complexity of building a simulation model. Still too little support exists to
ensure semantically valid composition or reuse, the same is to regarding
reasoning about similarities and differences. Other modeling and simulation
study artifacts, such as simulation experiments, requirements, or assumptions,
hold valuable information that future work needs to build on. At the same
time, those artifacts themselves will benefit from methods and efforts aimed
at facilitating composition and reuse, such as introducing standards and
formalization.

Increasing the fraction of simulation studies that can be executed
_automatically_ boosts the efficiency of conducting simulation studies and
holds the promise of more systematically conducted simulation studies than at
present. Automation relies on making knowledge involved in a modeling and
simulation study explicitly accessible. This refers to knowledge about the
application domain of the simulation study as well as modeling and simulation
methods and their application. Therefore, in addition to an unambiguous
representation of the various artifacts, suitable annotations and ontologies
will play a central role, as does the exploitation of machine learning
methods.

_Communication_ of simulation results relies on understanding the different
users, be these domain experts or decision makers. Their view on the system of
interest, expectations, and questions needs to be considered in communicating
and explaining simulation results to users. This applies to automatically
generated abstractions and explanations and visual and interactive methods
that allow users to gain insights into the system and possible answers to
their questions.

###  7.2 The Road Ahead

All goals rely on a clear separation of concerns and the unambiguous
representation of central artifacts of the modeling and simulation study.
Whereas in the past attention was focused on simulation models and their
formal representation, more recent efforts started to look more closely at how
to specify succinctly and unambiguously simulation experiments and behavioral
requirements. For achieving any of the four goals, these efforts need to be
intensified and expanded to other artifacts, such as assumptions and research
questions. Also annotations and ontologies play a role, therefore, suitable
means need to be developed for representing those unambiguously.

The methodological developments induced by pursuing the suggested roadmap must
be evaluated suitably. Evaluation might refer to efficiency (concerning time
and other resources), effectiveness (in terms of the intended purpose), and
accuracy (without technical or methodological errors). This will propose new
challenges. For example, maintaining or increasing understanding and trust in
modeling and simulation studies is crucial for the different goals, but how
can the level of understanding or trust be reliably assessed, and methods and
protocols designed to evaluate a change in those? This will open up an entire
area of new modeling and simulation research which can build on metrics,
evaluation methods, and best practices in other areas of computer science,
such as software engineering or human-computer interaction.

In fact, the proposed roadmap will _require_ close interaction with other
areas of computer science, e.g., with visual analytics, which plays a crucial
role in communication, but also, more generally, in keeping the modeler or
user within the loop, particularly, if substantial parts of the modeling and
simulation life cycle become automated. Similarly, adopting state-of-the-art
methods developed in the area of software engineering is key for making
progress in modeling and simulation as envisioned in our roadmap. This
includes methods that support the design of DSLs and formats for the diverse
artifacts of a simulation study as well as for reverse engineering to generate
abstractions to cater to the needs of different users. Reasoning about
variations or components of artifacts may benefit from developments such as
feature languages and other annotations. The latter leads us to the field of
artificial intelligence, as knowledge-based annotations and suitable inference
strategies will be crucial for any meaningful composition and reuse beyond the
syntactical level and for realizing a more significant degree of automation.
Therefore, knowledge-based deductive approaches need to be combined with
machine-learning methods to automate various tasks within the modeling and
simulation life cycle.

As cooperation with other computer science disciplines is needed to move
forward, so are additional efforts of the modeling and simulation communities.
For example, the maintenance of tools and repositories requires further
attention. Efforts need to be directed towards defining and utilizing
languages with clear semantics, introducing standards and best practices,
developing ontologies of the application domain and modeling and simulation
methods, and making those accessible. These research efforts may not be
restricted to simulation models but need to be expanded to other modeling and
simulation artifacts, such as research questions, assumptions, requirements,
simulation experiments, and diverse processes and activities, to treat those
as first-class citizens of the simulation study.

## Acknowledgements

We would like to thank Justin Noah Kreikemeyer for his support in developing
Figures 1 and 7. In particular, we would like to thank Schloss Dagstuhl
(www.dagstuhl.de). The paper summarizes and extends the discussions of one of
the three working groups of the Dagstuhl Seminar 22401: Computer Science
Methods for Effective and Sustainable Simulation Studies (03. Oct.–07. Oct,
2022).

### Funding

A.M.U. and P.W. are funded by the German Research Foundation (DFG) grant
320435134 “GrEASE - Towards Generating and Executing Automatically Simulation
Experiments”.

F.L. is funded by the Wallenberg AI, Autonomous Systems and Software Program –
Humanities and Society (WASP-HS) funded by the Marianne and Marcus Wallenberg
Foundation and the Marcus and Amalia Wallenberg Foundation.

## References

  * Winsberg [2010] Eric Winsberg.  _Science in the Age of Computer Simulation_.  University of Chicago Press, 2010.
  * Balci [2012] Osman Balci.  A life cycle for modeling and simulation.  _Simulation_ , 88(7):870–883, 2012.
  * Law [2019] Averill M Law.  How to build valid and credible simulation models.  In _2019 Winter Simulation Conference (WSC)_ , pages 1402–1414. IEEE, 2019.
  * Robinson [2014] Stewart Robinson.  _Simulation: The Practice of Model Development and Use_.  Bloomsbury Publishing, 2014.
  * Shannon [1998] Robert E Shannon.  Introduction to the art and science of simulation.  In _1998 Winter Simulation Conference_ , volume 1, pages 7–14. IEEE, 1998.
  * Lorig et al. [2021] Fabian Lorig, Emil Johansson, and Paul Davidsson.  Agent-based social simulation of the covid-19 pandemic: A systematic review.  _JASSS: Journal of Artificial Societies and Social Simulation_ , 24(3), 2021.
  * Shinde et al. [2020] Gitanjali R Shinde, Asmita B Kalamkar, Parikshit N Mahalle, Nilanjan Dey, Jyotismita Chaki, and Aboul Ella Hassanien.  Forecasting models for coronavirus disease (covid-19): A survey of the state-of-the-art.  _SN Computer Science_ , 1(4):1–15, 2020.
  * Basso et al. [2022] Leonardo J Basso, Marcel Goic, Marcelo Olivares, Denis Sauré, Charles Thraves, Aldo Carranza, Gabriel Y Weintraub, Julio Covarrubias, Cristian Escobedo, Natalia Jara, et al.  Analytics saves lives during the covid crisis in chile.  Technical report, 2022.  Franz Edelman Award.
  * Ferguson et al. [2020] Neil M Ferguson, Daniel Laydon, Gemma Nedjati-Gilani, Natsuko Imai, Kylie Ainslie, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, Zulma Cucunubá, Gina Cuomo-Dannenburg, et al.  Impact of non-pharmaceutical interventions (npis) to reduce covid-19 mortality and healthcare demand.  Technical report, Imperial College COVID-19 Response Team London, 2020\.
  * Garcia-Vicuña et al. [2022] Daniel Garcia-Vicuña, Laida Esparza, and Fermin Mallor.  Hospital preparedness during epidemics using simulation: the case of covid-19.  _Central European Journal of Operations Research_ , 30(1):213–249, 2022.
  * Ivanov [2020] Dmitry Ivanov.  Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (covid-19/sars-cov-2) case.  _Transportation Research Part E: Logistics and Transportation Review_ , 136:101922, 2020.
  * Frazier et al. [2022] Peter I Frazier, J Massey Cashore, Ning Duan, Shane G Henderson, Alyf Janmohamed, Brian Liu, David B Shmoys, Jiayue Wan, and Yujia Zhang.  Modeling for covid-19 college reopening decisions: Cornell, a case study.  _Proceedings of the National Academy of Sciences_ , 119(2):e2112532119, 2022.
  * Cai et al. [2023] Wentong Cai, Christopher Carothers, David M. Nicol, and Adelinde M. Uhrmacher.  Computer Science Methods for Effective and Sustainable Simulation Studies (Dagstuhl Seminar 22401).  _Dagstuhl Reports_ , 12(10):1–60, 2023.  ISSN 2192-5283.  doi:10.4230/DagRep.12.10.1.  URL https://drops.dagstuhl.de/opus/volltexte/2023/17819.
  * Zeigler et al. [2000] Bernard P. Zeigler, Herbert Praehofer, and Tag Gon Kim.  _Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems_.  Academic Press, San Diego, CA, 2000.
  * Balbo [2000] Gianfranco Balbo.  Introduction to stochastic petri nets.  In _School organized by the European Educational Forum_ , pages 84–155. Springer, 2000.
  * Hillston [2005] Jane Hillston.  Process algebras for quantitative analysis.  In _20th Annual IEEE Symposium on Logic in Computer Science (LICS’05)_ , pages 239–248. IEEE, 2005.
  * Booch et al. [1998] Grady Booch, James Rumbaugh, and Ivar Jacobson.  _The Unified Modeling Language User Guide_.  Addison-Wesley, 1998.
  * Rumpe [2017] Bernhard Rumpe.  _Agile Modeling with UML: Code Generation, Testing, Refactoring_.  Springer International, May 2017.
  * Fowler and Scott [1997] Martin Fowler and Kendall Scott.  _UML Distilled: Applying the Standard Object Modeling Language_.  Addison-Wesley Longman Ltd., Essex, UK, UK, 1997.  ISBN 0-201-32563-2.
  * Weilkiens [2008] Tim Weilkiens.  _Systems Engineering with SysML/UML: Modeling, Analysis, Design_.  Elsevier, 2008.
  * Friedenthal et al. [2011] Sanford. Friedenthal, Alan Moore, and Rick Steiner.  _A Practical Guide to SysML: The Systems Modeling Language_.  The MK/OMG Press. Elsevier Science, 2011.  ISBN 9780123852076.  URL http://books.google.de/books?id=4xz6Fx50zwcC.
  * Jansen et al. [2022] Nico Jansen, Jerome Pfeiffer, Bernhard Rumpe, David Schmalzing, and Andreas Wortmann.  The Language of SysML v2 under the Magnifying Glass.  _Journal of Object Technology (JOT)_ , 21:1–15, July 2022\.
  * Blinov et al. [2004] Michael L Blinov, James R Faeder, Byron Goldstein, and William S Hlavacek.  Bionetgen: Software for rule-based modeling of signal transduction based on the interactions of molecular domains.  _Bioinformatics_ , 20(17):3289–3291, 2004.
  * Helms et al. [2017] Tobias Helms, Tom Warnke, Carsten Maus, and Adelinde M Uhrmacher.  Semantics and efficient simulation algorithms of an expressive multilevel modeling language.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 27(2):1–25, 2017.
  * Reinhardt et al. [2022] Oliver Reinhardt, Tom Warnke, and Adelinde M Uhrmacher.  A language for agent-based discrete-event modeling and simulation of linked lives.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 32(1):1–26, 2022.
  * Fowler [2010] Martin Fowler.  _Domain-Specific Languages_.  Pearson Education, 2010.
  * Pawlikowski et al. [2002] Krzysztof Pawlikowski, H-DJ Jeong, and J-SR Lee.  On credibility of simulation studies of telecommunication networks.  _IEEE Communications Magazine_ , 40(1):132–139, 2002.
  * Merali [2010] Zeeya Merali.  Computational science:… error.  _Nature_ , 467(7317):775–777, 2010.
  * Ewald and Uhrmacher [2014] Roland Ewald and Adelinde M Uhrmacher.  Sessl: A domain-specific language for simulation experiments.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 24(2):1–25, 2014.
  * Waltemath et al. [2011a] Dagmar Waltemath, Richard Adams, Frank T Bergmann, Michael Hucka, Fedor Kolpakov, Andrew K Miller, Ion I Moraru, David Nickerson, Sven Sahle, Jacky L Snoep, et al.  Reproducible computational biology experiments with sed-ml-the simulation experiment description markup language.  _BMC Systems Biology_ , 5(1):1–10, 2011a.
  * Salecker et al. [2019] Jan Salecker, Marco Sciaini, Katrin M Meyer, and Kerstin Wiegand.  The nlrx r package: A next-generation framework for reproducible netlogo model analyses.  _Methods in Ecology and Evolution_ , 10(11):1854–1863, 2019.
  * Görlach et al. [2011] Katharina Görlach, Mirko Sonntag, Dimka Karastoyanova, Frank Leymann, and Michael Reiter.  Conventional workflow technology for scientific simulation.  _Guide to e-Science: Next Generation Scientific Research and Discovery_ , pages 323–352, 2011.
  * Waltemath et al. [2011b] Dagmar Waltemath, Richard Adams, Daniel A Beard, Frank T Bergmann, Upinder S Bhalla, Randall Britten, Vijayalakshmi Chelliah, Michael T Cooling, Jonathan Cooper, Edmund J Crampin, et al.  Minimum Information about a Simulation Experiment (MIASE).  _PLoS Computational Biology_ , 7(4):e1001122, 2011b.
  * Rahmandad and Sterman [2012] Hazhir Rahmandad and John D Sterman.  Reporting guidelines for simulation-based research in social sciences.  _Systems Dynamics Review_ , 28(4):396–411, 2012\.
  * Grimm et al. [2014] Volker Grimm, Jacqueline Augusiak, Andreas Focks, Béatrice M Frank, Faten Gabsi, Alice SA Johnston, Chun Liu, Benjamin T Martin, Mattia Meli, Viktoriia Radchuk, et al.  Towards better modelling and decision support: Documenting model development, testing, and analysis using trace.  _Ecological Modelling_ , 280:129–139, 2014.
  * Monks et al. [2019] Thomas Monks, Christine SM Currie, Bhakti Stephan Onggo, Stewart Robinson, Martin Kunc, and Simon JE Taylor.  Strengthening the Reporting of Empirical Simulation Studies: Introducing the STRESS Guidelines.  _Journal of Simulation_ , 13(1):55–67, 2019.
  * Agha and Palmskog [2018] Gul Agha and Karl Palmskog.  A survey of statistical model checking.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 28(1):1–39, 2018.
  * Legay et al. [2019] Axel Legay, Anna Lukina, Louis Marie Traonouez, Junxing Yang, Scott A. Smolka, and Radu Grosu.  Statistical Model Checking.  In Bernhard Steffen and Gerhard Woeginger, editors, _Computing and Software Science_ , volume 10000, pages 478–504. Springer International Publishing, Cham, 2019.  ISBN 978-3-319-91907-2 978-3-319-91908-9.  doi:10.1007/978-3-319-91908-9_23.  URL http://link.springer.com/10.1007/978-3-319-91908-9_23.  Series Title: Lecture Notes in Computer Science.
  * Lorig et al. [2017] Fabian Lorig, Colja A Becker, and Ingo J Timm.  Formal specification of hypotheses for assisting computer simulation studies.  In _Proceedings of the Symposium on Theory of Modeling & Simulation_, pages 1–12, 2017.
  * Zeigler et al. [2018] Bernard P Zeigler, Alexandre Muzy, and Ernesto Kofman.  _Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations_.  Academic press, 2018.
  * Denil et al. [2017] Joachim Denil, Stefan Klikovits, Pieter J Mosterman, Antonio Vallecillo, and Hans Vangheluwe.  The experiment model and validity frame in m&s.  In _Proceedings of the Symposium on Theory of Modeling & Simulation_, pages 1–12, 2017.
  * Deelman et al. [2015] Ewa Deelman, Karan Vahi, Gideon Juve, Mats Rynge, Scott Callaghan, Philip J. Maechling, Rajiv Mayani, Weiwei Chen, Rafael Ferreira da Silva, Miron Livny, and Kent Wenger.  Pegasus, a workflow management system for science automation.  _Future Generation Computer Systems_ , 46:17–35, 2015.  ISSN 0167-739X.  doi:https://doi.org/10.1016/j.future.2014.10.008.  URL https://www.sciencedirect.com/science/article/pii/S0167739X14002015.
  * Bartocci et al. [2018] Ezio Bartocci, Jyotirmoy Deshmukh, Alexandre Donzé, Georgios Fainekos, Oded Maler, Dejan Ničković, and Sriram Sankaranarayanan.  Specification-Based Monitoring of Cyber-Physical Systems: A Survey on Theory, Tools and Applications.  In _Lectures on Runtime Verification_ , Lecture Notes in Computer Science, pages 135–175. Springer, Cham, 2018.  ISBN 978-3-319-75631-8 978-3-319-75632-5.  doi:10.1007/978-3-319-75632-5_5.  URL https://link.springer.com/chapter/10.1007/978-3-319-75632-5_5.
  * Wilsdorf et al. [2022] Pia Wilsdorf, Jakob Heller, Kai Budde, Julius Zimmermann, Tom Warnke, Christian Haubelt, Dirk Timmermann, Ursula van Rienen, and Adelinde M Uhrmacher.  A model-driven approach for conducting simulation experiments.  _Applied Sciences_ , 12(16):7977, 2022.
  * Chinosi and Trombetta [2012] Michele Chinosi and Alberto Trombetta.  Bpmn: An introduction to the standard.  _Computer Standards & Interfaces_, 34(1):124–134, 2012.
  * Mitra et al. [2019] Eshan D Mitra, Ryan Suderman, Joshua Colvin, Alexander Ionkov, Andrew Hu, Herbert M Sauro, Richard G Posner, and William S Hlavacek.  Pybionetfit and the biological property specification language.  _IScience_ , 19:1012–1036, 2019.
  * North et al. [2013] Michael J. North, Nicholson T. Collier, Jonathan Ozik, Eric R. Tatara, Charles M. Macal, Mark Bragen, and Pam Sydelko.  Complex adaptive systems modeling with Repast Simphony.  _Complex Adaptive Systems Modeling_ , 1(1):3, March 2013.  ISSN 2194-3206.  doi:10.1186/2194-3206-1-3.  URL https://doi.org/10.1186/2194-3206-1-3.
  * Johnsen et al. [2011] Einar Broch Johnsen, Reiner Hähnle, Jan Schäfer, Rudolf Schlatte, and Martin Steffen.  ABS: A core language for abstract behavioral specification.  In Bernhard K. Aichernig, Frank de Boer, and Marcello M. Bonsangue, editors, _Proc. 9th Intl. Symp. on Formal Methods for Components and Objects (FMCO 2010)_ , volume 6957 of _LNCS_ , pages 142–164. Springer, 2011\.
  * Kamburjan et al. [2022] Eduard Kamburjan, Stefan Mitsch, and Reiner Hähnle.  A hybrid programming language for formal modeling and verification of hybrid systems.  _Leibniz Transactions on Embedded Systems_ , 8(1), 2022\.  Special Issue on Distributed Hybrid Systems.
  * Warnke and Uhrmacher [2018] Tom Warnke and Adelinde M Uhrmacher.  Complex simulation experiments made easy.  In _2018 Winter Simulation Conference (WSC)_ , pages 410–424. IEEE, 2018.
  * Bertalanffy [1968] Ludwig von Bertalanffy.  _General System Theory: Foundations, Development, Applications_.  G. Braziller, 1968.
  * Milner et al. [1992] Robin Milner, Joachim Parrow, and David Walker.  A calculus of mobile processes, i.  _Information and Computation_ , 100(1):1–40, 1992\.
  * Priami [1995] Corrado Priami.  Stochastic $\pi$-calculus.  _The Computer Journal_ , 38(7):578–589, 1995\.  doi:10.1093/comjnl/38.7.578.
  * Phillips and Cardelli [2007] Andrew Phillips and Luca Cardelli.  Efficient, correct simulation of biological processes in the stochastic pi-calculus.  In _Proceedings of the 2007 International Conference on Computational Methods in Systems Biology_ , CMSB’07, page 184–199, Berlin, Heidelberg, 2007. Springer-Verlag.  ISBN 3540751394.
  * Jensen [1996] Kurt Jensen.  _Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use_ , volume 1.  Springer Science & Business Media, 1996.
  * John et al. [2010] Mathias John, Cédric Lhoussaine, Joachim Niehren, and Adelinde M. Uhrmacher.  _The Attributed Pi-Calculus with Priorities_ , pages 13–76.  Springer Berlin Heidelberg, Berlin, Heidelberg, 2010.  ISBN 978-3-642-11712-1.  doi:10.1007/978-3-642-11712-1_2.  URL https://doi.org/10.1007/978-3-642-11712-1_2.
  * Barros [1997] Fernando J. Barros.  Modeling formalisms for dynamic structure systems.  _ACM Transactions on Modeling and Computer Simulation_ , 7(4):501–515, oct 1997.  ISSN 1049-3301.  doi:10.1145/268403.268423.  URL https://doi.org/10.1145/268403.268423.
  * Cellier and Greifeneder [2013] François E Cellier and Jurgen Greifeneder.  _Continuous System Modeling_.  Springer Science & Business Media, 2013.
  * Zeigler [1977] Bernard P. Zeigler.  Constructs for the specifications of models and experimental frames.  _SIGSIM Simul. Dig._ , 9(1):12–13, sep 1977\.  ISSN 0163-6103.  doi:10.1145/1102505.1102510.  URL https://doi.org/10.1145/1102505.1102510.
  * Traoré and Muzy [2006] Mamadou K Traoré and Alexandre Muzy.  Capturing the dual relationship between simulation models and their context.  _Simulation Modelling Practice and Theory_ , 14(2):126–142, 2006.
  * Van Mierlo et al. [2020] Simon Van Mierlo, Bentley James Oakes, Bert Van Acker, Raheleh Eslampanah, Joachim Denil, and Hans Vangheluwe.  Exploring validity frames in practice.  In _Systems Modelling and Management: First International Conference, ICSMM 2020, Bergen, Norway, June 25–26, 2020_ , pages 131–148. Springer, 2020.
  * Ribault and Wainer [2012] Judicaël Ribault and Gabriel Wainer.  Using workflows and web services to manage simulation studies (wip).  In _Proceedings of the 2012 Symposium on Theory of Modeling and Simulation-DEVS Integrative M &S Symposium_, pages 1–6, 2012.
  * Cuevas-Vicenttín et al. [2012] Víctor Cuevas-Vicenttín, Saumen C. Dey, Sven Köhler, Sean Riddle, and Bertram Ludäscher.  Scientific workflows and provenance: Introduction and research opportunities.  _Datenbank-Spektrum_ , 12(3):193–203, 2012.  doi:10.1007/s13222-012-0100-z.  URL https://doi.org/10.1007/s13222-012-0100-z.
  * Kahn and MacQueen [1976] Gilles Kahn and David MacQueen.  Coroutines and networks of parallel processes.  1976\.
  * Ludäscher et al. [2006] Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat Jaeger, Matthew Jones, Edward A Lee, Jing Tao, and Yang Zhao.  Scientific workflow management and the kepler system.  _Concurrency and Computation: Practice and Experience_ , 18(10):1039–1065, 2006.
  * Oinn et al. [2006] Tom Oinn, Mark Greenwood, Matthew Addis, M Nedim Alpdemir, Justin Ferris, Kevin Glover, Carole Goble, Antoon Goderis, Duncan Hull, Darren Marvin, Peter Li, Phillip Lord, Matthew R. Pocock, Martin Senger, Robert Stevens, Anil Wipat, and Chris Wroe.  Taverna: Lessons in creating a workflow environment for the life sciences.  _Concurrency and Computation: Practice and Experience_ , 18(10):1067–1100, 2006.
  * Leye et al. [2009] Stefan Leye, Jan Himmelspach, and Adelinde M Uhrmacher.  A discussion on experimental model validation.  In _2009 11th International Conference on Computer Modelling and Simulation_ , pages 161–167. IEEE, 2009.
  * Batt et al. [2006] Gregory Batt, JT Bradley, Roland Ewald, Francois Fages, Holger Hermans, Jane Hillston, Peter Kemper, Alke Martens, Pieter Mosterman, Flemming Nielson, et al.  Working groups’ report: The challenge of combining simulation and verification.  In _Dagstuhl Seminar Proc. 06161: Simulation and Verification of Dynamic Systems_ , 2006.
  * Clarke et al. [1999] Edmund M. Clarke, Jr., Orna Grumberg, and Doron A. Peled.  _Model Checking_.  MIT Press, Cambridge, MA, USA, 1999.  ISBN 0-262-03270-8.
  * Donzé et al. [2013] Alexandre Donzé, Thomas Ferrere, and Oded Maler.  Efficient robust monitoring for stl.  In _International Conference on Computer Aided Verification_ , pages 264–279. Springer, 2013.
  * Nenzi et al. [2018] Laura Nenzi, Luca Bortolussi, Vincenzo Ciancia, Michele Loreti, and Mieke Massink.  Qualitative and quantitative monitoring of spatio-temporal properties with SSTL.  _Logical Methods in Computer Science_ , 14(4), 2018.  doi:10.23638/LMCS-14(4:2)2018.  URL https://doi.org/10.23638/LMCS-14(4:2)2018.
  * Brim et al. [2014] Lubos Brim, Petr Dluhos, David Safránek, and Tomas Vejpustek.  Stl∗: Extending signal temporal logic with signal-value freezing operator.  _Information and Computation_ , 236:52–67, 2014.  doi:10.1016/j.ic.2014.01.012.  URL https://doi.org/10.1016/j.ic.2014.01.012.
  * Donzé et al. [2012] Alexandre Donzé, Oded Maler, Ezio Bartocci, Dejan Nickovic, Radu Grosu, and Scott A. Smolka.  On temporal logic and signal processing.  In Supratik Chakraborty and Madhavan Mukund, editors, _Automated Technology for Verification and Analysis - 10th International Symposium, ATVA 2012, Thiruvananthapuram, India, October 3-6, 2012. Proceedings_ , volume 7561 of _Lecture Notes in Computer Science_ , pages 92–106. Springer, 2012.  doi:10.1007/978-3-642-33386-6_9.  URL https://doi.org/10.1007/978-3-642-33386-6_9.
  * Vissat et al. [2019] Ludovica Luisa Vissat, Michele Loreti, Laura Nenzi, Jane Hillston, and Glenn Marion.  Analysis of spatio-temporal properties of stochastic systems using tstl.  _ACM Transactions on Modeling and Computer Simulation_ , 29(4), 2019.  ISSN 1049-3301.  doi:10.1145/3326168.  URL https://doi.org/10.1145/3326168.
  * Maoz et al. [2011] Shahar Maoz, Jan Oliver Ringert, and Bernhard Rumpe.  CDDiff: Semantic Differencing for Class Diagrams.  In Mira Mezini, editor, _ECOOP 2011 - Object-Oriented Programming_ , pages 230–254. Springer Berlin Heidelberg, 2011.  URL https://se-rwth.de/publications/CDDiff-Semantic-Differencing-for-Class-Diagrams.pdf.
  * Rumpe [2016] Bernhard Rumpe.  _Modeling with UML: Language, Concepts, Methods_.  Springer International, July 2016.  URL https://mbse.se-rwth.de/.
  * Björkander and Kobryn [2003] Morgan Björkander and Cris Kobryn.  Architecting Systems with UML 2.0.  _IEEE Software_ , 20(4):57–61, 2003.  ISSN 0740-7459.
  * Evans et al. [1999] Andy Evans, Robert France, Kevin Lano, and Bernhard Rumpe.  Meta-Modelling Semantics of UML.  In H. Kilov, B. Rumpe, and I. Simmonds, editors, _Behavioral Specifications of Businesses and Systems_ , pages 45–60. Kluver Academic Publisher, 1999.  ISBN 978-1-4613-7383-4.
  * Evans et al. [1998] Andy Evans, Jean-Michel Bruel, Robert France, Kevin Lano, and Bernhard Rumpe.  Making UML Precise.  In _OOPSLA’98 Workshop on “Formalizing UML. Why and How?”_ , Vancouver, Canada, October 1998.
  * Harel [1987] David Harel.  Statecharts: A visual formalism for complex systems.  _Science of Computer Programming_ , 8(3):231–274, 1987.
  * OMG [2015] OMG.  Unified modeling language -uml- specification, October 2015.  URL http://www.omg.org/spec/UML/2.5/.  Accessed: 2023-09-30.
  * Object Management Group [2006] Object Management Group.  Object Constraint Language Version 2.0 (OMG Standard 2006-05-01), 2006\.  URL https://www.omg.org/spec/OCL/2.0/PDF.  Accessed: 2023-09-30.
  * Casse [2017] Olivier Casse.  _SysML in Action with Cameo Systems Modeler_.  Elsevier, 2017.  ISBN 978-1-78548-171-0.
  * Nikolaidou et al. [2015] Mara Nikolaidou, George-Dimitrios Kapos, Anargyros Tsadimas, Vassilis Dalakas, and Dimosthenis Anagnostopoulos.  Simulating sysml models: Overview and challenges.  In _2015 10th System of Systems Engineering Conference (SoSE)_ , 2015\.  doi:10.1109/SYSOSE.2015.7151961.
  * Clark et al. [2015] Tony Clark, Mark van den Brand, Benoit Combemale, and Bernhard Rumpe.  Conceptual Model of the Globalization for Domain-Specific Languages.  In _Globalizing Domain-Specific Languages_ , LNCS 9400, pages 7–20. Springer, 2015.  URL http://www.se-rwth.de/publications/Conceptual-Model-of-the-Globalization-for-Domain-Specific-Languages.pdf.
  * Faeder et al. [2009] James R Faeder, Michael L Blinov, and William S Hlavacek.  Rule-based modeling of biochemical systems with bionetgen.  In _Systems Biology_ , pages 113–167. Springer, 2009.
  * Hoops et al. [2006] Stefan Hoops, Sven Sahle, Ralph Gauges, Christine Lee, Jürgen Pahle, Natalia Simus, Mudita Singhal, Liang Xu, Pedro Mendes, and Ursula Kummer.  Copasi—a complex pathway simulator.  _Bioinformatics_ , 22(24):3067–3074, 2006.
  * Blinov et al. [2017] Michael L Blinov, James C Schaff, Dan Vasilescu, Ion I Moraru, Judy E Bloom, and Leslie M Loew.  Compartmental and spatial rule-based modeling with virtual cell.  _Biophysical Journal_ , 113(7):1365–1372, 2017\.
  * Ciocchetta and Hillston [2009] Federica Ciocchetta and Jane Hillston.  Bio-pepa: A framework for the modelling and analysis of biological systems.  _Theoretical Computer Science_ , 410(33-34):3065–3084, 2009.
  * Gilmore and Hillston [1994] Stephen Gilmore and Jane Hillston.  The pepa workbench: A tool to support a process algebra-based approach to performance modelling.  _Computer Performance Evaluation_ , 794:353–368, 1994.
  * Gupta and Mendes [2018] Abhishekh Gupta and Pedro Mendes.  An overview of network-based and -free approaches for stochastic simulation of biochemical systems.  _Computation_ , 6(1):9, 2018.
  * Andelfinger et al. [2023] Philipp Andelfinger, Till Köster, and Adelinde Uhrmacher.  Zero lookahead? zero problem. the window racer algorithm.  In _Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation_ , pages 1–11, 2023.
  * Székely Jr and Burrage [2014] Tamás Székely Jr and Kevin Burrage.  Stochastic simulation in systems biology.  _Computational and Structural Biotechnology Journal_ , 12(20-21):14–25, 2014.
  * Köster et al. [2022] Till Köster, Tom Warnke, and Adelinde M. Uhrmacher.  Generating fast specialized simulators for stochastic reaction networks via partial evaluation.  _ACM Transactions on Modeling and Computer Simulation_ , 32(2), 2022.  ISSN 1049-3301.  doi:10.1145/3485465.  URL https://doi.org/10.1145/3485465.
  * Abar et al. [2017] Sameera Abar, Georgios K. Theodoropoulos, Pierre Lemarinier, and Gregory M.P. O’Hare.  Agent based modelling and simulation tools: A review of the state-of-art software.  _Computer Science Review_ , 24:13–33, 2017.  ISSN 1574-0137.  doi:https://doi.org/10.1016/j.cosrev.2017.03.001.  URL https://www.sciencedirect.com/science/article/pii/S1574013716301198.
  * Zeigler and Sarjoughian [2005] Bernard P. Zeigler and Hessam Sarjoughian.  Introduction to DEVS Modeling and Simulation with JAVA: Developing Component-based Simulation Models.  Technical report, Arizona Center of Integrative Modeling and Simulation, University of Arizona, 2005.
  * Wainer et al. [2001] Gabriel Wainer, Gastón Christen, and Alejandro Dobniewski.  Defining devs models with the cd++ toolkit.  In _Proceedings of SCS European Simulation Symposium_ , 2001.
  * North et al. [2006] Michael J North, Nicholson T Collier, and Jerry R Vos.  Experiences creating three implementations of the repast agent modeling toolkit.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 16(1):1–25, 2006.
  * Luke et al. [2005] Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, Keith Sullivan, and Gabriel Balan.  Mason: A multiagent simulation environment.  _Simulation_ , 81(7):517–527, 2005.
  * Howell and McNab [1998] Fred Howell and Ross McNab.  Simjava: A discrete event simulation library for java.  _Simulation Series_ , 30:51–56, 1998.
  * McNab and Howell [1996] Ross McNab and Fred Howell.  Using java for discrete event simulation.  In _Proceedings of the Twelfth UK Computer and Telecommunications Performance Engineering Workshop_ , pages 219–228. Citeseer, 1996.
  * Bersini [2012] Hugues Bersini.  Uml for abm.  _Journal of Artificial Societies and Social Simulation_ , 15(1):9, 2012.
  * Wagner [2014] Gerd Wagner.  Tutorial: Information and process modeling for simulation.  In _Proceedings of the 2014 Winter Simulation Conference_ , WSC ’14, page 103–117. IEEE Press, 2014.
  * Warnke et al. [2016] Tom Warnke, Oliver Reinhardt, and Adelinde M Uhrmacher.  Population-based ctmcs and agent-based models.  In _2016 Winter Simulation Conference (WSC)_ , pages 1253–1264. IEEE, 2016.
  * de Boer et al. [2017] Frank de Boer, Crystal Chang Din, Kiko Fernandez-Reyes, Reiner Hähnle, Ludovic Henrio, Einar Broch Johnsen, Ehsan Khamespanah, Justine Rochas, Vlad Serbanescu, Marjan Sirjani, and Albert Mingkun Yang.  A survey of active object languages.  _ACM Computing Surveys_ , 50(5):76:1–76:39, oct 2017.  Article 76.
  * Agha [1986] Gul Agha.  An overview of actor languages.  In Peter Wegner and Bruce D. Shriver, editors, _Proceedings of the 1986 SIGPLAN Workshop on Object-Oriented Programming, OOPWORK 1986, Yorktown Heights, New York, USA, June 9-13, 1986_ , pages 58–67. ACM, 1986.  doi:10.1145/323779.323743.  URL https://doi.org/10.1145/323779.323743.
  * Henrio and Rochas [2017] Ludovic Henrio and Justine Rochas.  Multiactive objects and their applications.  _Logical Methods in Computer Science_ , 13(4), 2017.  doi:10.23638/LMCS-13(4:12)2017.  URL https://doi.org/10.23638/LMCS-13(4:12)2017.
  * Schlatte et al. [2022] Rudolf Schlatte, Einar Broch Johnsen, Eduard Kamburjan, and Silvia Lizeth Tapia Tarifa.  The ABS simulator toolchain.  _Science of Computer Programming_ , 223:102861, 2022.  doi:10.1016/j.scico.2022.102861.
  * Kamburjan et al. [2018] Eduard Kamburjan, Reiner Hähnle, and Sebastian Schön.  Formal modeling and analysis of railway operations with active objects.  _Science of Computer Programming_ , 166:167–193, 2018.  doi:10.1016/j.scico.2018.07.001.  URL https://doi.org/10.1016/j.scico.2018.07.001.
  * Emmanuel et al. [2021] Julien Emmanuel, Matthieu Moy, Ludovic Henrio, and Grégoire Pichon.  S4BXI: the mpi-ready portals 4 simulator.  In _29th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2021, Houston, TX, USA, November 3-5, 2021_ , pages 1–8. IEEE, 2021.  doi:10.1109/MASCOTS53633.2021.9614285.  URL https://doi.org/10.1109/MASCOTS53633.2021.9614285.
  * Turin et al. [2020] Gianluca Turin, Andrea Borgarelli, Simone Donetti, Einar Broch Johnsen, Silvia Lizeth Tapia Tarifa, and Ferruccio Damiani.  A formal model of the kubernetes container framework.  In Tiziana Margaria and Bernhard Steffen, editors, _Leveraging Applications of Formal Methods, Verification and Validation: Verification Principles - 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20-30, 2020, Proceedings, Part I_ , volume 12476 of _Lecture Notes in Computer Science_ , pages 558–577. Springer, 2020.  doi:10.1007/978-3-030-61362-4_32.  URL https://doi.org/10.1007/978-3-030-61362-4_32.
  * Johnsen et al. [2012] Einar Broch Johnsen, Rudolf Schlatte, and Silvia Lizeth Tapia Tarifa.  Modeling resource-aware virtualized applications for the cloud in real-time ABS.  In Toshiaki Aoki and Kenji Taguchi, editors, _Formal Methods and Software Engineering - 14th International Conference on Formal Engineering Methods, ICFEM 2012, Kyoto, Japan, November 12-16, 2012. Proceedings_ , volume 7635 of _Lecture Notes in Computer Science_ , pages 71–86. Springer, 2012.  doi:10.1007/978-3-642-34281-3_8.  URL https://doi.org/10.1007/978-3-642-34281-3_8.
  * Khamespanah et al. [2018] Ehsan Khamespanah, Ramtin Khosravi, and Marjan Sirjani.  An efficient TCTL model checking algorithm and a reduction technique for verification of timed actor models.  _Science of Computer Programming_ , 153:1–29, 2018.  doi:10.1016/j.scico.2017.11.004.  URL https://doi.org/10.1016/j.scico.2017.11.004.
  * Sirjani [2019] Marjan Sirjani.  Analysing real-time distributed systems using timed actors.  In Floriano De Rango, Carlos T. Calafate, Miroslav Voznák, Alfredo Garro, and Mauro Tropea, editors, _23rd IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications DS-RT 2019, Cosenza, Italy, October 7-9, 2019_ , page 1. IEEE, 2019.  doi:10.1109/DS-RT47707.2019.8958670.  URL https://doi.org/10.1109/DS-RT47707.2019.8958670.
  * Din et al. [2015] Crystal Chang Din, Richard Bubel, and Reiner Hähnle.  Key-abs: A deductive verification tool for the concurrent modelling language ABS.  In Amy P. Felty and Aart Middeldorp, editors, _Automated Deduction - CADE-25 - 25th International Conference on Automated Deduction, Berlin, Germany, August 1-7, 2015, Proceedings_ , volume 9195 of _Lecture Notes in Computer Science_ , pages 517–526. Springer, 2015.  doi:10.1007/978-3-319-21401-6_35.  URL https://doi.org/10.1007/978-3-319-21401-6_35.
  * Kamburjan et al. [2020] Eduard Kamburjan, Crystal Chang Din, Reiner Hähnle, and Einar Broch Johnsen.  Behavioral contracts for cooperative scheduling.  In Wolfgang Ahrendt, Bernhard Beckert, Richard Bubel, Reiner Hähnle, and Mattias Ulbrich, editors, _Deductive Software Verification: Future Perspectives_ , volume 12345 of _LNCS_ , pages 85–121. Springer, Cham, 2020.
  * Giachino et al. [2016] Elena Giachino, Cosimo Laneve, and Michael Lienhardt.  A framework for deadlock detection in core ABS.  _Software and Systems Modeling_ , 15(4):1013–1048, 2016.  doi:10.1007/s10270-014-0444-y.
  * Albert et al. [2015] Elvira Albert, Jesús Correas Fernández, Germán Puebla, and Guillermo Román-Díez.  Quantified abstract configurations of distributed systems.  _Formal Aspects of Computing_ , 27(4):665–699, 2015.  doi:10.1007/s00165-014-0321-z.
  * Bubel et al. [2014] Richard Bubel, Antonio Flores-Montoya, and Reiner Hähnle.  Analysis of executable software models.  In Marco Bernardo, Ferruccio Damiani, Reiner Hähnle, Einar Broch Johnsen, and Ina Schaefer, editors, _Formal Methods for Executable Software Models: 14th Intl. School on Formal Methods for the Design of Computer, Communication, and Software Systems, SFM, Bertinoro, Italy_ , volume 8483 of _LNCS_ , pages 1–25. Springer, 2014.  doi:10.1007/978-3-319-07317-0_1.
  * Matloff [2008] Norm Matloff.  Introduction to discrete-event simulation and the simpy language.  _Davis, CA. Dept of Computer Science. University of California at Davis._ , 2(2009):1–33, 2008.
  * Vasudevan et al. [2021] Sandhya Vasudevan, Faizan Zafar, Yuan Xingran, Ravikumar Singh, and Wil MP van der Aalst.  A python extension to simulate petri nets in process mining.  _arXiv preprint arXiv:2102.08774_ , 2021.
  * Van Tendeloo and Vangheluwe [2014] Yentl Van Tendeloo and Hans Vangheluwe.  The modular architecture of the python (p) devs simulation kernel.  In _Proceedings of the 2014 Symposium on Theory of Modeling and Simulation-DEVS_ , pages 387–392, 2014.
  * Douglas-Smith et al. [2020] Dominique Douglas-Smith, Takuya Iwanaga, Barry FW Croke, and Anthony J Jakeman.  Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques.  _Environmental Modelling & Software_, 124:104588, 2020\.
  * Peyman et al. [2021] Mohammad Peyman, Pedro Copado, Javier Panadero, Angel A. Juan, and Mohammad Dehghanimohammadabadi.  A tutorial on how to connect python with different simulation software to develop rich simheuristics.  In _2021 Winter Simulation Conference (WSC)_ , pages 1–12, 2021.  doi:10.1109/WSC52266.2021.9715511.
  * Peng et al. [2016] Danhua Peng, Tom Warnke, Fiete Haack, and Adelinde M. Uhrmacher.  Reusing simulation experiment specifications to support developing models by successive extension.  _Simulation Modelling Practice and Theory_ , 68:33–53, 2016\.  doi:10.1016/j.simpat.2016.07.006.  URL https://doi.org/10.1016/j.simpat.2016.07.006.
  * Wilsdorf et al. [2023a] Pia Wilsdorf, Anja Wolpers, Jason Hilton, Fiete Haack, and Adelinde Uhrmacher.  Automatic reuse, adaption, and execution of simulation experiments via provenance patterns.  _ACM Transactions on Modeling and Computer Simulation_ , 33(1–2), feb 2023a.  ISSN 1049-3301.  doi:10.1145/3564928.  URL https://doi.org/10.1145/3564928.
  * Ludäscher et al. [2009] Bertram Ludäscher, Mathias Weske, Timothy M. McPhillips, and Shawn Bowers.  Scientific workflows: Business as usual?  In Umeshwar Dayal, Johann Eder, Jana Koehler, and Hajo A. Reijers, editors, _Business Process Management (BPM), 7th Intl. Conference_ , volume 5701 of _LNCS_ , pages 31–47, Ulm, Germany, 2009. Springer.  doi:10.1007/978-3-642-03848-8_4.  URL https://doi.org/10.1007/978-3-642-03848-8_4.
  * McPhillips et al. [2009] Timothy M. McPhillips, Shawn Bowers, Daniel Zinn, and Bertram Ludäscher.  Scientific workflow design for mere mortals.  _Future Generation Computer Systems_ , 25(5):541–551, 2009.  doi:10.1016/j.future.2008.06.013.  URL https://doi.org/10.1016/j.future.2008.06.013.
  * Zinn et al. [2009] Daniel Zinn, Shawn Bowers, Timothy M. McPhillips, and Bertram Ludäscher.  Scientific workflow design with data assembly lines.  In Ewa Deelman and Ian J. Taylor, editors, _4th Workshop on Workflows in Support of Large-Scale Scienc (WORKS)_. ACM, 2009.  doi:10.1145/1645164.1645178.  URL https://doi.org/10.1145/1645164.1645178.
  * Crusoe et al. [2022] Michael R. Crusoe, Sanne Abeln, Alexandru Iosup, Peter Amstutz, John Chilton, Nebojša Tijanić, Hervé Ménager, Stian Soiland-Reyes, Bogdan Gavrilović, Carole Goble, and The CWL Community.  Methods included: Standardizing computational reuse and portability with the Common Workflow Language.  _Communications of the ACM_ , 65(6):54–63, May 2022.  ISSN 0001-0782.  doi:10.1145/3486897.  URL https://dl.acm.org/doi/10.1145/3486897.
  * Otter et al. [2015] Martin Otter, Nguyen Thuy, Daniel Bouskela, Lena Buffoni, Hilding Elmqvist, Peter Fritzson, Alfredo Garro, Audrey Jardin, Hans Olsson, Maxime Payelleville, Wladimir Schamai, Eric Thomas, and Andrea Tundis.  Formal requirements modeling for simulation-based verification.  In _11th International Modelica Conference_ , 2015.
  * Kwiatkowska et al. [2005] Marta Z. Kwiatkowska, Gethin Norman, and David Parker.  Probabilistic model checking in practice: Case studies with PRISM.  _SIGMETRICS Performance Evaluation Review_ , 32(4):16–21, 2005.  doi:10.1145/1059816.1059820.  URL https://doi.org/10.1145/1059816.1059820.
  * Bortolussi et al. [2016] Luca Bortolussi, Dimitrios Milios, and Guido Sanguinetti.  Smoothed model checking for uncertain continuous-time markov chains.  _Information and Computation_ , 247:235–253, 2016.  doi:10.1016/j.ic.2016.01.004.  URL https://doi.org/10.1016/j.ic.2016.01.004.
  * Bartocci et al. [2022] Ezio Bartocci, Cristinel Mateis, Eleonora Nesterini, and Dejan Nickovic.  Survey on mining signal temporal logic specifications.  _Information and Computation_ , 289(Part):104957, 2022.  doi:https://doi.org/10.1016/j.ic.2022.104957.
  * Sepúlveda et al. [2016] Samuel Sepúlveda, Ania Cravero, and Cristina Cachero.  Requirements modeling languages for software product lines: A systematic literature review.  _Information and Software Technology_ , 69:16–36, 2016\.  ISSN 0950-5849.  doi:https://doi.org/10.1016/j.infsof.2015.08.007.  URL https://www.sciencedirect.com/science/article/pii/S0950584915001494.
  * Combemale et al. [2016] Benoit Combemale, Robert France, Jean-Marc Jézéquel, Bernhard Rumpe, James Steel, and Didier Vojtisek.  _Engineering Modeling Languages: Turning Domain Knowledge into Tools_.  Chapman & Hall/CRC Innovations in Software Engineering and Software Development Series, November 2016.
  * Cengarle et al. [2009] María Victoria Cengarle, Hans Grönniger, and Bernhard Rumpe.  Variability within Modeling Language Definitions.  In _Conference on Model Driven Engineering Languages and Systems (MODELS’09)_ , LNCS 5795, pages 670–684. Springer, 2009.  URL http://www.se-rwth.de/publications/Variability-within-Modeling-Language-Definitions.pdf.
  * Hölldobler et al. [2021] Katrin Hölldobler, Oliver Kautz, and Bernhard Rumpe.  _MontiCore Language Workbench and Library Handbook: Edition 2021_.  Aachener Informatik-Berichte, Software Engineering, Band 48. Shaker Verlag, May 2021.  ISBN 978-3-8440-8010-0.  URL http://www.monticore.de/handbook.pdf.
  * Clark et al. [2004] Tony Clark, Andy Evans, Paul Sammut, and James Willans.  An executable metamodelling facility for domain specific language design.  In _Proc. 4th OOPSLA Workshop on Domain-Specific Modeling_ , 2004\.
  * Atkinson and Kuhne [2003] Colin Atkinson and Thomas Kuhne.  Model-driven development: a metamodeling foundation.  _IEEE Software_ , 20(5), 2003.  ISSN 0740-7459.
  * Kleppe [2008] Anneke Kleppe.  _Software Language Engineering: Creating Domain-Specific Languages using Metamodels_.  Pearson Education, 2008.
  * Zschaler et al. [2009] Steffen Zschaler, Dimitrios S Kolovos, Nikolaos Drivalos, Richard F Paige, and Awais Rashid.  Domain-specific metamodelling languages for software language engineering.  In _International Conference on Software Language Engineering_ , pages 334–353. Springer, 2009.
  * Object Management Group [2008] Object Management Group.  Meta Object Facility (MOF) 2.0 Query/View/Transformation Specification, April 2008.  URL https://www.omg.org/spec/QVT/1.3/PDF.  Accessed: 2023-09-30.
  * Kapos et al. [2021] George-Dimitrios Kapos, Anargyros Tsadimas, Christos Kotronis, Vassilis Dalakas, Mara Nikolaidou, and Dimosthenis Anagnostopoulos.  A declarative approach for transforming sysml models to executable simulation models.  _IEEE Transactions on Systems, Man, and Cybernetics: Systems_ , 51(6):3330–3345, 2021.  doi:10.1109/TSMC.2019.2922153.
  * Cetinkaya et al. [2012] Deniz Cetinkaya, Alexander Verbraeck, and Mamadou D. Seck.  Model transformation from bpmn to devs in the mdd4ms framework.  In _Proceedings of the 2012 Symposium on Theory of Modeling and Simulation - DEVS Integrative M &S Symposium_, TMS/DEVS ’12, San Diego, CA, USA, 2012. Society for Computer Simulation International.  ISBN 9781618397867.
  * de Lara et al. [2004] Juan de Lara, Hans Vangheluwe, and Manuel Alfonseca.  Meta-modelling and graph grammars for multi-paradigm modelling in AToM3.  _Software & Systems Modeling_, 3(3):194–209, August 2004.  ISSN 1619-1374.  doi:10.1007/s10270-003-0047-5.  URL https://doi.org/10.1007/s10270-003-0047-5.
  * Mellor et al. [2002] Stephen J Mellor, Kendall Scott, Axel Uhl, and Dirk Weise.  Model-driven architecture.  In _International Conference on Object-Oriented Information Systems_ , pages 290–297. Springer, 2002.
  * Teran-Somohano et al. [2015] Alejandro Teran-Somohano, Alice E. Smith, Joseph Ledet, Levent Yilmaz, and Halit Oğuztüzün.  A model-driven engineering approach to simulation experiment design and execution.  In _2015 Winter Simulation Conference (WSC)_ , pages 2632–2643, 2015\.  doi:10.1109/WSC.2015.7408371.
  * Robinson [2008] Stewart Robinson.  Conceptual modelling for simulation part i: Definition and requirements.  _Journal of the Operational Research Society_ , 59(3):278–290, 2008.
  * Fujimoto et al. [2017] Richard Fujimoto, Conrad Bock, Wei Chen, Ernest Page, and Jitesh H Panchal.  _Research Challenges in Modeling and Simulation for Engineering Complex Systems_.  Springer, 2017.
  * Wilsdorf et al. [2020] Pia Wilsdorf, Fiete Haack, and Adelinde M Uhrmacher.  Conceptual models in simulation studies: Making it explicit.  In _2020 Winter Simulation Conference (WSC)_ , pages 2353–2364. IEEE, 2020.
  * Grimm et al. [2017] Volker Grimm, Gary Polhill, and Julia Touza.  Documenting social simulation models: the odd protocol as a standard.  In _Simulating Social Complexity_ , pages 349–365. Springer, 2017\.
  * Grimm et al. [2020] Volker Grimm, Steven F Railsback, Christian E Vincenot, Uta Berger, Cara Gallagher, Donald L DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Juergen Groeneveld, et al.  The odd protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism.  _Journal of Artificial Societies and Social Simulation_ , 23(2), 2020.
  * Erdemir et al. [2012] Ahmet Erdemir, Trent M Guess, Jason Halloran, Srinivas C Tadepalli, and Tina M Morrison.  Considerations for reporting finite element analysis studies in biomechanics.  _Journal of Biomechanics_ , 45(4):625–633, 2012\.
  * Moreau and Groth [2013] Luc Moreau and Paul Groth.  Provenance: an introduction to prov.  _Synthesis Lectures on the Semantic Web: Theory and Technology_ , 3(4):1–129, 2013.
  * Ruscheinski and Uhrmacher [2017] Andreas Ruscheinski and Adelinde Uhrmacher.  Provenance in modeling and simulation studies—bridging gaps.  In _2017 Winter Simulation Conference (WSC)_ , pages 872–883. IEEE, 2017.
  * Haack et al. [2020] Fiete Haack, Kai Budde, and Adelinde M. Uhrmacher.  Exploring mechanistic and temporal regulation of lrp6 endocytosis in canonical wnt signaling.  _Journal of Cell Science_ , 133(15), August 2020.
  * Budde et al. [2021] Kai Budde, Jacob Smith, Pia Wilsdorf, Fiete Haack, and Adelinde M Uhrmacher.  Relating simulation studies by provenance—developing a family of wnt signaling models.  _PLOS Computational Biology_ , 17(8):e1009227, 2021.
  * Ludäscher et al. [2009] Bertram Ludäscher, Shawn Bowers, and Timothy McPhillips.  Scientific Workflows.  In Ling Liu and M. Tamer Özsu, editors, _Encyclopedia of Database Systems_ , pages 2507–2511. Springer US, Boston, MA, 2009.  ISBN 978-0-387-39940-9.  doi:10.1007/978-0-387-39940-9_1471.  URL https://doi.org/10.1007/978-0-387-39940-9_1471.
  * Liew et al. [2016] Chee Sun Liew, Malcolm P. Atkinson, Michelle Galea, Tan Fong Ang, Paul Martin, and Jano I. Van Hemert.  Scientific Workflows: Moving Across Paradigms.  _ACM Computing Surveys_ , 49(4):66:1–66:39, December 2016.  ISSN 0360-0300.  doi:10.1145/3012429.  URL https://dl.acm.org/doi/10.1145/3012429.
  * Herschel et al. [2017] Melanie Herschel, Ralf Diestelkämper, and Houssem Ben Lahmar.  A survey on provenance: What for? What form? What from?  _The VLDB Journal_ , 26(6):881–906, December 2017\.  ISSN 1066-8888, 0949-877X.  doi:10.1007/s00778-017-0486-1.  URL https://link-springer-com.proxy2.library.illinois.edu/article/10.1007/s00778-017-0486-1.
  * Ruscheinski et al. [2019a] Andreas Ruscheinski, Pia Wilsdorf, Marcus Dombrowsky, and Adelinde M. Uhrmacher.  Capturing and Reporting Provenance Information of Simulation Studies Based on an Artifact-Based Workflow Approach.  In _Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation_ , SIGSIM-PADS ’19, pages 185–196, New York, NY, USA, 2019a. ACM.  ISBN 978-1-4503-6723-3.  event-place: Chicago, IL, USA.
  * Vaculín et al. [2011] Roman Vaculín, Richard Hull, Terry Heath, Craig Cochran, Anil Nigam, and Piyawadee Sukaviriya.  Declarative business artifact centric modeling of decision and knowledge intensive business processes.  In _2011 IEEE 15th International Enterprise Distributed Object Computing Conference_ , pages 151–160. IEEE, 2011.
  * Ruscheinski et al. [2019b] Andreas Ruscheinski, Tom Warnke, and Adelinde M Uhrmacher.  Artifact-based workflows for supporting simulation studies.  _IEEE Transactions on Knowledge and Data Engineering_ , 32(6):1064–1078, 2019b.
  * Klabunde et al. [2015] Anna Klabunde, F Willekens, Sabine Zinn, Matthias Leuchter, et al.  An agent-based decision model of migration, embedded in the life course-model description in odd+ d format.  _Max Planck Institute for Demographic Research_ , 2015.
  * Bergmann et al. [2014] Frank T Bergmann, Richard Adams, Stuart Moodie, Jonathan Cooper, Mihai Glont, Martin Golebiewski, Michael Hucka, Camille Laibe, Andrew K Miller, David P Nickerson, et al.  Combine archive and omex format: One file to share all information to reproduce a modeling project.  _BMC Bioinformatics_ , 15(1):1–9, 2014.
  * Ayllón et al. [2021] Daniel Ayllón, Steven F Railsback, Cara Gallagher, Jacqueline Augusiak, Hans Baveco, Uta Berger, Sandrine Charles, Romina Martin, Andreas Focks, Nika Galic, et al.  Keeping modelling notebooks with trace: Good for you and good for environmental research and management support.  _Environmental Modelling & Software_, 136:104932, 2021\.
  * Pimentel et al. [2017] Joao Felipe Pimentel, Leonardo Murta, Vanessa Braganholo, and Juliana Freire.  noworkflow: a tool for collecting, analyzing, and managing provenance from python scripts.  _Proceedings of the VLDB Endowment_ , 10(12), 2017.
  * Apel et al. [2013] Sven Apel, Don S. Batory, Christian Kästner, and Gunter Saake.  _Feature-Oriented Software Product Lines: Concepts and Implementation_.  Springer, 2013.  ISBN 978-3-642-37520-0.
  * Schobbens et al. [2006] Pierre-Yves Schobbens, Patrick Heymans, and Jean-Christophe Trigaux.  Feature diagrams: A survey and a formal semantics.  In _14th IEEE International Conference on Requirements Engineering (RE 2006), 11-15 September 2006, Minneapolis/St.Paul, Minnesota, USA_ , pages 136–145. IEEE Computer Society, 2006.  doi:10.1109/RE.2006.23.  URL https://doi.org/10.1109/RE.2006.23.
  * Schaefer et al. [2010] Ina Schaefer, Lorenzo Bettini, Viviana Bono, Ferruccio Damiani, and Nico Tanzarella.  Delta-Oriented Programming of Software Product Lines.  In Jan Bosch and Jaejoon Lee, editors, _Software Product Lines: Going Beyond (SPLC 2010)_ , volume 6287 of _LNCS_ , pages 77–91, 2010.  ISBN 978-3-642-15578-9.  doi:10.1007/978-3-642-15579-6_6.
  * Thüm et al. [2012] Thomas Thüm, Ina Schaefer, Martin Hentschel, and Sven Apel.  Family-based deductive verification of software product lines.  In Klaus Ostermann and Walter Binder, editors, _Generative Programming and Component Engineering, GPCE’12, Dresden, Germany_ , pages 11–20. ACM, 2012.
  * Ruscheinski et al. [2022] Andreas Ruscheinski, Pia Wilsdorf, Julius Zimmermann, Ursula van Rienen, and Adelinde M Uhrmacher.  An artefact-based workflow for finite element simulation studies.  _Simulation Modelling Practice and Theory_ , 116:102464, 2022.
  * Paul and Taylor [2002] Ray J Paul and Simon JE Taylor.  What use is model reuse: Is there a crook at the end of the rainbow?  In _Proceedings of the Winter Simulation Conference_ , volume 1, pages 648–652. IEEE, 2002.
  * Szabo and Teo [2007] Claudia Szabo and Yong Meng Teo.  On syntactic composability and model reuse.  In _First Asia International Conference on Modelling & Simulation (AMS’07)_, pages 230–237. IEEE, 2007.
  * Robinson et al. [2004] Stewart Robinson, Richard E Nance, Ray J Paul, Michael Pidd, and Simon JE Taylor.  Simulation model reuse: Definitions, benefits and obstacles.  _Simulation Modelling Practice and Theory_ , 12(7-8):479–494, 2004.
  * Janssen et al. [2020] Marco A Janssen, Calvin Pritchard, and Allen Lee.  On code sharing and model documentation of published individual and agent-based models.  _Environmental Modelling & Software_, 134:104873, 2020\.
  * Tolk and Muguira [2003] Andreas Tolk and James A Muguira.  The levels of conceptual interoperability model.  In _Proceedings of the 2003 Fall Simulation Interoperability Workshop_ , volume 7, pages 1–11. Citeseer, 2003.
  * Rohl and Uhrmacher [2008] Mathias Rohl and Adelinde M. Uhrmacher.  Definition and analysis of composition structures for discrete-event models.  In _2008 Winter Simulation Conference_ , pages 942–950, 2008.  doi:10.1109/WSC.2008.4736160.
  * Dalle [2006] Olivier Dalle.  OSA: An Open Component-based Architecture for Discrete-event Simulation.  In _Proceedings of the $20^{th}$ European Conference on Modeling and Simulation_, Prague, Czech Republic, 2006.
  * Wang et al. [2009] Wenguang Wang, Andreas Tolk, and Weiping Wang.  The levels of conceptual interoperability model: Applying systems engineering principles to m&s.  In _Proceedings of the 2009 Spring Simulation Multiconference_ , SpringSim ’09, San Diego, CA, USA, 2009. Society for Computer Simulation International.
  * Pidd [2002] Michael Pidd.  Simulation software and model reuse: A polemic.  In _Proceedings of the Winter Simulation Conference_ , volume 1, pages 772–775. IEEE, 2002.
  * Petty and Weisel [2019] Mikel D Petty and Eric W Weisel.  Model composition and reuse.  In _Model Engineering for Simulation_ , pages 57–85. Elsevier, 2019\.
  * Hussain et al. [2022] Mohammad Hussain, Nafiseh Masoudi, Gregory Mocko, and Chris Paredis.  Approaches for simulation model reuse in systems design—a review.  _SAE Technical Paper_ , (2022-01-0355), 2022.
  * Popper et al. [2020] Nikolas Popper, Melanie Zechmeister, Dominik Brunmeir, Claire Rippinger, Nadine Weibrecht, Christoph Urach, Martin Bicher, Günter Schneckenreither, and Andreas Rauber.  Synthetic reproduction and augmentation of covid-19 case reporting data by agent-based simulation.  _medRxiv_ , pages 2020–11, 2020.
  * Bicher et al. [2018] Martin Bicher, Christoph Urach, and Niki Popper.  Gepoc abm: A generic agent-based population model for austria.  In _2018 Winter Simulation Conference (WSC)_ , pages 2656–2667. IEEE, 2018.
  * Zhu et al. [2019] Feng Zhu, Yiping Yao, Jin Li, and Wenjie Tang.  Reusability and composability analysis for an agent-based hierarchical modelling and simulation framework.  _Simulation Modelling Practice and Theory_ , 90:81–97, 2019\.
  * Liu and Lee [2002] Jie Liu and Edward A. Lee.  A component-based approach to modeling and simulating mixed-signal and hybrid systems.  _ACM Transactions on Modeling and Computer Simulation_ , 12(4):343–368, 2002.  ISSN 1049-3301.  doi:10.1145/643120.643125.  URL https://doi.org/10.1145/643120.643125.
  * Bartholet et al. [2004] Robert G. Bartholet, David C. Brogan, Paul F. Reynolds, and Joseph C. Carnahan.  In Search of the Philosopher’s Stone: Simulation Composability Versus Component-Based Software Design.  In _Proceedings of the Fall Simulation Interoperability Workshop_ , Orlando, USA, 2004.
  * Himmelspach and Uhrmacher [2007] Jan Himmelspach and Adelinde M. Uhrmacher.  Plug’n simulate.  In _Proceedings of the 40th Annual Simulation Symposium_ , ANSS ’07, page 137–143, USA, 2007. IEEE Computer Society.  ISBN 0769528147.  doi:10.1109/ANSS.2007.34.  URL https://doi.org/10.1109/ANSS.2007.34.
  * Helms et al. [2015] Tobias Helms, Roland Ewald, Stefan Rybacki, and Adelinde M Uhrmacher.  Automatic runtime adaptation for component-based simulation algorithms.  _ACM Transactions on Modeling and Computer Simulation (TOMACS)_ , 26(1):1–24, 2015.
  * Grimm et al. [2006] Volker Grimm, Uta Berger, Finn Bastiansen, Sigrunn Eliassen, Vincent Ginot, Jarl Giske, John Goss-Custard, Tamara Grand, Simone K Heinz, Geir Huse, et al.  A standard protocol for describing individual-based and agent-based models.  _Ecological Modelling_ , 198(1-2):115–126, 2006\.
  * Novère et al. [2005] Nicolas Le Novère, Andrew Finney, Michael Hucka, Upinder S Bhalla, Fabien Campagne, Julio Collado-Vides, Edmund J Crampin, Matt Halstead, Edda Klipp, Pedro Mendes, et al.  Minimum information requested in the annotation of biochemical models (miriam).  _Nature Biotechnology_ , 23(12):1509–1515, 2005\.
  * Lloyd et al. [2004] Catherine M Lloyd, Matt DB Halstead, and Poul F Nielsen.  Cellml: its future, present and past.  _Progress in Biophysics and Molecular Biology_ , 85(2-3):433–450, 2004.
  * Hucka et al. [2003] Michael Hucka, Andrew Finney, Herbert M Sauro, Hamid Bolouri, John C Doyle, Hiroaki Kitano, Adam P Arkin, Benjamin J Bornstein, Dennis Bray, Athel Cornish-Bowden, et al.  The systems biology markup language (sbml): a medium for representation and exchange of biochemical network models.  _Bioinformatics_ , 19(4):524–531, 2003.
  * Smith et al. [2014] Lucian P Smith, Erik Butterworth, James B Bassingthwaighte, and Herbert M Sauro.  Sbml and cellml translation in antimony and jsim.  _Bioinformatics_ , 30(7):903–907, 2014.
  * Krause et al. [2010] Falko Krause, Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, Edda Klipp, and Wolfram Liebermeister.  Annotation and merging of sbml models with semantic sbml.  _Bioinformatics_ , 26(3):421–422, 2010.
  * Smith et al. [2021] Lucian P Smith, Frank T Bergmann, Alan Garny, Tomáš Helikar, Jonathan Karr, David Nickerson, Herbert Sauro, Dagmar Waltemath, and Matthias König.  The simulation experiment description markup language (sed-ml): Language specification for level 1 version 4.  _Journal of Integrative Bioinformatics_ , 18(3):20210021, 2021.
  * Silver et al. [2011] Gregory A Silver, John A Miller, Maria Hybinette, Gregory Baramidze, and William S York.  An ontology for discrete-event modeling and simulation.  _Simulation_ , 87(9):747–773, 2011.
  * Li et al. [2010] Chen Li, Marco Donizelli, Nicolas Rodriguez, Harish Dharuri, Lukas Endler, Vijayalakshmi Chelliah, Lu Li, Enuo He, Arnaud Henry, Melanie I Stefan, et al.  Biomodels database: An enhanced, curated and annotated resource for published quantitative kinetic models.  _BMC Systems Biology_ , 4(1):1–14, 2010.
  * ACM [2020] ACM.  Artifact review and badging, 2020.  URL https://www.acm.org/publications/policies/artifact-review-and-badging-current.  Accessed: 2023-08-07.
  * Mili et al. [1995] Hafedh Mili, Fatma Mili, and Ali Mili.  Reusing software: Issues and research directions.  _IEEE Transactions on Software Engineering_ , 21(6):528–562, 1995.
  * Mohagheghi and Conradi [2007] Parastoo Mohagheghi and Reidar Conradi.  Quality, productivity and economic benefits of software reuse: A review of industrial studies.  _Empirical Software Engineering_ , 12:471–516, 2007.
  * Müller et al. [2013] Birgit Müller, Friedrich Bohn, Gunnar Dreßler, Jürgen Groeneveld, Christian Klassert, Romina Martin, Maja Schlüter, Jule Schulze, Hanna Weise, and Nina Schwarz.  Describing human decisions in agent-based models–odd+ d, an extension of the odd protocol.  _Environmental Modelling & Software_, 48:37–48, 2013\.
  * Wilsdorf et al. [2023b] Pia Wilsdorf, Marian Zuska, Philipp Andelfinger, and Adelinde M. Uhrmacher.  Validation without data - formalizing stylized facts of time series.  In _Winter Simulation Conference (WSC 2023)_. IEEE Press, 2023b.  accepted.
  * Steiniger and Uhrmacher [2016] Alexander Steiniger and Adelinde M. Uhrmacher.  Intensional couplings in variable-structure models: An exploration based on multilevel-devs.  _ACM Transactions on Modeling and Computer Simulation_ , 26(2):9:1–9:27, 2016.  doi:10.1145/2818641.  URL https://doi.org/10.1145/2818641.
  * Elmqvist and Mattsson [1997] Hilding Elmqvist and Sven-Erik Mattsson.  Modelica-the next generation modeling language: An international design effort.  In _Proceedings of First World Congress of System Simulation_ , pages 1–3. Citeseer, 1997.
  * Randhawa et al. [2010] Ranjit Randhawa, Cliff Shaffer, and John Tyson.  Model composition for macromolecular regulatory networks.  _IEEE/ACM Transactions on Computational Biology and Bioinformatics_ , 7(2):278–287, 2010.  doi:10.1109/TCBB.2008.64.
  * Schulz et al. [2006] Marvin Schulz, Edda Klipp, Jannis Uhlendorf, and Wolfram Liebermeister.  Sbmlmerge, a system for combining biochemical network models.  _Genome Informatics_ , 17(1):62–71, 2006.
  * Davis et al. [2000] Paul K Davis, James Bigelow, and Jimmie McEver.  _Exploratory Analysis and a Case History of Multiresolution, Multiperspective Modeling_.  Number RP-925. Rand Corporation, 2000.
  * Pohl et al. [2005] Klaus Pohl, Günter Böckle, and Frank J. van der Linden.  _Software Product Line Engineering: Foundations, Principles and Techniques_.  Springer-Verlag, 2005.
  * Clements and Northrop [2001] Paul Clements and Linda Northrop.  _Software Product Lines: Practices & Patterns_.  Addison Wesley Longman, 2001.
  * Setyautami and Hähnle [2021] Maya Retno Ayu Setyautami and Reiner Hähnle.  An architectural pattern to realize multi software product lines in Java.  In Paul Grünbacher, Christoph Seidl, Deepak Dhungana, and Helena Lovasz-Bukvova, editors, _VaMoS’21: 15th Intl. Working Conf. on Variability Modelling of Software-Intensive Systems, Virtual Event_ , pages 9:1–9:9. ACM, 2021.  doi:10.1145/3442391.
  * Henkel et al. [2018] Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knüpfer, Wolfram Liebermeister, and Dagmar Waltemath.  Notions of similarity for systems biology models.  _Briefings in Bioinformatics_ , 19(1):77–88, 2018\.
  * Waltz and Buchanan [2009] David Waltz and Bruce G Buchanan.  Automating science.  _Science_ , 324(5923):43–44, 2009.
  * Maass and Storey [2021] Wolfgang Maass and Veda C. Storey.  Pairing conceptual modeling with machine learning.  _Data & Knowledge Engineering_, 134:101909, July 2021\.  ISSN 0169-023X.  doi:10.1016/j.datak.2021.101909.  URL https://www.sciencedirect.com/science/article/pii/S0169023X21000367.
  * Shuttleworth and Padilla [2022] David Shuttleworth and Jose Padilla.  From narratives to conceptual models via natural language processing.  In _2022 Winter Simulation Conference (WSC)_ , pages 2222–2233, 2022\.  doi:10.1109/WSC57314.2022.10015274.
  * Auer et al. [2007] Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives.  Dbpedia: A nucleus for a web of open data.  In Karl Aberer, Key-Sun Choi, Natasha Noy, Dean Allemang, Kyung-Il Lee, Lyndon Nixon, Jennifer Golbeck, Peter Mika, Diana Maynard, Riichiro Mizoguchi, Guus Schreiber, and Philippe Cudré-Mauroux, editors, _The Semantic Web_ , pages 722–735, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg.  ISBN 978-3-540-76298-0.
  * Gütebier et al. [2022] Lea Gütebier, Tim Bleimehl, Ron Henkel, Jamie Munro, Sebastian Müller, Axel Morgner, Jakob Laenge, Anke Pachauer, Alexander Erdl, Jens Weimar, Kirsten Walther Langendorf, Vincent Vialard, Thorsten Liebig, Martin Preusse, Dagmar Waltemath, and Alexander Jarasch.  CovidGraph: A Graph to Fight COVID-19.  _Bioinformatics_ , 38(20):4843–4845, 08 2022\.  ISSN 1367-4803.  doi:10.1093/bioinformatics/btac592.  URL https://doi.org/10.1093/bioinformatics/btac592.
  * Maneschijn et al. [2022] Dennis GJC Maneschijn, Rob H Bemthuis, Faiza Allah Bukhsh, and Maria-Eugenia Iacob.  A Methodology for Aligning Process Model Abstraction Levels and Stakeholder Needs.  In _ICEIS (1)_ , pages 137–147, 2022.
  * Brown et al. [2020] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei.  Language models are few-shot learners.  In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, _Advances in Neural Information Processing Systems_ , volume 33, pages 1877–1901. Curran Associates, Inc., 2020.  URL https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.
  * Jackson and Saenz [2022] Ilya Jackson and Maria Jesus Saenz.  From natural language to simulations: Applying gpt-3 codex to automate simulation modeling of logistics systems, 2022.  URL https://arxiv.org/abs/2202.12107.
  * Hansen and Hebart [2022] Hannes Hansen and Martin N. Hebart.  Semantic features of object concepts generated with gpt-3, 2022.  URL https://arxiv.org/abs/2202.03753.
  * Džeroski and Todorovski [2008] Sašo Džeroski and Ljupčo Todorovski.  Equation discovery for systems biology: Finding the structure and dynamics of biological networks from time course data.  _Current Opinion in Biotechnology_ , 19(4):360–368, 2008.  ISSN 0958-1669.  doi:https://doi.org/10.1016/j.copbio.2008.07.002.  URL https://www.sciencedirect.com/science/article/pii/S0958166908000839.  Protein technologies / Systems biology.
  * North et al. [2022] Joshua S. North, Christopher K. Wikle, and Erin M. Schliep.  A review of data-driven discovery for dynamic systems, 2022.
  * Angelis et al. [2023] Dimitrios Angelis, Filippos Sofos, and Theodoros E Karakasidis.  Artificial intelligence in physical sciences: Symbolic regression trends and perspectives.  _Archives of Computational Methods in Engineering_ , pages 1–21, 2023\.
  * Zhang et al. [2023] Michael Zhang, Samuel Kim, Peter Y. Lu, and Marin Soljačić.  Deep learning and symbolic regression for discovering parametric equations, 2023.
  * Lorig [2019] Fabian Lorig.  _Hypothesis-Driven Simulation Studies: Assistance for the Systematic Design and Conducting of Computer Simulation Experiments_.  Springer Vieweg, Wiesbaden, 2019.
  * Nenzi et al. [2022] Laura Nenzi, Ezio Bartocci, Luca Bortolussi, and Michele Loreti.  A logic for monitoring dynamic networks of spatially-distributed cyber-physical systems.  _Logical Methods in Computer Science_ , 18(1), 2022.  doi:10.46298/lmcs-18(1:4)2022.  URL https://doi.org/10.46298/lmcs-18(1:4)2022.
  * Wilsdorf et al. [2021] Pia Wilsdorf, Nadine Fischer, Fiete Haack, and Adelinde M. Uhrmacher.  Exploiting provenance and ontologies in supporting best practices for simulation experiments: A case study on sensitivity analysis.  In _2021 Winter Simulation Conference (WSC)_ , pages 1–12, 2021.  doi:10.1109/WSC52266.2021.9715362.
  * Leye et al. [2014] Stefan Leye, Roland Ewald, and Adelinde M. Uhrmacher.  Composing problem solvers for simulation experimentation: A case study on steady state estimation.  _PLOS ONE_ , 9(4):1–13, 04 2014.  doi:10.1371/journal.pone.0091948.  URL https://doi.org/10.1371/journal.pone.0091948.
  * Edeling et al. [2021] Wouter Edeling, Hamid Arabnejad, Robbie Sinclair, Diana Suleimenova, Krishnakumar Gopalakrishnan, Bartosz Bosak, Derek Groen, Imran Mahmood, Daan Crommelin, and Peter V. Coveney.  The impact of uncertainty on predictions of the CovidSim epidemiological code.  _Nature Computational Science_ , 1(2), February 2021.  ISSN 2662-8457.  doi:10.1038/s43588-021-00028-9.  URL https://www.nature.com/articles/s43588-021-00028-9.
  * Suleimenova et al. [2021] Diana Suleimenova, Hamid Arabnejad, Wouter Edeling, and Derek Groen.  Sensitivity-driven simulation development: A case study in forced migration.  _Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences_ , 379(2197):20200077, may 2021.  doi:10.1098/rsta.2020.0077.  Publisher: Royal Society.
  * Villaverde et al. [2018] Alejandro F Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, and Julio R Banga.  Benchmarking Optimization Methods for Parameter Estimation in Large Kinetic Models.  _Bioinformatics_ , 35(5):830–838, 08 2018.  ISSN 1367-4803.  doi:10.1093/bioinformatics/bty736.  URL https://doi.org/10.1093/bioinformatics/bty736.
  * Andelfinger [2023] Philipp Andelfinger.  Towards differentiable agent-based simulation.  _ACM Transactions on Modeling and Computer Simulation_ , 32(4), 2023.  ISSN 1049-3301.  doi:10.1145/3565810.  URL https://doi.org/10.1145/3565810.
  * Perrone et al. [2012] L Felipe Perrone, Christopher S Main, and Bryan C Ward.  Safe: Simulation automation framework for experiments.  In _Proceedings of the 2012 Winter Simulation Conference (WSC)_ , pages 1–12. IEEE, 2012.
  * König et al. [2022] Matthias König, Holger H Hoos, and Jan N van Rijn.  Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio.  _Machine Learning_ , pages 1–20, 2022.
  * Alemi et al. [2016] Alexander A. Alemi, François Chollet, Niklas Een, Geoffrey Irving, Christian Szegedy, and Josef Urban.  Deepmath - deep sequence models for premise selection.  In _Proceedings of the 30th International Conference on Neural Information Processing Systems_ , NIPS’16, page 2243–2251, Red Hook, NY, USA, 2016. Curran Associates Inc.  ISBN 9781510838819.
  * Zela et al. [2018] Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter.  Towards automated deep learning: Efficient joint neural architecture and hyperparameter search.  2018\.  doi:10.48550/ARXIV.1807.06906.  URL https://arxiv.org/abs/1807.06906.
  * Hunter et al. [2019] Susan R. Hunter, Eric A. Applegate, Viplove Arora, Bryan Chong, Kyle Cooper, Oscar Rincón-Guevara, and Carolina Vivas-Valencia.  An introduction to multiobjective simulation optimization.  _ACM Transactions on Modeling and Computer Simulation_ , 29(1), 2019.  ISSN 1049-3301.  doi:10.1145/3299872.  URL https://doi.org/10.1145/3299872.
  * Xiao et al. [2020] Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, and David Eckhoff.  Openablext: An automatic code generation framework for agent-based simulations on cpu-gpu-fpga heterogeneous platforms.  _Concurrency and Computation: Practice and Experience_ , 32(21):e5807, 2020.  doi:https://doi.org/10.1002/cpe.5807.
  * Noor and Hemmati [2015] Tanzeem Bin Noor and Hadi Hemmati.  A similarity-based approach for test case prioritization using historical failure data.  In _2015 IEEE 26th International Symposium on Software Reliability Engineering (ISSRE)_ , pages 58–68, 2015.  doi:10.1109/ISSRE.2015.7381799.
  * Bocciarelli et al. [2013] Paolo Bocciarelli, Andrea D’Ambrogio, Andrea Giglio, and Daniele Gianni.  A saas-based automated framework to build and execute distributed simulations from sysml models.  In _2013 Winter Simulations Conference (WSC)_ , pages 1371–1382, 2013\.  doi:10.1109/WSC.2013.6721523.
  * Feldkamp et al. [2020a] Niclas Feldkamp, Soeren Bergmann, and Steffen Strassburger.  Knowledge Discovery in Simulation Data.  _ACM Transactions on Modeling and Computer Simulation_ , 30(4):24:1–24:25, November 2020a.  ISSN 1049-3301.  doi:10.1145/3391299.  URL https://doi.org/10.1145/3391299.
  * Ali et al. [2019] Mohammed Ali, Ali Alqahtani, Mark W. Jones, and Xianghua Xie.  Clustering and classification for time series data in visual analytics: A survey.  _IEEE Access_ , 7:181314–181338, 2019.  doi:10.1109/ACCESS.2019.2958551.
  * Dambros et al. [2019] Jônathan W.V. Dambros, Jorge O. Trierweiler, and Marcelo Farenzena.  Oscillation detection in process industries – part i: Review of the detection methods.  _Journal of Process Control_ , 78:108–123, 2019.  ISSN 0959-1524.  doi:https://doi.org/10.1016/j.jprocont.2019.04.002.  URL https://www.sciencedirect.com/science/article/pii/S0959152419302239.
  * Blázquez-García et al. [2021] Ane Blázquez-García, Angel Conde, Usue Mori, and Jose A. Lozano.  A review on outlier/anomaly detection in time series data.  _ACM Computing Surveys_ , 54(3), apr 2021.  ISSN 0360-0300.  doi:10.1145/3444690.  URL https://doi.org/10.1145/3444690.
  * Feng and Staum [2017] Mingbin Feng and Jeremy Staum.  Green Simulation: Reusing the Output of Repeated Experiments.  _ACM Transactions on Modeling and Computer Simulation_ , 27(4):23:1–23:28, October 2017.  ISSN 1049-3301.  doi:10.1145/3129130.  URL https://doi.org/10.1145/3129130.
  * Blount et al. [2021] Tom Blount, Adriane Chapman, Michael Johnson, and Bertram Ludascher.  Observed vs. Possible Provenance (Research Track).  2021\.  URL https://www.usenix.org/conference/tapp2021/presentation/blount.
  * Allen et al. [2010] M David Allen, Len Seligman, Barbara Blaustein, and Adriane Chapman.  Provenance capture and use: A practical guide.  Technical report, Mitre CorpP McLean VA, 2010.
  * Gebhardt et al. [2022] Tom Gebhardt, Vasundra Touré, Dagmar Waltemath, Olaf Wolkenhauer, and Martin Scharm.  Exploring the evolution of biochemical models at the network level.  _PLOS ONE_ , 17(3):e0265735, March 2022.  ISSN 1932-6203.  doi:10.1371/journal.pone.0265735.  URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265735.  Publisher: Public Library of Science.
  * Berti et al. [2019] Alessandro Berti, Sebastiaan J Van Zelst, and Wil van der Aalst.  Process mining for python (pm4py): Bridging the gap between process and data science.  _arXiv preprint arXiv:1905.06169_ , 2019.
  * Altintas et al. [2006] Ilkay Altintas, Oscar Barney, and Efrat Jaeger-Frank.  Provenance collection support in the kepler scientific workflow system.  In _International Provenance and Annotation Workshop_ , pages 118–132. Springer, 2006.
  * Jeschke et al. [2011] Matthias Jeschke, Roland Ewald, and Adelinde M. Uhrmacher.  Exploring the performance of spatial stochastic simulation algorithms.  _Journal of Computational Physics_ , 230(7):2562–2574, 2011.  ISSN 0021-9991.  doi:https://doi.org/10.1016/j.jcp.2010.12.030.  URL https://www.sciencedirect.com/science/article/pii/S0021999110007047.
  * Kleijnen [1995] Jack P. C. Kleijnen.  Sensitivity analysis and optimization in simulation: Design of experiments and case studies.  In _Proceedings of the 27th Conference on Winter Simulation_ , WSC ’95, page 133–140, USA, 1995. IEEE Computer Society.  ISBN 0780330188.  doi:10.1145/224401.224454.  URL https://doi.org/10.1145/224401.224454.
  * 161 [2019] Collaborative Research Center SFB-TRR 161.  Quantitative methods for visual computing, 2019.  URL https://www.sfbtrr161.de/.  Accessed: 2023-01-27.
  * Bylinskii et al. [2022] Zoya Bylinskii, Laura Herman, Aaron Hertzmann, Stefanie Hutka, and Yile Zhang.  Towards better user studies in computer graphics and vision, 2022.  URL https://arxiv.org/abs/2206.11461.
  * Von Elm et al. [2007] Erik Von Elm, Douglas G Altman, Matthias Egger, Stuart J Pocock, Peter C Gøtzsche, and Jan P Vandenbroucke.  The strengthening the reporting of observational studies in epidemiology (strobe) statement: Guidelines for reporting observational studies.  _The Lancet_ , 370(9596):1453–1457, 2007.
  * Sadowski and Grabau [1999] Deborah A Sadowski and Mark R Grabau.  Tips for successful practice of simulation.  In _WSC’99. 1999 Winter Simulation Conference_ , volume 1, pages 60–66. IEEE, 1999.
  * Sturrock [2020] David T Sturrock.  Tested success tips for simulation project excellence.  In _2020 Winter Simulation Conference (WSC)_ , pages 1143–1151. IEEE, 2020.
  * Sturrock [2015] David T Sturrock.  Tutorial: Tips for successful practice of simulation.  In _2015 Winter Simulation Conference (WSC)_ , pages 1756–1764. IEEE, 2015.
  * Baldwin et al. [2004] Lynne P Baldwin, Tillal Eldabi, and Ray J Paul.  Simulation in healthcare management: A soft approach (mapiu).  _Simulation, Modelling, Practice and Theory_ , 12(7-8):541–557, 2004.
  * Blomkamp [2018] Emma Blomkamp.  The promise of co-design for public policy.  _Australian journal of public administration_ , 77(4):729–743, 2018.
  * Orton et al. [2011] Lois Orton, Ffion Lloyd-Williams, David Taylor-Robinson, Martin O’Flaherty, and Simon Capewell.  The use of research evidence in public health decision making processes: Systematic review.  _PloS One_ , 6(7):e21704, 2011.
  * Smith and Stewart [2015] Katherine E Smith and Ellen Stewart.  ‘black magic’ and ‘gold dust’: the epistemic and political uses of evidence tools in public health policy making.  _Evidence & Policy_, 11(3):415–437, 2015.
  * Oliver et al. [2014] Kathryn Oliver, Simon Innvar, Theo Lorenc, Jenny Woodman, and James Thomas.  A systematic review of barriers to and facilitators of the use of evidence by policymakers.  _BMC Health Services Research_ , 14:1–12, 2014.
  * Taylor-Robinson et al. [2008] David Taylor-Robinson, Beth Milton, Efion Lloyd-Williams, Martin O’Flaherty, and Simon Capewell.  Policy-makers’ attitudes to decision support models for coronary heart disease: a qualitative study.  _Journal of Health Services Research & Policy_, 13(4):209–2014, 2008.
  * Burns et al. [2003] Terry W Burns, D John O’Connor, and Susan M Stocklmayer.  Science communication: A contemporary definition.  _Public Understanding of Science_ , 12(2):183–202, 2003.
  * Kappel and Holmen [2019] Klemens Kappel and Sebastian Jon Holmen.  Why science communication, and does it work? a taxonomy of science communication aims and a survey of the empirical evidence.  _Frontiers in Communication_ , 4:55, 2019.
  * Guenther and Joubert [2017] Lars Guenther and Marina Joubert.  Science communication as a field of research: Identifying trends, challenges and gaps by analysing research papers.  _Journal of Science Communication_ , 16:A02, 2017.  doi:https://doi.org/10.22323/2.16020202.
  * Guo et al. [2021] Yue Guo, Wei Qiu, Yizhong Wang, and Trevor Cohen.  Automated lay language summarization of biomedical scientific reviews.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , pages 160–168, 2021.  doi:https://doi.org/10.1609/aaai.v35i1.16089.
  * Chen and Pu [2004] Li Chen and Pearl Pu.  Survey of preference elicitation methods.  Technical Report IC/2004/67, Ecole Politechnique Federale de Lausanne (EPFL), 2004.
  * Berger [2013] James O Berger.  _Statistical Decision Theory and Bayesian Analysis_.  Springer Science & Business Media, 2013.
  * Best et al. [2020] Nicky Best, Nigel Dallow, and Timothy Montague.  Prior elicitation.  _Bayesian Methods in Pharmaceutical Research_ , pages 87–109, 2020\.
  * Wallenius et al. [2008] Jyrki Wallenius, James S Dyer, Peter C Fishburn, Ralph E Steuer, Stanley Zionts, and Kalyanmoy Deb.  Multiple criteria decision making, multiattribute utility theory: Recent accomplishments and what lies ahead.  _Management Science_ , 54(7):1336–1349, 2008\.
  * Knowles [2006] Joshua Knowles.  Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems.  _IEEE Transactions on Evolutionary Computation_ , 10(1):50–66, 2006.
  * Daulton et al. [2021] Samuel Daulton, Maximilian Balandat, and Eytan Bakshy.  Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement.  _Advances in Neural Information Processing Systems_ , 34:2187–2200, 2021.
  * Astudillo and Frazier [2020] Raul Astudillo and Peter Frazier.  Multi-attribute bayesian optimization with interactive preference learning.  In _International Conference on Artificial Intelligence and Statistics_ , pages 4496–4507. PMLR, 2020.
  * Lin et al. [2022] Zhiyuan Jerry Lin, Raul Astudillo, Peter Frazier, and Eytan Bakshy.  Preference exploration for efficient bayesian optimization with multiple outcomes.  In _International Conference on Artificial Intelligence and Statistics_ , pages 4235–4258. PMLR, 2022.
  * González et al. [2017] Javier González, Zhenwen Dai, Andreas Damianou, and Neil D Lawrence.  Preferential bayesian optimization.  In _International Conference on Machine Learning_ , pages 1282–1291. PMLR, 2017.
  * Vernon-Bido et al. [2015] Daniele Vernon-Bido, Andrew Collins, and John Sokolowski.  Effective visualization in modeling & simulation.  In _Proceedings of the 48th Annual Simulation Symposium_ , ANSS ’15, page 33–40, San Diego, CA, USA, 2015. Society for Computer Simulation International.
  * Lane [2008] David C. Lane.  The emergence and use of diagramming in system dynamics: a critical account.  _Systems Research and Behavioral Science_ , 25(1):3–23, 2008.  doi:https://doi.org/10.1002/sres.826.
  * Siebers and Klügl [2017] Peer-Olaf Siebers and Franziska Klügl.  _What Software Engineering Has to Offer to Agent-Based Social Simulation_ , pages 81–117.  Springer International Publishing, Cham, 2017.  doi:10.1007/978-3-319-66948-9_6.
  * Tominski et al. [2009] Christian Tominski, James Abello, and Heidrun Schumann.  Cgv—an interactive graph visualization system.  _Computers & Graphics_, 33(6):660–678, 2009\.
  * Schulz [2011] Hans-Jorg Schulz.  Treevis.net: A tree visualization reference.  _IEEE Computer Graphics and Applications_ , 31(6):11–15, 2011.
  * St-Aubin et al. [2023] Bruno St-Aubin, Gabriel Wainer, and Fernando Loor.  A survey of visualization capabilities for simulation environments.  In _2023 Annual Modeling and Simulation Conference (ANNSIM)_ , pages 13–24, 2023.
  * Unger and Schumann [2009] Andrea Unger and Heidrun Schumann.  Visual support for the understanding of simulation processes.  In _2009 IEEE Pacific Visualization Symposium_ , pages 57–64, 2009\.  doi:10.1109/PACIFICVIS.2009.4906838.
  * Eichner et al. [2014] Christian Eichner, Arne Bittig, Heidrun Schumann, and Christian Tominski.  Analyzing simulations of biochemical systems with feature-based visual analytics.  _Computers & Graphics_, 38:18–26, 2014.  ISSN 0097-8493.  doi:https://doi.org/10.1016/j.cag.2013.09.001.
  * Feldkamp et al. [2020b] Niclas Feldkamp, Soeren Bergmann, and Steffen Strassburger.  Knowledge discovery in simulation data.  _ACM Transactions on Modeling and Computer Simulation_ , 30(4), nov 2020b.  ISSN 1049-3301.  doi:10.1145/3391299.
  * Matković et al. [2018] Krešimir Matković, Denis Gračanin, and Helwig Hauser.  Visual analytics for simulation ensembles.  In _2018 Winter Simulation Conference (WSC)_ , pages 321–335, 2018\.  doi:10.1109/WSC.2018.8632312.
  * Hakanen et al. [2022] Jussi Hakanen, Sanjin Radoš, Giovanni Misitano, Bhupinder S. Saini, Kaisa Miettinen, and Krešimir Matković.  Interactivized: Visual interaction for better decisions with interactive multiobjective optimization.  _IEEE Access_ , 10:33661–33678, 2022.  doi:10.1109/ACCESS.2022.3161465.
  * Andrienko et al. [2018] Natalia Andrienko, Tim Lammarsch, Gennady Andrienko, Georg Fuchs, Daniel Keim, Silvia Miksch, and Andrea Rind.  Viewing visual analytics as model building.  In _Computer Graphics Forum_ , volume 37, pages 275–299. Wiley Online Library, 2018.
  * Sondag et al. [2022] Max Sondag, Cagatay Turkay, Kai Xu, Louise Matthews, Sibylle Mohr, and Daniel Archambault.  Visual analytics of contact tracing policy simulations during an emergency response.  In _Computer Graphics Forum_ , volume 41, pages 29–41. Wiley Online Library, 2022.
  * Afzal et al. [2020] Shehzad Afzal, Sohaib Ghani, Hank C Jenkins-Smith, David S Ebert, Markus Hadwiger, and Ibrahim Hoteit.  A visual analytics based decision making environment for covid-19 modeling and visualization.  In _2020 IEEE Visualization Conference (VIS)_ , pages 86–90. IEEE, 2020.
  * Vennix [1999] Jac A. M. Vennix.  Group model-building: Tackling messy problems.  _System Dynamics Review_ , 15(4):379–401, 1999\.  doi:https://doi.org/10.1002/(SICI)1099-1727(199924)15:4<379::AID-SDR179>3.0.CO;2-E.
  * Ramanath and Gilbert [2004] Ana Maria Ramanath and Nigel Gilbert.  The design of participatory agent-based social simulations.  _Journal of Artificial Societies and Social Simulation_ , 7(4):1, 2004.  URL https://www.jasss.org/7/4/1.html.
  * Voinov et al. [2018] Alexey Voinov, Karen Jenni, Steven Gray, Nagesh Kolagani, Pierre D. Glynn, Pierre Bommel, Christina Prell, Moira Zellner, Michael Paolisso, Rebecca Jordan, Eleanor Sterling, Laura Schmitt Olabisi, Philippe J. Giabbanelli, Zhanli Sun, Christophe Le Page, Sondoss Elsawah, Todd K. BenDor, Klaus Hubacek, Bethany K. Laursen, Antonie Jetter, Laura Basco-Carrera, Alison Singer, Laura Young, Jessica Brunacini, and Alex Smajgl.  Tools and methods in participatory modeling: Selecting the right tool for the job.  _Environmental Modelling & Software_, 109:232–255, 2018\.  ISSN 1364-8152.  doi:https://doi.org/10.1016/j.envsoft.2018.08.028.
  * Barreteau et al. [2017] Olivier Barreteau, Pieter Bots, Katherine Daniell, Michel Etienne, Pascal Perez, Cécile Barnaud, Didier Bazile, Nicolas Becu, Jean-Christophe Castella, William’s Daré, and Guy Trebuil.  _Participatory Approaches, in Simulating Social Complexity: A Handbook_ , pages 253–292.  Springer International Publishing, 2017.  doi:10.1007/978-3-319-66948-9_12.  URL https://doi.org/10.1007/978-3-319-66948-9_12.
  * Barnaud et al. [2008] Cécile Barnaud, François Bousquet, and Guy Trebuil.  Multi-agent simulations to explore rules for rural credit in a highland farming community of northern thailand.  _Ecological Economics_ , 66(4):615–627, 2008\.  doi:https://doi.org/10.1016/j.ecolecon.2007.10.022.
  * Sahraoui et al. [2021] Yohan Sahraoui, Charles De Godoy Leski, Marie-Lise Benot, Frédéric Revers, Denis Salles, Inge van Halder, Marie Barneix, and Laure Carassou.  Integrating ecological networks modelling in a participatory approach for assessing impacts of planning scenarios on landscape connectivity.  _Landscape and Urban Planning_ , 209:104039, 2021.  doi:https://doi.org/10.1016/j.landurbplan.2021.104039.
  * van Bruggen et al. [2019] Anna van Bruggen, Ior Nikolic, and Jan Kwakkel.  Modeling with stakeholders for transformative change.  _Sustainability_ , 11(3):825, 2019.  doi:https://doi.org/10.3390/su11030825.
  * Barreteau and Others [2003] Olivier Barreteau and Others.  Our companion modelling approach.  _Journal of Artificial Societies and Social Simulation_ , 6(2):1, 2003.  URL https://jasss.soc.surrey.ac.uk/6/2/1.html.
  * Will et al. [2021] Meike Will, Gunnar Dressler, David Kreuer, Hans-Hermann Thulke, Adrienne Grêt-Regamey, and Birgit Müller.  How to make socio-environmental modelling more useful to support policy and management?  _People and Nature_ , 3(3):560–572, 2021.  doi:https://doi.org/10.1002/pan3.10207.
  * Milani et al. [2021] Alessandra Maciel Paz Milani, Lucas Angelo Loges, Fernando Vieira Paulovich, and Isabel Harb Manssour.  Prava: Preprocessing profiling approach for visual analytics.  _Information Visualization_ , 20(2-3):101–122, 2021.
  * Bouchet et al. [2022] Louis Bouchet, Martin C Thoms, and Melissa Parsons.  Using causal loop diagrams to conceptualize groundwater as a social-ecological system.  _Frontiers in Environmental Science_ , 10:836206, 2022.
  * Wilsdorf et al. [2019] Pia Wilsdorf, Maria E. Pierce, Jane Hillston, and Adelinde M. Uhrmacher.  Round-based super-individuals - balancing speed and accuracy.  In _Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation_ , SIGSIM-PADS ’19, page 95–98, New York, NY, USA, 2019. Association for Computing Machinery.  ISBN 9781450367233.  doi:10.1145/3316480.3322894.  URL https://doi.org/10.1145/3316480.3322894.
  * Keim et al. [2008] Daniel A. Keim, Gennady L. Andrienko, Jean-Daniel Fekete, Carsten Görg, Jörn Kohlhammer, and Guy Melançon.  Visual analytics: Definition, process, and challenges.  In Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, and Chris North, editors, _Information Visualization - Human-Centered Issues and Perspectives_ , volume 4950 of _Lecture Notes in Computer Science_ , pages 154–175. Springer, 2008.  doi:10.1007/978-3-540-70956-5_7.  URL https://doi.org/10.1007/978-3-540-70956-5_7.