Building, composing and experimenting complex spatial models with the GAMA platform

Abstract

The agent-based modeling approach is now used in many domains such as geography, ecology, or economy, and more generally to study (spatially explicit) socio-environmental systems where the heterogeneity of the actors and the numerous feedback loops between them requires a modular and incremental approach to modeling. One major reason of this success, besides this conceptual facility, can be found in the support provided by the development of increasingly powerful software platforms, which now allow modelers without a strong background in computer science to easily and quickly develop their own models. Another trend observed in the latest years is the development of much more descriptive and detailed models able not only to better represent complex systems, but also answer more intricate questions. In that respect, if all agent-based modeling platforms support the design of small to mid-size models, i.e. models with little heterogeneity between agents, simple representation of the environment, simple agent decision-making processes, etc., very few are adapted to the design of large-scale models. GAMA is one of the latter. It has been designed with the aim of supporting the writing (and composing) of fairly complex models, with a strong support of the spatial dimension, while guaranteeing non-computer scientists an easy access to high-level, otherwise complex, operations. This paper presents GAMA 1.8, the latest revision to date of the platform, with a focus on its modeling language and its capabilities to manage the spatial dimension of models. The capabilities of GAMA are illustrated by the presentation of applications that take advantage of its new features.

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    https://github.com/gama-platform/gama

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    https://groups.google.com/forum/#!forum/gama-platform

References

  1. 1.

    Adam C, Gaudou B (2016) BDI agents in social simulations: a survey. Knowl Eng Rev 31:207–238

    Article  Google Scholar 

  2. 2.

    Adam C, Gaudou B (2017) Modelling human Behaviours in disasters from interviews: application to Melbourne bushfires. Journal of Artificial Societies and Social Simulation 20

  3. 3.

    Adam C, Taillandier P, Dugdale J (2017) Comparing agent architectures in social simulation: Bdi agents versus finite-state machines. In: Hawaii International Conference on System Sciences (HICSS)

  4. 4.

    Allan RJ (2009) Survey of agent based modelling and simulation tools. Tech rep

  5. 5.

    Alonso, L., Zhang, Y. R., Grignard, A., Noyman, A., Sakai, Y., ElKatsha, M., ... & Larson, K.: Cityscope: a data-driven interactive simulation tool for urban design. Use case volpe. In: International Conference on Complex Systems, pp. 253–261, Springer (2018)

  6. 6.

    Amblard, F., Bouadjio-Boulic, A., Guti’errez, C.S., Gaudou, B.: Which models are used in social simulation to generate social networks? a review of 17 years of publications in jasss. In: Winter Simulation Conference (WSC), 2015, pp. 4021–4032. IEEE (2015)

  7. 7.

    Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S.: A descriptive framework for temporal data visualizations based on generalized space-time cubes. In: Computer Graphics Forum, vol. 36, pp. 36–61. Wiley Online Library (2017)

  8. 8.

    Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K., Axhausen, K.: Matsim-t: Architecture and simulation times. Multi-Agent Systems for Traffic and Transportation Engineering pp. 57–78 (2009)

  9. 9.

    Becu, N., Amalric, M., Anselme, B., Beck, E., Bertin, X., Delay, E., Long, N., Manson, C., Nicolas, M., Pignon-Mussaud, C., Rousseaux, F.: Participatory simulation of coastal flooding: building social learning on prevention measures with decision-makers. In: 8th International Congress on Environmental Modelling and Software, pp. 1–14. Toulouse, France (2016)

  10. 10.

    Bell AR, Robinson DT, Malik A, Dewal S (2015) Modular abm development for improved dissemination and training. Environ Model Softw 73:189–200

    Article  Google Scholar 

  11. 11.

    Bellifemine, F., Poggi, A., Rimassa, G.: Jade: a fipa compliant agent development environment. In: Proceedings of the fifth international conference on Autonomous agents, pp. 216–217. ACM (2001)

  12. 12.

    Bhamidipati, S., Van der Lei, T., Herder, P.: A layered approach to model interconnected infrastructure and its significance for asset management. European Journal of Transport and Infrastructure Research (EJTIR), 16 (1), 2016 (2016)

  13. 13.

    Bourgais M, Taillandier P, Vercouter L (2016) An agent architecture coupling cognition and emotions for simulation of complex systems. In, Social Simulation Conference

    Google Scholar 

  14. 14.

    Bourgais, M., Taillandier, P., Vercouter, L.: Enhancing the behavior of agents in social simulations with emotions and social relations. In: The 18th Workshop on Multi-agent based Simulation-MABS 2017 (2017)

  15. 15.

    Bourgais M, Taillandier P, Vercouter L, Adam C (2018) Emotion modeling in social simulation: a survey. Journal of Artificial Societies and Social Simulation 21

  16. 16.

    Bratman, M.: Intentions, plans, and practical reason. (1987)

    Google Scholar 

  17. 17.

    Caillou, P., Gaudou, B., Grignard, A., Truong, C.Q., Taillandier, P.: A simple-to-use bdi architecture for agent-based modeling and simulation. In: Advances in Social Simulation 2015, pp. 15–28. Springer (2017)

  18. 18.

    Chapotat, W., Houssou, L., Bouadjio Boulic, A., Maestripieri, N., Lerigoleur, E., Gaudou, B., Saqalli, M.: An agent-based model of the amazonian forest colonisation and oil exploitation: the oriente study case (poster). In: S. sauvage, J.M. Sanchez Perez, A.E. Rizzoli (eds.) International Environmental Modelling and Software Society (iEMSs), Toulouse, France, vol. 5, pp. 1335–1335. International Environmental Modelling & Software Society, http://www.iemss.org (2016)

  19. 19.

    Chapuis, K., Taillandier, P., Renaud, M., Drogoul, A.: Gen*: a generic toolkit to generate spatially explicit synthetic populations. International Journal of Geographical Information Science pp. 1–17 (2018)

  20. 20.

    Chen B, Cheng HH (2010) A review of the applications of agent technology in traffic and transportation systems. IEEE Trans Intell Transp Syst 11(2):485–497

    Article  Google Scholar 

  21. 21.

    Cottineau, C., Perret, J., Reuillon, R., Rey-Coyrehourcq, S., Vallée, J.: An agent-based model to investigate the effects of social segregation around the clock on social disparities in dietary behaviour. In: CIST2018- Representing territories (2018)

  22. 22.

    Crooks, A., Malleson, N., Wise, S., Heppenstall, A.: big data, agents and the city. Big Data for Regional Science (2017)

  23. 23.

    Crooks, A.T., Castle, C.J.: The integration of agent-based modelling and geographical information for geospatial simulation. In: Agent-based models of geographical systems, pp. 219–251. Springer (2012)

  24. 24.

    Czura, G., Taillandier, P., Tranouez, P., Daudé, E.: Mosaiic: City-level agent-based ´ traffic simulation adapted to emergency situations. In: Proceedings of the International Conference on Social Modeling and Simulation, plus Econophysics Colloquium 2014, pp. 265–274. Springer (2015)

  25. 25.

    Dorin A, Geard N (2014) The practice of agent-based model visualization. Artificial life 20(2):271–289

    Article  Google Scholar 

  26. 26.

    Drogoul A, Huynh NQ, Truong QC (2016) Coupling environmental, social and economic models to understand land-use change dynamics in the mekong delta. Frontiers in Environmental Science 4:19

    Article  Google Scholar 

  27. 27.

    Drogoul, A., Vanbergue, D., Meurisse, T.: Multi-agent based simulation: Where are the agents? In: International Workshop on Multi-Agent Systems and Agent-Based Simulation, pp. 1–15. Springer (2002)

  28. 28.

    Edmonds, B., Moss, S.: From kiss to kids–an anti-simplisticmodelling approach. In: International workshop on multi-agent systems and agent-based simulation, pp. 130–144. Springer (2004)

  29. 29.

    Fosset P, Banos A, Beck E, Chardonnel S, Lang C, Marilleau N, Piombini A, Leysens T, Conesa A, Andre-Poyaud I, Thevenin T (2016) Exploring intra-urban accessibility and impacts of pollution policies with an agent-based simulation platform: Gamirod. Systems 4(1):5

    Article  Google Scholar 

  30. 30.

    Gasmi, N., Grignard, A., Drogoul, A., Gaudou, B., Taillandier, P., Tessier, O., An, V.D.: Reproducing and exploring past events using agent-based geo-historical models. In: International Workshop on Multi-Agent Systems and Agent-Based Simulation, pp. 151–163. Springer (2014)

  31. 31.

    Gaudou, B.: Toward complex models of complex systems - one step further in the art of agent-based modelling. Habilitation `a diriger des recherches, Universit Toulouse 1 Capitole (2016)

  32. 32.

    Gaudou, B., Marilleau, N., Ho, T.V.: Toward a methodology of collaborative modeling and simulation of complex systems. In: Intelligent Networking, Collaborative Systems and Applications, pp. 27–53. Springer (2010)

  33. 33.

    Grignard, A., Alonso, L., Taillandier, P., Gaudou, B., Nguyen-Huu, T., Gruel, W., Larson, K. : The Impact of New Mobility Modes on a City: A Generic Approach Using ABM. In: Unifying Themes in Complex Systems IX. ICCS 2018. Springer proceedings in complexity, pp; 272-280, Springer (2018)

  34. 34.

    Grignard, A., Macià, N., Alonso Pastor, L., Noyman, A., Zhang, Y., & Larson, K.: Cityscope andorra: a multilevel interactive and tangible agent-based visualization. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (2018)

  35. 35.

    Grignard, A., Drogoul, A.: Agent-based visualization: a real-time visualization tool applied both to data and simulation outputs (2017)

    Google Scholar 

  36. 36.

    Grignard, A., Drogoul, A., Zucker, J.D.: Online analysis and visualization of agent based models. In: International Conference on Computational Science and Its Applications, pp. 662–672. Springer (2013)

  37. 37.

    Grignard, A., Fantino, G., Lauer, J.W., Verpeaux, A., Drogoul, A.: Agent-based visualization: A simulation tool for the analysis of river morphosedimentary adjustments. In: International Workshop on Multi-Agent Systems and Agent-Based Simulation, pp. 109–120. Springer (2015)

  38. 38.

    Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., & Drogoul, A.: GAMA 1.6: Advancing the art of complex agent-based modeling and simulation. In: International Conference on Principles and Practice of Multi-Agent Systems, pp. 117–131. Springer (2013)

  39. 39.

    Harabor DD, Grastien A et al (2014) Improving jump point search. In, ICAPS

    Google Scholar 

  40. 40.

    Heppenstall AJ (2011) Crooks. A.T., See, L.M., Batty, M.: Agent-based models of geographical systems. Springer Science & Business Media

    Google Scholar 

  41. 41.

    Huynh, Q.N.: Comodels, engineering dynamic compositions of coupled models to support the simulation of complex systems. Ph.D. thesis, Paris 6 (2016)

  42. 42.

    Jakob, M., Moler, Z.: Modular framework for simulation modelling of interaction-rich transport systems. In: Proceedings of the 16th IEEE Intelligent Transportation Systems Conference (ITSC 2013) (2013)

  43. 43.

    Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of SUMO - simulation of urban MObility. International Journal On Advances in Systems and Measurements 5(3&4):128–138

    Google Scholar 

  44. 44.

    Kravari K, Bassiliades N (2015) A survey of agent platforms. Journal of Artificial Societies and Social Simulation 18(1):11

    Article  Google Scholar 

  45. 45.

    Laatabi A, Marilleau N, Nguyen-Huu T, Hbid H, Babram MA et al (2018) Odd+ 2d: An odd based protocol for mapping data to empirical abms. Journal of Artificial Societies and Social Simulation 21(2):1–9

    Article  Google Scholar 

  46. 46.

    Le, V.M., Chevaleyre, Y., Vinh, H.T., Zucker, J.D.: Hybrid of linear programming and genetic algorithm for optimizing agent-based simulation. application to optimization of sign placement for tsunami evacuation. In: Computing & Communication TechnologiesResearch, Innovation, and Vision for the Future (RIVF), 2015 IEEE RIVF International Conference on, pp. 138–143. IEEE (2015)

  47. 47.

    Le Page, C., Bousquet, F., Bakam, I., Bah, A., Baron, C.: Cormas: A multiagent simulation toolkit to model natural and social dynamics at multiple scales. In: Proceedings of Workshop” The ecology of scales”, Wageningen (The Netherlands) (2000)

  48. 48.

    Maneerat S, Daudé E (2016) A spatial agent-based simulation model of the dengue vector aedes aegypti to explore its population dynamics in urban areas. Ecol Model 333:66–78

    Article  Google Scholar 

  49. 49.

    Minar, N., Burkhart, R., Langton, C., Askenazi, M., et al.: The swarm simulation system: a toolkit for building multi-agent simulations (1996)

    Google Scholar 

  50. 50.

    Nguyen, T.K., Gaudou, B., Ho, T.V., Marilleau, N.: Application of pams collaboration platform to simulation-based researches in soil science: The case of the micro-organism project. In: Computing and Communication Technologies, 2009. RIVF’09. International Conference on, pp. 1–8. IEEE (2009)

  51. 51.

    North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P (2013) Complex adaptive systems modeling with repast simphony. Complex adaptive systems modeling 1(1):3

    Article  Google Scholar 

  52. 52.

    Philippon D, Choisy M, Drogoul A, Gaudou B, Marilleau N, Taillandier P, Truong CQ (2016) Exploring trade and health policies influence on dengue spread with an agent-based model. In: international workshop on multi-agent-based simulation (MABS). Singapore

  53. 53.

    Pijls W, Post H (2009) Yet another bidirectional algorithm for shortest paths. Tech. rep, Econometric Institute Research Papers

    Google Scholar 

  54. 54.

    Pires B, Crooks AT (2017) Modeling the emergence of riots: a geosimulation approach. Comput Environ Urban Syst 61:66–80

    Article  Google Scholar 

  55. 55.

    Pumain, D., Reuillon, R.: An incremental multi-modelling method to simulate systems of cities evolution. In: Urban Dynamics and Simulation Models, pp. 57–80. Springer (2017)

  56. 56.

    Repenning, A.: Making programming more conversational. In: Visual Languages and Human-Centric Computing (VL/HCC), 2011 IEEE Symposium on, pp. 191–194. IEEE (2011)

  57. 57.

    Resnick, M.: Starlogo: An environment for decentralized modeling and decentralized thinking. In: Conference companion on Human factors in computing systems, pp. 11–12. ACM (1996)

  58. 58.

    Reuillon R, Leclaire L, Rey-Coyrehourcq S (2013) Openmole, a workflow engine specifically tailored for the distributed exploration of simulation models. Futur Gener Comput Syst 29(8):1981–1990

    Article  Google Scholar 

  59. 59.

    Rose J, Ligtenberg A, Van der Spek S (2014) Simulating pedestrians through the innercity: An agent-based approach. UAB Press

  60. 60.

    Ta, X.H., Longin, D., Gaudou, B., Ho, T.V.: Impact of group on the evacuation process - theory and simulation. In: L. De Raedt, Y. Deville, M. Bui, T.T.D. Lin (eds.) Symposium on Information and Communication Technology (SoICT), Hue, Vietnam, pp. 350–357. ACM DL (2015)

  61. 61.

    Taillandier, F., Adam, C.: Games ready to use: A serious game for teaching natural risk management. Simulation & Gaming p. 1046878118770217 (2018)

  62. 62.

    Taillandier F, Taillandier P, Hamzaoui F, Breysse D (2016) A new agent-based model to manage construction project risks–application to the crossroad of bab el karmadine at Tlemcen. Eur J Environ Civ Eng 20(10):1197–1213

    Article  Google Scholar 

  63. 63.

    Taillandier F, Taillandier P, Tepeli E, Breysse D, Mehdizadeh R, Khartabil F (2015) A multi-agent model to manage risks in construction project (smacc). Autom Constr 58:1–18

    Article  Google Scholar 

  64. 64.

    Taillandier, P., Bourgais, M., Caillou, P., Adam, C., Gaudou, B.: A bdi agent architecture for the gama modeling and simulation platform. In: MABS 2016 Multi-Agent-Based Simulation (2016)

  65. 65.

    Therond, O., Sibertin-Blanc, C., Lardy, R., Gaudou, B., Balestrat, M., Hong, Y., Louail, T., Panzoli, D., Sanchez-Perez, J.M., Sauvage, S., et al.: Integrated modelling of socialecological systems: The maelia high-resolution multi-agent platform to deal with water scarcity problems. In: 7th International Environmental Modelling and Software Society (2014)

  66. 66.

    Tisue, S., Wilensky, U.: Netlogo: A simple environment for modeling complexity. In: International conference on complex systems, vol. 21, pp. 16–21. Boston, MA (2004)

  67. 67.

    Truong, Q.C., Taillandier, P., Gaudou, B., Vo, M.Q., Nguyen, T.H., Drogoul, A.: Exploring agent architectures for farmer behavior in land-use change. a case study in coastal area of the vietnamese mekong delta. In: International Workshop on Multi-Agent Systems and Agent-Based Simulation, pp. 146–158. Springer (2015)

  68. 68.

    Woolridge M, Wooldridge MJ (2001) Introduction to multiagent systems. John Wiley & Sons, Inc., New York, NY, USA

    Google Scholar 

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Appendix

Appendix

GAMA, which is under a GNU General Public License, can be downloaded from the GAMA Website: http://www.gama-platform.org. It is available in 32 and 64 bits for Windows, OS-X and Linux. GAMA requires a Java Virtual Machine (1.8) to run, but the website proposes a version of GAMA with an embedded JVM. A library of more than 300 models is provided with GAMA: this library contains models presenting how to use the different features of GAMA (data importation, agent movement, 3D display, etc.), classic toy models (ant foraging, boids, Schelling, SugarScape…) and tutorials. The Website of GAMA provides a complete documentation on the platform. It provides as well a series of tutorials to learn the bases of GAML. At last, an online user groupFootnote 2 is available to ask questions about the platform.

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Taillandier, P., Gaudou, B., Grignard, A. et al. Building, composing and experimenting complex spatial models with the GAMA platform. Geoinformatica 23, 299–322 (2019). https://doi.org/10.1007/s10707-018-00339-6

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Keywords

  • Agent-based modeling
  • Spatial simulation
  • Platform
  • Modeling language