Abstract
In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://eurai.org/activities/ACAI_courses (accessed Jun 2022).
- 2.
https://simassocc.org/ (accessed Jun 2022).
- 3.
https://ccl.northwestern.edu/netlogo/download.shtml (accessed Jun 2022).
- 4.
https://ccl.northwestern.edu/netlogo/docs/ (accessed Jun 2022).
- 5.
https://github.com/lvanhee/COVID-sim (accessed Jun 2022).
References
Anderson, L.W., et al.: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition, vol. 5, no. 1. Longman, White Plains (2001)
Banks, J.: Introduction to simulation. In: 2000 Proceedings of the Winter Simulation Conference, vol. 1, pp. 9–16. IEEE (2000)
Biggs, J., Tang, C.: Teaching for Quality Learning at University. McGraw-hill education (UK), New York (2011)
Commons, M.L., Richards, F.A.: Four postformal stages. In: Demick, J., Andreoletti, C., (eds) Handbook of adult development. The Springer Series in Adult Development and Aging, pp. 199–219. Springer, Boston (2002). https://doi.org/10.1007/978-1-4615-0617-1_11
Davidsson, P.: Agent based social simulation: a computer science view. J. Artif. Soc. Soc. Simul. 5(1) (2002)
Dignum, F.: Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis. Springer, Cham (2021)
Gilbert, N., Troitzsch, K.: Simulation for the Social Scientist. McGraw-Hill Education (UK), New York (2005)
Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. Roy. Soc. London. Ser. A, Containing Pap. Math. Phys. Charact. 115(772), 700–721 (1927)
Law, A.M.: Simulation Modeling and Analysis, vol. 5. McGraw-Hill, New York (2014)
Lorig, F., Johansson, E., Davidsson, P.: Agent-based social simulation of the covid-19 pandemic: a systematic review. JASSS: J. Artif. Soc. Soc. Simul. 24(3) (2021)
Maslow, A., Lewis, K.: Maslow’s hierarchy of needs. Salenger Incorporated 14(17), 987–990 (1987)
McClelland, D.C.: Human Motivation. CUP Archive (1987)
Schwartz, S.H.: An overview of the schwartz theory of basic values. Online Readings Psychol. Cult. 2(1), 2307–2919 (2012)
Sokolowski, J.A., Banks, C.M.: Principles of Modeling and Simulation: A Multidisciplinary Approach. John Wiley, Hoboken (2011)
Squazzoni, F., Jager, W., Edmonds, B.: Social simulation in the social sciences: a brief overview. Soc. Sci. Comput. Rev. 32(3), 279–294 (2014)
Acknowledgement
This tutorial was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society (WASP-HS) research program funded by the Marianne and Marcus Wallenberg Foundation, the Marcus and Amalia Wallenberg Foundation, and the Knut and Alice Wallenberg Foundation (no. 570080103). The simulations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lorig, F., Vanhée, L., Dignum, F. (2023). Agent-Based Social Simulation for Policy Making. In: Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C. (eds) Human-Centered Artificial Intelligence. ACAI 2021. Lecture Notes in Computer Science(), vol 13500. Springer, Cham. https://doi.org/10.1007/978-3-031-24349-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-24349-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24348-6
Online ISBN: 978-3-031-24349-3
eBook Packages: Computer ScienceComputer Science (R0)