A Model-Driven Approach for Conducting Simulation Experiments

Wilsdorf, Pia; Heller, Jakob; Budde, Kai; Zimmermann, Július; Warnke, Tom; Haubelt, Christian; Timmermann, Dirk; Rienen, Ursula van; Uhrmacher, Adelinde M.

doi:10.36227/techrxiv.16836868

Cited by 3 publications

(7 citation statements)

References 52 publications

(54 reference statements)

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“…These include, e.g., the Simulation Experiment Description Markup Language for experiments in systems biology (SED-ML) [72], a description language for the network simulator ns-3 (NEDL) [38], or Xperimenter for the design of experiments (DoE) [16]. Furthermore, model-based approaches allow for the generation of factorial experiment design [66] and many other experiment types [74]. These approaches also allow one to conquer a myriad of tools based on a single, tool-agnostic specification format if software bindings are implemented [17,74].…”

Section: Explicit Simulation Experimentsmentioning

confidence: 99%

“…Furthermore, model-based approaches allow for the generation of factorial experiment design [66] and many other experiment types [74]. These approaches also allow one to conquer a myriad of tools based on a single, tool-agnostic specification format if software bindings are implemented [17,74].…”

Section: Explicit Simulation Experimentsmentioning

confidence: 99%

“…Adapting and generating simulation experiments based on metamodels. The pipeline and the metamodels were defined in [74].…”

Section: Adaptation Of the Experiments Specificationsmentioning

confidence: 99%

“…Only if simulation experiments are explicitly specified, they can be automatically interpreted. Their interpretation is facilitated by schemas [74] for the different types of experiments, possibly complemented by ontologies about their various roles [58] and designs [60].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Wilsdorf

Wolpers

Hilton

et al. 2023

ACM Trans. Model. Comput. Simul.

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Simulation experiments are typically conducted repeatedly during the model development process, for example, to re-validate if a behavioral property still holds after several model changes. Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner. They rely on explicit experiment specifications and, so far, on user interaction for initiating the reuse. Thereby, they are constrained to support the reuse of simulation experiments in a specific setting. Our approach now goes one step further by automatically identifying and adapting the experiments to be reused for a variety of scenarios. To achieve this, we exploit provenance graphs of simulation studies, which provide valuable information about the previous modeling and experimenting activities, and contain meta-information about the different entities that were used or produced during the simulation study. We define provenance patterns and associate them with a semantics, which allows us to interpret the different activities, and construct transformation rules for provenance graphs. Our approach is implemented in a Reuse and Adapt framework for Simulation Experiments (RASE) which can interface with various modeling and simulation tools. In the case studies, we demonstrate the utility of our framework for a) the repeated sensitivity analysis of an agent-based model of migration routes, and b) the cross-validation of two models of a cell signaling pathway.

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Section: Explicit Simulation Experimentsmentioning

confidence: 99%

Section: Explicit Simulation Experimentsmentioning

confidence: 99%

“…Adapting and generating simulation experiments based on metamodels. The pipeline and the metamodels were defined in [74].…”

Section: Adaptation Of the Experiments Specificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Wilsdorf

Wolpers

Hilton

et al. 2023

ACM Trans. Model. Comput. Simul.

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show abstract

“…Thus, each entity of type simulation model or simulation experiment would refer to an openly accessible code repository. The accessibility and transparency of code can be enhanced by exploiting developments in specifying executable simulation experiments, including domain-specific languages [43], [44] or model-based experiment designs [45].…”

Section: Modeling and Model Analysismentioning

confidence: 99%

Simulation Studies of Social Systems – Telling the Story Based on Provenance

Reinhardt¹,

Prike²,

Hinsch³

et al. 2022

Preprint

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<p>Social simulation studies are complex, because they typically combine various sources of data and hypotheses, that are integrated by intertwined processes, of model building, simulation experiment execution, and analysis. Various documentation approaches exist that support transparency and traceability of social simulation studies. The exploitation of provenance standards allows for making the information about what sources and activities contributed to the generation of an entity, e.g., simulation model, queryable and computationally accessible. Therefore, provenance patterns have been defined to capture central activities and entities. Activities include model building, calibration, analysis, and validation. Entities are simulation model, simulation experiment (its specification), and research question. Here we refine and extend this approach to address specific challenges of social agent-based simulation studies, i.e., activities such as collecting and analyzing primary data about human decisions, or collecting and assessing the quality of secondary data. This allows us to tell the whole story of these simulation studies in a comprehensive manner. We illustrate the potential of the approach by applying it to central activities and results of the Bayesian Agent-Based Population Studies project and implementing it in a web-based provenance tool.</p>

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Simulation Studies of Social Systems – Telling the Story Based on Provenance Patterns

Reinhardt¹,

Prike²,

Hinsch³

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>Social simulation studies are complex. They typically combine various sources of data and hypotheses, that are integrated by intertwined processes of model building, simulation experiment execution, and analysis. Various documentation approaches exist to support the transparency and traceability of complex social simulation studies. In particular, provenance patterns can be used to capture central activities and entities of a simulation study. Entities can include, simulation models, experiments, or research questions, and activities – model building, calibration, validation, and analysis. The exploitation of provenance standards enables information on sources and activities, which contribute to the generation of an entity, to be queryable and computationally accessible. In this study, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analyzing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of the Bayesian Agent-Based Population Studies project and by presenting its implementation in a web-based tool.</p>

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A Model-Driven Approach for Conducting Simulation Experiments

Cited by 3 publications

References 52 publications

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns

Simulation Studies of Social Systems – Telling the Story Based on Provenance

Simulation Studies of Social Systems – Telling the Story Based on Provenance Patterns

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