IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2020-61-3.html
   My bibliography  Save this article

Opinion Dynamics and Collective Risk Perception: An Agent-Based Model of Institutional and Media Communication About Disasters

Author

Listed:
  • Francesca Giardini
  • Daniele Vilone

Abstract

The behavior of a heterogeneous population of individuals during an emergency, such as epidemics, natural disasters, terrorist attacks, is dynamic, emergent and complex. In this situation, reducing uncertainty about the event is crucial in order to identify and pursue the best possible course of action. People depend on experts, government sources, the media and fellow community members as potentially valid sources of information to reduce uncertainty, but their messages can be ambiguous, misleading or contradictory. Effective risk prevention depends on the way in which the population receives, elaborates and spread the message, and together these elements result in a collective perception of risk. The interaction between individuals' attitudes toward risk and institutions, the more or less alarmist way in which the information is reported and the role of the media can lead to risk perception that differs from the original message, as well as to contrasting opinions about risk within the same population. The aim of this study is to bridge a model of opinion dynamics with the issue of uncertainty and trust in the sources, in order to understand the determinants of collective risk assessment. Our results show that alarming information spreads more easily than reassuring one, and that the media plays a key role in this. Concerning the role of internal variables, our simulation results show that risk sensitiveness has more influence on the final opinion than trust towards the institutional message. Furthermore, the role of different network structures seemed to be negligible, even on two empirically calibrated network topologies, thus suggesting that knowing beforehand how much the public trusts their institutional representatives and how reactive they are to a certain risk might provide useful indications to design more effective communication strategies during crises.

Suggested Citation

  • Francesca Giardini & Daniele Vilone, 2021. "Opinion Dynamics and Collective Risk Perception: An Agent-Based Model of Institutional and Media Communication About Disasters," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(1), pages 1-4.
  • Handle: RePEc:jas:jasssj:2020-61-3
    as

    Download full text from publisher

    File URL: https://www.jasss.org/24/1/4/4.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andreas Flache & Michael Mäs & Thomas Feliciani & Edmund Chattoe-Brown & Guillaume Deffuant & Sylvie Huet & Jan Lorenz, 2017. "Models of Social Influence: Towards the Next Frontiers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-2.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dorit Zimand-Sheiner & Shalom Levy & Eyal Eckhaus, 2021. "Exploring Negative Spillover Effects on Stakeholders: A Case Study on Social Media Talk about Crisis in the Food Industry Using Data Mining," Sustainability, MDPI, vol. 13(19), pages 1-16, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael T Gastner & Károly Takács & Máté Gulyás & Zsuzsanna Szvetelszky & Beáta Oborny, 2019. "The impact of hypocrisy on opinion formation: A dynamic model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    2. Guillaume Deffuant & Ilaria Bertazzi & Sylvie Huet, 2018. "The Dark Side Of Gossips: Hints From A Simple Opinion Dynamics Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-20, September.
    3. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    4. Kononovicius, Aleksejus, 2021. "Supportive interactions in the noisy voter model," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    5. Deffuant, Guillaume & Roubin, Thibaut, 2023. "Emergence of group hierarchy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
    6. Deffuant, Guillaume & Roubin, Thibaut, 2022. "Do interactions among unequal agents undermine those of low status?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    7. Boschi, Gioia & Cammarota, Chiara & Kühn, Reimer, 2021. "Opinion dynamics with emergent collective memory: The impact of a long and heterogeneous news history," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    8. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    9. Morshedi, Mohamad Ali & Kashani, Hamed, 2022. "Assessment of vulnerability reduction policies: Integration of economic and cognitive models of decision-making," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    10. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    11. Hirofumi Takesue, 2020. "From defection to ingroup favoritism to cooperation: simulation analysis of the social dilemma in dynamic networks," Journal of Computational Social Science, Springer, vol. 3(1), pages 189-207, April.
    12. Ambrosius, Floor H.W. & Kramer, Mark R. & Spiegel, Alisa & Bokkers, Eddie A.M. & Bock, Bettina B. & Hofstede, Gert Jan, 2022. "Diffusion of organic farming among Dutch pig farmers: An agent-based model," Agricultural Systems, Elsevier, vol. 197(C).
    13. Matthew I. Jones & Antonio D. Sirianni & Feng Fu, 2022. "Polarization, abstention, and the median voter theorem," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    14. Bruce Edmonds, 2020. "Co-developing beliefs and social influence networks—towards understanding socio-cognitive processes like Brexit," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(2), pages 491-515, April.
    15. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).
    16. Christian Ganser & Marc Keuschnigg, 2018. "Social Influence Strengthens Crowd Wisdom Under Voting," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-23, September.
    17. Kononovicius, Aleksejus & Ruseckas, Julius, 2019. "Order book model with herd behavior exhibiting long-range memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 171-191.
    18. Marijn A. Keijzer & Michael Mäs & Andreas Flache, 2018. "Communication in Online Social Networks Fosters Cultural Isolation," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    19. Denis Tverskoi & Andrea Guido & Giulia Andrighetto & Angel Sánchez & Sergey Gavrilets, 2023. "Disentangling material, social, and cognitive determinants of human behavior and beliefs," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    20. Guillaume Deffuant, 2023. "Opinion Dynamics Model Revealing Yet Undetected Cognitive Biases," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 26(4), pages 1-12.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2020-61-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.