Author:
Zschaler Steffen,Polack Fiona A. C.
Abstract
AbstractSimulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.
Publisher
Springer Science and Business Media LLC
Subject
Modeling and Simulation,Software
Reference53 articles.
1. Alden, K.: Simulation and statistical techniques to explore lymphoid tissue organogenesis. PhD thesis, University of York, UK, http://etheses.whiterose.ac.uk/id/eprint/3220 (2016)
2. Alden, K., Timmis, J., Andrews, P., et al.: Pairing experimentation and computational modeling to understand the role of tissue inducer cells in the development of lymphoid organs. Front. Immunol. 3, 172 (2012). https://doi.org/10.3389/fimmu.2012.00172
3. Alden, K., Read, M., Timmis, J., et al.: Spartan: A comprehensive tool for understanding uncertainty in simulations of biological systems. PLoS Comput. Biol. 9(2), 1–9 (2013). https://doi.org/10.1371/journal.pcbi.1002916
4. Alden, K., Andrews, P.S., Polack, F.A.C., et al.: Using argument notation to engineer biological simulations with increased confidence. J. R. Soc. Interface (2015). https://doi.org/10.1098/rsif.2014.1059
5. Alden, K., Timmis, J., Andrews, P.S., et al.: Extending and applying Spartan to perform temporal sensitivity analyses for predicting changes in influential biological pathways in computational models. IEEE Trans. Comput. Biol. 14(2), 422–431 (2016)
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献