Abstract
Agent-based simulation is increasingly being used to model social phenomena involving large numbers of agents. However, existing agent-based simulation platforms severely limit the kinds of the social phenomena that can modeled, as they do not support large scale simulations involving agents with complex behaviors. In this paper, we present a scalable agent-based simulation framework that supports modeling of complex social phenomena. The framework integrates a new simulation platform that exploits distributed computer architectures, with an extension of a multi-agent programming technology that allows development of complex deliberative agents. To show the scalability of our framework, we briefly describe its application to the development of a model of the spread of COVID-19 involving complex deliberative agents in the US state of Virginia.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Source code for PanSim is available at https://github.com/parantapa/pansim, and that for Sim-2APL is available at https://bitbucket.org/goldenagents/sim2apl.
- 2.
Here we use the terms simple and complex contagions in their literal sense and not specifically in the sense developed and popularized in [13].
- 3.
- 4.
- 5.
To ensure that the socio-psychological module processes and PanSim processes don’t compete for CPU resources we use MPI implementation specific configuration to make PanSim processes sleep during the execution of the socio-psychological module. This configuration trades of some performance for ease of programming.
- 6.
We experimented with using Metis and ParMetis [24] for this partitioning. However, we found that our simple approach was much faster and produced adequately good partitions.
- 7.
Since agents can execute multiple plans during one deliberation cycle, this approach does not restrict the agent’s number of actions per time step.
References
Adam, C., Gaudou, B.: BDI agents in social simulations: a survey. Knowl. Eng. Rev. 31(n\(^{\circ }\) 3), pp. 207–238 (2016). https://doi.org/10.1017/S0269888916000096, https://hal.archives-ouvertes.fr/hal-01484960
Adiga, A., et al.: Generating a synthetic population of the United States. Technical report NDSSL 15–009, Network Dynamics and Simulation Science Laboratory (2015)
Barrett, C., et al.: Planning and response in the aftermath of a large crisis: an agent-based informatics framework. In: Pasupathy, R., Kim, S.H., Tolk, A., Hill, R., Kuhl, M.E. (eds.) Proceedings of the 2013 Winter Simulation Conference, Piscataway, NJ, USA, pp. 1515–1526. IEEE Press (2013)
Barrett, C.L., Bisset, K.R., Eubank, S.G., Feng, X., Marathe, M.V.: Episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, pp. 37:1–37:12 (2008)
Bhatele, A., et al.: Massively parallel simulations of spread of infectious diseases over realistic social networks. In: Proceedings of the IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID. IEEE (2017)
Bisset, K., Chen, J., Feng, X., Vullikanti, A., Marathe, M.: EpiFast: a fast algorithm for large-scale realistic epidemic simulations on distributed memory systems. In: Proceedings of the 23rd International Conference on Supercomputing (2009)
Bordini, R.H., Dastani, M., Dix, J., Fallah-Seghrouchni, A.E. (eds.): Multi-Agent Programming: Languages, Platforms and Applications, Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 15. Springer, Boston (2005). https://doi.org/10.1007/b137449
Bordini, R.H., Dastani, M., Dix, J., Fallah-Seghrouchni, A.E. (eds.): Multi-Agent Programming: Languages, Tools and Applications. Springer, USA (2009). https://doi.org/10.1007/978-0-387-89299-3
Bordini, R.H., Hübner, J.F.: Agent-based simulation using BDI programming in Jason. In: Multi-Agent Systems: Simulation and Applications, pp. 451–471 (2009)
Broersen, J., Dastani, M., Hulstijn, J., Huang, Z., van der Torre, L.: The BOID architecture: conflicts between beliefs, obligations, intentions and desires. In: Proceedings of the 5th International Conference on Autonomous Agents, pp. 9–16 (2001)
Caballero, A., BotÃa, J., Gómez-Skarmeta, A.: Using cognitive agents in social simulations. Eng. Appl. Artif. Intell. 24(7), 1098–1109 (2011)
Centers for Disease Control and Prevention: COVID-19 pandemic planning scenarios. https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html. Accessed 06 Oct 2020
Centola, D., Macy, M.: Complex contagions and the weakness of long ties. Am. J. Sociol. 113(3), 702–734 (2007)
Citycovid. https://www.anl.gov/dis/citycovid-about-the-model
Dastani, M., Hulstijn, J., van der Torre, L.: How to decide what to do? Eur. J. Oper. Res. 160(3), 762–784 (2005). Decision Analysis and Artificial Intelligence
Dastani, M., Testerink, B.: Design patterns for multi-agent programming. Int. J. Agent-Oriented Softw. Eng. 5(2/3), 167–202 (2016)
de Mooij, J., Dell’Anna, D., Bhattacharya, P., Dastani, M., Logan, B., Swarup, S.: Quantifying the effects of norms on COVID-19 cases using an agent-based simulation. In: Proceedings of the The 22nd International Workshop on Multi-Agent-Based Simulation (MABS) (2021)
Dignum, F., Dignum, V., Jonker, C.M.: Towards agents for policy making. In: David, N., Sichman, J.S. (eds.) Multi-Agent-Based Simulation IX, pp. 141–153. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01991-3_11
Gaudou, B., et al.: Comokit: a modeling kit to understand, analyze, and compare the impacts of mitigation policies against the COVID-19 epidemic at the scale of a city. Front. Public Health 8, 587 (2020). https://doi.org/10.3389/fpubh.2020.563247, https://www.frontiersin.org/article/10.3389/fpubh.2020.563247
Gerbessiotis, A.V., Valiant, L.G.: Direct bulk-synchronous parallel algorithms. J. Parallel Distrib. Comput. 22(2), 251–267 (1994)
Gilbert, N.: When does social simulation need cognitive models? In: Sun, R. (ed.) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 428–432. Cambridge University Press, Cambridge (2006)
Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist. Open University Press (2006)
Glanz, K., Rimer, B.K., Viswanath, K.: Health Behavior and Health Education: Theory, Research, and Practice. Wiley, Hoboken (2008)
Karypis, G., Schloegel, K., Kumar, V.: Parmetis. Parallel graph partitioning and sparse matrix ordering library. Version 2 (2003)
Macal, C.M., Collier, N.T., Ozik, J., Tatara, E.R., Murphy, J.T.: CHISIM: an agent-based simulation model of social interactions in a large urban area. In: 2018 Winter Simulation Conference (WSC), pp. 810–820. IEEE (2018)
Michel, F.: The IRM4S model: the influence/reaction principle for multi-agent based simulation. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (2007)
Morvan, G., Veremme, A., Dupont, D.: IRM4MLS: the influence reaction model for multi-level simulation. In: Bosse, T., Geller, A., Jonker, C.M. (eds.) MABS 2010. LNCS (LNAI), vol. 6532, pp. 16–27. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18345-4_2
Müller, J.P., Pecchiari, P.: A model for systems of situated autonomous agents: an application to automated deduction (1996)
Müller, S.A., Balmer, M., Neumann, A., Nagel, K.: Mobility traces and spreading of COVID-19. Technical report, Technische Universität Berlin (2020). https://doi.org/10.14279/depositonce-9835
Navarro, L., Flacher, F., Corruble, V.: Dynamic level of detail for large scale agent-based urban simulations. In: Tumer, K., Yolum, P., Sonenberg, L., Stone, P. (eds.) Proceedings of 10th International Conference on Autonomous Agents and Multiagent Systems, Taipei, Taiwan, 2–6 May 2011, pp. 701–708 (2011)
Satsuma, J., Willox, R., Ramani, A., Grammaticos, B., Carstea, A.: Extending the SIR epidemic model. Phys. A: Stat. Mech. Appl. 336(3–4), 369–375 (2004)
Silverman, B.G., Johns, M., Cornwell, J., O’Brien, K.: Human behavior models for agents in simulators and games: part I: enabling science with PMFserv. Presence: Teleoper. Virtual Environ. 15(2), 139–162 (2006)
Singh, D., Padgham, L., Logan, B.: Integrating BDI agents with agent-based simulation platforms. Auton. Agents Multi-Agent Syst. 30(6), 1050–1071 (2016). https://doi.org/10.1007/s10458-016-9332-x
Taillandier, P., et al.: Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica 23(2), 299–322 (2019)
Acknowledgments
Parantapa Bhattacharya and Samarth Swarup were supported in part by NSF Expeditions in Computing Grant CCF-1918656 and DTRA subcontract/ARA S-D00189-15-TO-01-UVA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Bhattacharya, P., de Mooij, A.J., Dell’Anna, D., Dastani, M., Logan, B., Swarup, S. (2022). PanSim + Sim-2APL: A Framework for Large-Scale Distributed Simulation with Complex Agents. In: Alechina, N., Baldoni, M., Logan, B. (eds) Engineering Multi-Agent Systems. EMAS 2021. Lecture Notes in Computer Science(), vol 13190. Springer, Cham. https://doi.org/10.1007/978-3-030-97457-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-97457-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97456-5
Online ISBN: 978-3-030-97457-2
eBook Packages: Computer ScienceComputer Science (R0)