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Aspects of Modeling Human Behavior in Agent-Based Social Simulation – What Can We Learn from the COVID-19 Pandemic?

  • Conference paper
  • First Online:
Multi-Agent-Based Simulation XXIV (MABS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14558))

  • 19 Accesses

Abstract

Proper modeling of human behavior is crucial when developing agent-based models to investigate the effects of policies, such as the potential consequences of interventions during a pandemic. It is, however, unclear, how sophisticated behavior models need to be for being considered suitable to support policy making. The goal of this paper is to identify recommendations on how human behavior should be modeled in Agent-Based Social Simulation (ABSS) as well as to investigate to what extent these recommendations are actually followed by models explicitly developed for policy making. By analyzing the literature, we identify seven relevant aspects of human behavior for consideration in ABSS. Based on these aspects, we review how human behavior is modeled in ABSS of COVID-19 interventions, in order to investigate the capabilities and limitations of these models to provide policy advice. We focus on models that were published within six months of the start of the pandemic as this is when policy makers needed the support provided by ABSS the most. It was found that most models did not include the majority of the identified relevant aspects, in particular norm compliance, agent deliberation, and interventions’ affective effects on individuals. We argue that ABSS models need a higher level of descriptiveness than what is present in most of the studied early COVID-19 models to support policymaker decisions.

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References

  1. Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017)

    Article  Google Scholar 

  2. Aleta, A., et al.: Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19. Nat. Human Behav. 4(9), 964–971 (2020)

    Google Scholar 

  3. An, L.: Modeling human decisions in coupled human and natural systems: review of agent-based models. Ecol. Model. 229, 25–36 (2012)

    Article  Google Scholar 

  4. Andrews, J.L., Foulkes, L., Blakemore, S.J.: Peer influence in adolescence: public-health implications for COVID-19. Trends Cogn. Sci. 24(8), 585–587 (2020)

    Article  Google Scholar 

  5. Azzimonti, M., Fogli, A., Perri, F., Ponder, M.: Pandemic control in ECON-EPI networks. Technical report, National Bureau of Economic Research (2020)

    Google Scholar 

  6. Bahl, R., et al.: Modeling COVID-19 spread in small colleges. PLoS ONE 16(8), e0255654 (2021)

    Google Scholar 

  7. Balke, T., Gilbert, N.: How do agents make decisions? A survey. J. Artif. Soc. Soc. Simul. 17(4), 13 (2014)

    Article  Google Scholar 

  8. Bicher, M., Rippinger, C., Urach, C., Brunmeir, D., Siebert, U., Popper, N.: Evaluation of contact-tracing policies against the spread of SARS-CoV-2 in Austria: an agent-based simulation. Med. Decis. Making 41(8), 1017–1032 (2021)

    Article  Google Scholar 

  9. Brotherhood, L., Kircher, P., Santos, C., Tertilt, M.: An economic model of the COVID-19 epidemic: the importance of testing and age-specific policies. CESifo working paper (2020)

    Google Scholar 

  10. Castelfranchi, C., Dignum, F., Jonker, C.M., Treur, J.: Deliberative normative agents: principles and architecture. In: Jennings, N.R., Lespérance, Y. (eds.) ATAL 1999. LNCS (LNAI), vol. 1757, pp. 364–378. Springer, Heidelberg (2000). https://doi.org/10.1007/10719619_27

    Chapter  Google Scholar 

  11. Chang, S.L., Harding, N., Zachreson, C., Cliff, O.M., Prokopenko, M.: Modelling transmission and control of the COVID-19 pandemic in Australia. Nat. Commun. 11(1), 1–13 (2020)

    Article  Google Scholar 

  12. Chen, C., Frey, C.B., Presidente, G.: Culture and contagion: individualism and compliance with COVID-19 policy. J. Econ. Behav. Organ. 190, 191–200 (2021)

    Article  Google Scholar 

  13. Cullen, W., Gulati, G., Kelly, B.D.: Mental health in the COVID-19 pandemic. QJM: Int. J. Med. 113(5), 311–312 (2020)

    Google Scholar 

  14. Dignum, F., et al.: Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation. Mind. Mach. 30(2), 177–194 (2020)

    Article  Google Scholar 

  15. Epstein, J.M.: Modelling to contain pandemics. Nature 460(7256), 687–687 (2009)

    Article  Google Scholar 

  16. Ferguson, N.M., et al.: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team (2020)

    Google Scholar 

  17. Gasparek, M., Racko, M., Dubovsky, M.: A stochastic, individual-based model for the evaluation of the impact of non-pharmacological interventions on COVID-19 transmission in Slovakia. MedRxiv (2020)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Gilbert, N., Chattoe-Brown, E., Watts, C., Robertson, D.: Why we need more data before the next pandemic. Sociologica 15(3), 125–143 (2021)

    Google Scholar 

  20. Gomez, J., Prieto, J., Leon, E., Rodríguez, A.: INFEKTA-an agent-based model for transmission of infectious diseases: the COVID-19 case in Bogotá, Colombia. PLoS ONE 16(2), e0245787 (2021)

    Article  Google Scholar 

  21. Gopalan, A., Tyagi, H.: How reliable are test numbers for revealing the COVID-19 ground truth and applying interventions? J. Indian Inst. Sci. 100(4), 863–884 (2020)

    Article  Google Scholar 

  22. Gressman, P.T., Peck, J.R.: Simulating COVID-19 in a university environment. Math. Biosci. 328, 108436 (2020)

    Article  MathSciNet  Google Scholar 

  23. Grimm, V., et al.: The ODD protocol for describing agent-based and other simulation models: a second update to improve clarity, replication, and structural realism. J. Artif. Soc. Soc. Simul. 23(2) (2020)

    Google Scholar 

  24. Groeneveld, J., et al.: Theoretical foundations of human decision-making in agent-based land use models-a review. Environ. Model. Softw. 87, 39–48 (2017)

    Article  Google Scholar 

  25. Groff, E.R., Johnson, S.D., Thornton, A.: State of the art in agent-based modeling of urban crime: an overview. J. Quant. Criminol. 35(1), 155–193 (2019)

    Article  Google Scholar 

  26. Hoertel, N., et al.: Facing the COVID-19 epidemic in NYC: a stochastic agent-based model of various intervention strategies. MedRxiv (2020)

    Google Scholar 

  27. Hollander, C.D., Wu, A.S.: The current state of normative agent-based systems. J. Artif. Soc. Soc. Simul. 14(2), 6 (2011)

    Article  Google Scholar 

  28. Huber, R., et al.: Representation of decision-making in European agricultural agent-based models. Agric. Syst. 167, 143–160 (2018)

    Article  Google Scholar 

  29. Jackson, M.L.: Low-impact social distancing interventions to mitigate local epidemics of SARS-CoV-2. Microbes Infect. 22(10), 611–616 (2020)

    Article  Google Scholar 

  30. Jager, W.: Enhancing the realism of simulation: on implementing and developing psychological theory in social simulation. J. Artif. Soc. Soc. Simul. 20(3), 14 (2017)

    Article  Google Scholar 

  31. Jalayer, M., Orsenigo, C., Vercellis, C.: CoV-ABM: a stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19. arXiv preprint arXiv:2007.13231 (2020)

  32. Johnston, R.M., Mohammed, A., Van Der Linden, C.: Evidence of exacerbated gender inequality in child care obligations in Canada and Australia during the COVID-19 pandemic. Politics Gender 16(4), 1131–1141 (2020)

    Article  Google Scholar 

  33. Kano, T., Yasui, K., Mikami, T., Asally, M., Ishiguro, A.: An agent-based model of the interrelation between the COVID-19 outbreak and economic activities. Proc. R. Soc. A 477(2245), 20200604 (2021)

    Article  MathSciNet  Google Scholar 

  34. Karaivanov, A.: A social network model of COVID-19. PLoS ONE 15(10), e0240878 (2020)

    Article  Google Scholar 

  35. Klabunde, A., Willekens, F.: Decision-making in agent-based models of migration: state of the art and challenges. Eur. J. Popul. 32(1), 73–97 (2016)

    Article  Google Scholar 

  36. Klôh, V.P., et al.: The virus and socioeconomic inequality: an agent-based model to simulate and assess the impact of interventions to reduce the spread of COVID-19 in Rio de Janeiro, Brazil. Brazilian J. Health Rev. 3(2), 3647–3673 (2020)

    Google Scholar 

  37. Lorch, L., et al.: Quantifying the effects of contact tracing, testing, and containment measures in the presence of infection hotspots. arXiv preprint arXiv:2004.07641 (2020)

  38. Lorig, F., Johansson, E., Davidsson, P.: Agent-based social simulation of the COVID-19 pandemic: a systematic review. J. Artif. Soc. Soc. Simul. 24(3) (2021)

    Google Scholar 

  39. Lynn, L.E.: The behavioral foundations of public policy-making. J. Bus. 59(4), S379–S384 (1986)

    Article  Google Scholar 

  40. Macal, C.M.: Everything you need to know about agent-based modelling and simulation. J. Simul. 10(2), 144–156 (2016)

    Article  Google Scholar 

  41. Mahmood, B.M., Dabdawb, M.M.: The pandemic COVID-19 infection spreading spatial aspects: a network-based software approach. AL-Rafidain J. Comput. Sci. Math. 14(1), 159–170 (2020)

    Google Scholar 

  42. Mahmood, I., et al.: FACS: a geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions. J. Simul. 16, 355–373 (2020)

    Article  Google Scholar 

  43. Milne, G.J., Xie, S.: The effectiveness of social distancing in mitigating COVID-19 spread: a modelling analysis. MedRxiv (2020)

    Google Scholar 

  44. Müller, B., et al.: Describing human decisions in agent-based models-ODD+ D, an extension of the odd protocol. Environ. Model. Softw. 48, 37–48 (2013)

    Article  Google Scholar 

  45. Müller, S.A., Balmer, M., Neumann, A., Nagel, K.: Mobility traces and spreading of COVID-19. MedRxiv (2020)

    Google Scholar 

  46. Ng, V., et al.: Projected effects of nonpharmaceutical public health interventions to prevent resurgence of SARS-CoV-2 transmission in Canada. CMAJ 192(37), E1053–E1064 (2020)

    Article  Google Scholar 

  47. Oldeweme, A., Märtins, J., Westmattelmann, D., Schewe, G., et al.: The role of transparency, trust, and social influence on uncertainty reduction in times of pandemics: empirical study on the adoption of COVID-19 tracing apps. J. Med. Internet Res. 23(2), e25893 (2021)

    Article  Google Scholar 

  48. Parady, G., Taniguchi, A., Takami, K.: Travel behavior changes during the COVID-19 pandemic in Japan: analyzing the effects of risk perception and social influence on going-out self-restriction. Transp. Res. Interdisc. Perspect. 7, 100181 (2020)

    Google Scholar 

  49. Pescarmona, G., et al.: An agent-based model of COVID-19 diffusion to plan and evaluate intervention policies. arXiv preprint arXiv:2108.08885 (2021)

  50. Rajkumar, R.P.: COVID-19 and mental health: a review of the existing literature. Asian J. Psychiatr. 52, 102066 (2020)

    Article  Google Scholar 

  51. Rechtin, M., Feldman, V., Klare, S., Riddle, N., Sharma, R.: Modeling and simulation of COVID-19 pandemic for Cincinnati Tri-State area. arXiv preprint arXiv:2006.06021 (2020)

  52. Reeves, D.C., Willems, N., Shastry, V., Rai, V.: Structural effects of agent heterogeneity in agent-based models: lessons from the social spread of COVID-19. J. Artif. Soc. Soc. Simul. 25(3), 1–3 (2022)

    Google Scholar 

  53. Schlüter, M., et al.: A framework for mapping and comparing behavioural theories in models of social-ecological systems. Ecol. Econ. 131, 21–35 (2017)

    Article  Google Scholar 

  54. Seale, H., et al.: Improving the impact of non-pharmaceutical interventions during COVID-19: examining the factors that influence engagement and the impact on individuals. BMC Infect. Dis. 20(1), 1–13 (2020)

    Article  MathSciNet  Google Scholar 

  55. Silva, P.C., Batista, P.V., Lima, H.S., Alves, M.A., Guimarães, F.G., Silva, R.C.: COVID-ABS: an agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions. Chaos, Solitons Fractals 139, 110088 (2020)

    Article  MathSciNet  Google Scholar 

  56. Simon, H.A.: From substantive to procedural rationality. In: Kastelein, T.J., Kuipers, S.K., Nijenhuis, W.A., Wagenaar, G.R. (eds.) 25 Years of Economic Theory, pp. 65–86. Springer, Cham (1976). https://doi.org/10.1007/978-1-4613-4367-7_6

  57. Squazzoni, F., et al.: Computational models that matter during a global pandemic outbreak: a call to action. J. Artif. Soc. Soc. Simul. 23(2), 1–10 (2020)

    Article  Google Scholar 

  58. Sun, R.: The importance of cognitive architectures: an analysis based on CLARION. J. Exp. Theor. Artif. Intell. 19(2), 159–193 (2007)

    Article  Google Scholar 

  59. Tan, A.X., Hinman, J.A., Magid, H.S.A., Nelson, L.M., Odden, M.C.: Association between income inequality and county-level COVID-19 cases and deaths in the us. JAMA Netw. Open 4(5), e218799 (2021)

    Article  Google Scholar 

  60. Wallentin, G., Kaziyeva, D., Reibersdorfer-Adelsberger, E.: COVID-19 intervention scenarios for a long-term disease management. Int. J. Health Policy Manag. 9(12), 508 (2020)

    Google Scholar 

  61. Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)

    Article  Google Scholar 

  62. Zhang, N., et al.: Impact of intervention methods on COVID-19 transmission in Shenzhen. Build. Environ. 180, 107106 (2020)

    Article  Google Scholar 

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Acknowledgments

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program - Humanities and Society (WASP-HS) funded by the Marianne and Marcus Wallenberg Foundation.

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Authors and Affiliations

  1. Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden

    Emil Johansson, Fabian Lorig & Paul Davidsson

  2. Internet of Things and People Research Center, Malmö University, Malmö, Sweden

    Emil Johansson, Fabian Lorig & Paul Davidsson

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  1. Emil Johansson
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Correspondence to Emil Johansson .

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  1. Clermont Auvergne University, Saint‐Étienne, France

    Luis G. Nardin

  2. Grantham Research Institute on Climate Change and the Environment, London, UK

    Sara Mehryar

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Johansson, E., Lorig, F., Davidsson, P. (2024). Aspects of Modeling Human Behavior in Agent-Based Social Simulation – What Can We Learn from the COVID-19 Pandemic?. In: Nardin, L.G., Mehryar, S. (eds) Multi-Agent-Based Simulation XXIV. MABS 2023. Lecture Notes in Computer Science(), vol 14558. Springer, Cham. https://doi.org/10.1007/978-3-031-61034-9_6

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