2008/12 | LEM Working Paper Series | |
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Laboratory for Simulation Develpment - LSD |
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Marco Valente |
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Keywords | ||
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Simulations models, programming languages
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JEL Classifications | ||
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Abstract | ||
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LSD is one of many programming languages designed to develop
agent-based models. LSD implements time-driven models expressed in
formats equivalent to discrete systems of equations, where each
equation computes the value of a generic instance of a variable at a
generic time step. LSD models are therefore extremely parsimonious in
terms of details that users must provide to the system. When a model
has been described, the system automatically generates a working
program implementing the model, endowed with a complete set of
interfaces for any possible operation on the model. The major feature
of is that users can rely on an automatic scheduling system and on
automatic retrieval of data required for the equations. Such features
are particularly attractive in complex, multi-herarchical models. They
permit even non- expert programmers to develop even relatively complex
models with minimal training. The systems interfaces guarantee
the complete control of the model at building, at run-time and at
post-simulation analysis, facilitating debugging, revisions and
detailed analysis of model results, which are useful properties
especially when developing large models for ambitious projects. The
design of LSD is based on an "open architecture", so that
LSD can be used to implement any type of model, including even-driven
models and models based on customized data structures. The intrinsic
modularity of LSD models make them easily scalable facilitating the
development of highly complex models by demanding users. The
underlining layer of C++, accessible by the users, allows the
inclusions of external libraries or of complex data structures,
besides an extreme speed and dimensions of the model. This work
reports on the major features of the design of LSD outlining its most
prominent advantages for users of simulation models in research,
particularly for agent- based simulations.
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