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Laboratory for Simulation Develpment - LSD

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  • Marco Valente

Abstract

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.

Suggested Citation

  • Marco Valente, 2008. "Laboratory for Simulation Develpment - LSD," LEM Papers Series 2008/12, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  • Handle: RePEc:ssa:lemwps:2008/12
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    Citations

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    Cited by:

    1. André Lorentz & Tommaso Ciarli & Maria Savona & Marco Valente, 2016. "The effect of demand-driven structural transformations on growth and technological change," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 219-246, March.
    2. Nadia Fiorino & Emma Galli & Ilde Rizzo & Marco Valente, 2023. "Public procurement and reputation. An agent‐based model," Metroeconomica, Wiley Blackwell, vol. 74(4), pages 806-832, November.
    3. Johannes Dahlke & Kristina Bogner & Matthias Mueller & Thomas Berger & Andreas Pyka & Bernd Ebersberger, 2020. "Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)," Papers 2003.11985, arXiv.org.
    4. Marco Valente, 2009. "Markets fo Heterogeneous Products: a Boundedly Rational Consumer Model," LEM Papers Series 2009/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Vandin, Andrea & Giachini, Daniele & Lamperti, Francesco & Chiaromonte, Francesca, 2022. "Automated and distributed statistical analysis of economic agent-based models," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    6. Faber, Albert & Valente, Marco & Janssen, Peter, 2010. "Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion," Energy Policy, Elsevier, vol. 38(6), pages 2763-2775, June.
    7. Ciarli, Tommaso & Valente, Marco, 2016. "The complex interactions between economic growth and market concentration in a model of structural change," Structural Change and Economic Dynamics, Elsevier, vol. 38(C), pages 38-54.
    8. Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2021. "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," Papers 2102.05405, arXiv.org, revised Nov 2023.
    9. Emanuele Russo, 2017. "Harrodian instability in decentralized economies: an agent-based approach," LEM Papers Series 2017/17, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Andrea Vandin & Daniele Giachini & Francesco Lamperti & Francesca Chiaromonte, 2020. "Automated and Distributed Statistical Analysis of Economic Agent-Based Models," LEM Papers Series 2020/31, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    11. Francesco Pasimeni, 2017. "Adoption and Diffusion of Micro-Grids in Italy. An Analysis of Regional Factors Using Agent-Based Modelling," SPRU Working Paper Series 2017-09, SPRU - Science Policy Research Unit, University of Sussex Business School.

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    Keywords

    Simulations models; programming languages;

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