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Cited by 57Multi-Agent Systems and NegotiationSimulation Techniques and ApplicationsScientific Computing and Data Management

ODD+2D: An ODD Based Protocol for Mapping Data to Empirical ABMs

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Laatabi, Ahmed, Marilleau, Nicolas, Nguyen-Huu, Tri, Hbid, Hassan, Ait Babram, Mohamed·2018·Journal of Artificial Societies and Social Simulation
DOI 10.18564/jasss.3646
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Abstract

The quantity of data and processes used in modeling projects has been dramatically increasing in recent years due to the progress in computation capability and to the popularity of new approaches such as open data.Modelers face an increasing difficulty in analyzing and modeling complex systems that consist of many heterogeneous entities.Adapting existing models is relevant to avoid dealing with the complexity of writing and studying a new model from scratch.ODD (Overview, Design concepts, Details) protocol has emerged as a solution to document Agent-Based Models (ABMs).It appears to be a convenient solution to address significant problems such as comprehension, replication, and dissemination.However, it lacks a standard that formalizes the use of data in empirical models.This paper tackles this issue by proposing a set of rules that outline the use of empirical data inside an ABM.We call this new protocol ODD+ D (ODD+Decision + Data).ODD+ D integrates a mapping diagram called DAMap (Data to Agent Mapping).This mapping model formalizes how data are processed and mapped to agent-based models.In this paper, we focus on the architecture of ODD+ D, and we illustrate it with a residential mobility model in Marrakesh.

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  • The MZs are grouped by geographical zones of tourist interest (ZITs), a characterization developed in the POT (Zoning Land Plan) of Santa Marta of 2020.

    “The MZ are grouped by geographical zones of tourist interest (ZITs), a characterization developed in the POT (Zoning Land Plan) of Santa Marta of 2020 [40]”

    From Agent-Based Model Applied for the Study of Overtourism in an Urban Context (2025) · cites this paper as [40]

    The Overview and Data structure sections describe six districts in Marrakesh for a residential-mobility model. Searches for Santa Marta, ZITs, tourist-interest zones, and the 2020 POT found no passage addressing the claimed grouping of MZs. (LLM, conf=0.75)

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Research works printed but not linked: 35

  1. Amouroux, E. ( ). KIMONO, a descriptive agent-based modelling method for the exploration of complex sys- tems: an application to epidemiology. Thesis, Université Pierre et Marie Curie -Paris VI
  2. Anh, B. T. M., Stinckwich, S., Ziane, M., Roche, B. & Vinh, H. T. ( ). Kendrick: A domain specific language and platform for mathematical epidemiological modelling. In Computing & Communication Technologies- Research, Innovation, and Vision for the Future (RIVF), IEEE RIVF International Conference on, (pp. -).
  3. IEEE
  4. Augusiak, J., Van den Brink, P. J. & Grimm, V. ( ). Merging validation and evaluation of ecological models to 'evaludation': a review of terminology and a practical approach. Ecological Modelling, , -
  5. Balbi, S., Giupponi, C., Perez, P. & Alberti, M. ( ). A spatial agent-based model for assessing strategies of adap- tation to climate and tourism demand changes in an alpine tourism destination. Environmental Modelling & So ware, , - Barreteau, O. & Smajgl, A. ( ). Designing empirical agent-based models: An issue of matching data, technical requirements and stakeholders expectations. In Empirical Agent-Based Modelling-Challenges and Solutions, (pp. -). Springer
  6. Barreteau, O. et al. ( ). Our companion modelling approach. Journal of Artificial Societies and Social Simu- lation, ( ),
  7. Bauer, B., Müller, J. P. & Odell, J. ( ). Agent UML: A formalism for specifying multiagent so ware systems. International Journal of So ware Engineering and Knowledge Engineering, ( ), - Bersini, H. ( ). UML for ABM. Journal of Artificial Societies and Social Simulation, ( ),
  8. Bin, O., Kruse, J. B. & Landry, C. E. ( ). Flood hazards, insurance rates, and amenities: Evidence from the coastal housing market. Journal of Risk and Insurance, ( ), - JASSS, ( ) , http://jasss.soc.surrey.ac.uk/ / / .html Doi: . /jasss.
  9. Boero, R. & Squazzoni, F. ( ). Does empirical embeddedness matter? methodological issues on agent-based models for analytical social science. Journal of Artificial Societies and Social Simulation, ( ),
  10. Donkin, E., Dennis, P., Ustalakov, A., Warren, J. & Clare, A. ( ). Replicating complex agent based models, a formidable task. Environmental Modelling & So ware, , - Filatova, T. ( ). Empirical agent-based land market: Integrating adaptive economic behavior in urban land- use models. Computers, Environment and Urban Systems, , - Geller, A. ( ). Building empirical multiagent models from first principles when fieldwork is difficult or im- possible. In Empirical Agent-Based Modelling-Challenges and Solutions, (pp. -). Springer
  11. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S. K., Huse, G. et al. ( ). A standard protocol for describing individual-based and agent-based models. Ecolog- ical Modelling, ( ), - Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J. & Railsback, S. F. ( ). The odd protocol: a review and first update. Ecological modelling, ( ), - Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T. & DeAngelis, D. L. ( ). Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science, ( ), - Groeneveld, J., Klabunde, A., O'Brien, M. L. & Grow, A. ( ). How to describe agent-based models in population studies? In Agent-Based Modelling in Population Studies, (pp. - ). Springer
  12. Hassan, S., Antunes, L., Pavon, J. & Gilbert, G. ( ). Stepping on earth: A roadmap for data-driven agent-based modelling. In Proceedings of the th Conference of the European Social Simulation Association (ESSA ).
  13. Hassan, S., Pavón, J., Antunes, L. & Gilbert, N. ( ). Injecting data into agent-based simulation. In Simulating Interacting Agents and Social Phenomena, (pp. -). Springer
  14. Hinkelmann, F., Murrugarra, D., Jarrah, A. S. & Laubenbacher, R. ( ). A mathematical framework for agent based models of complex biological networks. Bulletin of Mathematical Biology, ( ), -
  15. Holm, S., Lemm, R., Thees, O. & Hilty, L. M. ( ). Enhancing agent-based models with discrete choice experi- ments. Journal of Artificial Societies and Social Simulation, ( ),
  16. Janssen, M. A. ( ). The practice of archiving model code of agent-based models. Journal of Artificial Societies and Social Simulation, ( ),
  17. Jones, N. A., Perez, P., Measham, T. G., Kelly, G. J., d'Aquino, P., Daniell, K. A., Dray, A. & Ferrand, N. ( ). Evalu- ating participatory modeling: developing a framework for cross-case analysis. Environmental Management, ( ), - Jordan, R., Birkin, M. & Evans, A. ( ). Agent-based modelling of residential mobility, housing choice and regeneration. In Agent-based models of geographical systems, (pp. -). Springer
  18. Klabunde, A. & Willekens, F. ( ). Decision-making in agent-based models of migration: state of the art and challenges. European Journal of Population, ( ), -
  19. Laatabi, A., Babram, M. A. & Hbid, M. L. ( ). Generating and mapping a synthetic population of marrakesh. In Third World Conference on Complex Systems (WCCS), (pp. -). doi:10.1109/ICoCS.2015.7483306 · DOI
  20. Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H. & Babram, M. A. ( ). Formalizing Data to Agent Model Mapping Using MOF: Application to a Model of Residential Mobility in Marrakesh, (pp. -). Cham: Springer International Publishing. doi:10.1007/978-3-319-39883-9\_9 · DOI
  21. Lammoglia, A. ( ). Évolution spatio-temporelle d'une desserte de transport flexible simulée en sma. enjeux méthodologiques et perspectives. Cybergeo: European Journal of Geography
  22. Livet, P., Muller, J.-P., Phan, D. & Sanders, L. ( ). Ontology, a mediator for agent-based modeling in social science. Journal of Artificial Societies and Social Simulation, ( ),
  23. Nguyen, T. K., Marilleau, N., Ho, T. V. & El Fallah, A. ( ). New protocol supporting collaborative simulation. In Proceedings of the Second Symposium on Information and Communication Technology, (pp. -). ACM
  24. O'Sullivan, D., Evans, T., Manson, S., Metcalf, S., Ligmann-Zielinska, A. & Bone, C. ( ). Strategic directions for agent-based modeling: avoiding the yaawn syndrome. Journal of Land Use Science, ( ), -
  25. Polhill, J. G., Parker, D., Brown, D. & Grimm, V. ( ). Using the odd protocol for describing three agent-based social simulation models of land-use change. Journal of Artificial Societies and Social Simulation, ( ),
  26. Richiardi, M., Leombruni, R., Saam, N. & Sonnessa, M. ( ). A common protocol for agent-based social simu- lation. Journal of Artificial Societies and Social Simulation, ( ),
  27. Sansores, C., Pavón, J. & Gómez-Sanz, J. ( ). Visual modeling for complex agent-based simulation systems. In International Workshop on Multi-Agent Systems and Agent-Based Simulation, (pp. -). Springer
  28. Sargent, R. G. ( ). Verification and validation of simulation models. In Proceedings of the Winter Simula- tion Conference, (pp. -). winter simulation conference
  29. Siebers, P.-O. & Aickelin, U. ( ). Introduction to multi-agent simulation. Arxiv. Available at https://arxiv. org/abs/0803.3905
  30. Smajgl, A. & Barreteau, O. ( ). Empiricism and agent-based modelling. In Empirical Agent-Based Modelling- Challenges and Solutions, (pp. -). Springer
  31. Taillandier, P., Vo, D.-A., Amouroux, E. & Drogoul, A. ( ). Gama: a simulation platform that integrates ge- ographical information data, agent-based modeling and multi-scale control. In Principles and Practice of Multi-Agent Systems, (pp. - ). Springer
  32. Truong, M. T., Amblard, F. & Gaudou, B. ( ). Combination framework of bi solution & multi-agent platform (cfbm) for multi-agent based simulations. In eme Conference francophone sur le Gestion et l'Extraction de Connaissances: Journée Atelier aide à la Décision à tous les Etages (AIDE@ EGC ), (pp. -)
  33. Wang, M. ( ). Data assimilation for agent-based simulation of smart environment. Dissertation, Georgia State University. http://scholarworks.gsu.edu/cs_diss/91
  34. Williams, N. E., O'Brien, M. L. & Yao, X. ( ). Using survey data for agent-based modeling: design and chal- lenges in a model of armed conflict and population change. In Agent-Based Modelling in Population Studies, (pp. -). Springer
  35. Zambonelli, F., Jennings, N. R. & Wooldridge, M. ( ). Developing multiagent systems: The gaia methodology. ACM Transactions on So ware Engineering and Methodology (TOSEM), ( ), - Zinas, B. Z. & Jusan, M. ( ). Choice behaviour of housing attributes: Theory and measurement. Asian Journal of Environment-Behaviour Studies

Cited by

Sorted by citations

Authors
Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M.
Affiliations
  • Cadi Ayyad University (MA)
  • Unité de Modélisation Mathématique et Informatique des Systèmes Complexes (FR)
ORCID
Venue
Journal of Artificial Societies and Social Simulation
Publisher
Journal of Artificial Societies and Social Simulation
Published
2018
Volume
21
Issue
2
ISSN
1460-7425 (ISSN-L)
DOI
10.18564/jasss.3646
OpenAlex
W2794603090
Cited by
57
References
0
Citations by year
2026: 3 · 2025: 6 · 2024: 5 · 2023: 9 · 2022: 14 · 2021: 10 · 2020: 5 · 2019: 3 · 2018: 2
FWCI
2.80
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Artificial Societies and Social Simulation (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Greater South Information System
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords
ODD protocol, agent-based modeling, model documentation, model replication, model dissemination, residential mobility, Marrakech, open data

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