Trustworthy agent-based simulation: the case for domain-specific modelling languages

Simulation 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 vi...

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Veröffentlicht in:Software and systems modeling Jg. 22; H. 2; S. 455 - 470
Hauptverfasser: Zschaler, Steffen, Polack, Fiona A. C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Schlagworte:
Agent-based models
Behavior
Compilers
Complex systems
Computer Science
Design
Engineering
Experimentation
Experiments
Expert Voice
Hypotheses
Information Systems Applications (incl.Internet)
Interpreters
IT in Business
Laboratories
Languages
Maintainability
Modelling
Programming Languages
Programming Techniques
Sensitivity analysis
Simulation
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Subject specialists
Trust
ISSN:1619-1366, 1619-1374, 1619-1374
Online-Zugang:Volltext
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Zusammenfassung:Simulation 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.
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Communicated by Bernhard Rumpe.
ISSN:1619-1366
1619-1374
1619-1374
DOI:10.1007/s10270-023-01082-9