hard to find. Then simulation, or dynamic, modeling may be applied. A simulation model may be considered as a set of rules (e.g. equations, flowcharts, state machines, cellular automata) that define how the system being modeled will change in the future, given its present state. Simulation is the process of model “execution” that takes the model through (discrete or continuous) state changes over time. In general, for complex problems where time dynamics is important, simulation modeling is a better answer.
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