Abstract
Agent-Based Modelling has been used for social simulation because of the several benefits it entails. Social models are often constructed by inter-disciplinary teams that include subject-matter experts with no programming skills. These experts are typically involved in the creation of the conceptual model, but not the verification or validation of the simulation model. The Overview, Design concepts, and Details (ODD) protocol has emerged as a way of presenting a model at a high level of abstraction and as an effort towards improving the reproducibility of Agent-Based Models (ABMs) but it is typically written after a model has been completed. This paper reverses the process and provides non-programming experts with a user-friendly and extensible tool called ODD2ABM for creating and altering models on their own. This is done by formalizing ODD using concepts abstracted from the NetLogo language, enabling users to generate NetLogo code from an ODD description automatically. We verified the ODD2ABM tool with three existing NetLogo models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abar, S., Theodoropoulos, G.K., Lemarinier, P., ÒHare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017). https://doi.org/10.1016/j.cosrev.2017.03.001
Garro, A., Parisi, F., Russo, W.: A process based on the model-driven architecture to enable the definition of platform-independent simulation models. In: Pina, N., Kacprzyk, J., Filipe, J. (eds.) Simulation and Modeling Methodologies, Technologies and Applications. Advances in Intelligent Systems and Computing, vol. 197, pp. 113–129. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-34336-0_8
Garro, A., Russo, W.: Easyabms: a domain-expert oriented methodology for agent-based modeling and simulation. Simul. Model. Pract. Theory 18(10), 1453–1467 (2010). https://doi.org/10.1016/j.simpat.2010.04.004
Ghorbani, A., Bots, P., Dignum, V., Dijkema, G.: MAIA: a framework for developing agent-based social simulations. J. Artif. Soc. Soc. Simul. 16(2), 9 (2013). https://doi.org/10.18564/jasss.2166
Grimm, V., Polhill, G., Touza, J.: Documenting social simulation models: the ODD protocol as a standard. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity. Understanding Complex Systems, pp. 117–133. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-540-93813-2_7
Hamill, L.: Agent-based modelling: the next 15 years. J. Artif. Soc. Soc. Simul. 13(4), 11 (2010). https://doi.org/10.18564/jasss.1640
Hassan, S., Fuentes-Fernández, R., Galán, J.M., López-Paredes, A., Pavón, J.: Reducing the modeling gap: on the use of metamodels in agent-based simulation. In: 6th Conference of the European Social Simulation Association (ESSA 2009), pp. 1–13 (2009)
Janssen, M.A., Alessa, L.N., Barton, M., Bergin, S., Lee, A.: Towards a community framework for agent-based modelling. J. Artif. Soc. Soc. Simul. 11(2), 6 (2008). http://jasss.soc.surrey.ac.uk/11/2/6.html
JetBrains: MPS Meta Programming System. https://www.jetbrains.com/mps/
Klügl, F., Davidsson, P.: AMASON: Abstract Meta-model for Agent-based SimulatiON. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) MATES 2013. LNCS (LNAI), vol. 8076, pp. 101–114. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40776-5_11
Kubera, Y., Mathieu, P., Picault, S.: Interaction-oriented agent simulations: from theory to implementation. In: Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence, pp. 383–387. IOS Press, Amsterdam (2008). http://dl.acm.org/citation.cfm?id=1567281.1567367
Netlogo dictionary. https://ccl.northwestern.edu/netlogo/docs/dictionary.html
Santos, F., Nunes, I., Bazzan, A.L.: Model-driven agent-based simulation development: a modeling language and empirical evaluation in the adaptive traffic signal control domain. Simul. Model. Pract. Theory 83, 162–187 (2018). https://doi.org/10.1016/j.simpat.2017.11.006
Sargent, R.G.: Verification and validation of simulation models. J. Simul. 7(1), 12–24 (2013). https://doi.org/10.1057/jos.2012.20
Wilensky, U.: Netlogo wolf sheep predation model. Report, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1997). http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation
Wilensky, U.: Netlogo home page. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999). http://ccl.northwestern.edu/netlogo/
Wilensky, U., Rand, W.: The Components of Agent-Based Modeling, 1st edn, pp. 203–282. The MIT Press, Cambridge (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xanthopoulou, T.D., Prinz, A., Shults, F.L. (2019). Generating Executable Code from High-Level Social or Socio-Ecological Model Descriptions. In: Fonseca i Casas, P., Sancho, MR., Sherratt, E. (eds) System Analysis and Modeling. Languages, Methods, and Tools for Industry 4.0. SAM 2019. Lecture Notes in Computer Science(), vol 11753. Springer, Cham. https://doi.org/10.1007/978-3-030-30690-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-30690-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30689-2
Online ISBN: 978-3-030-30690-8
eBook Packages: Computer ScienceComputer Science (R0)