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Authoritarianism vs. democracy: Simulating responses to disease outbreaks

Author

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  • Biondo, A.E.
  • Brosio, G.
  • Pluchino, A.
  • Zanola, R.

Abstract

Disease outbreaks force the governments to rapid decisions. However, the stream of decision-making could be costly in terms of the democratic representativeness. The goal of the paper is to investigate the trade-off between pluralism of preferences and the time required to approach a decision. To this aim we develop and test a modified version of the Hegselmann and Krause (2002) model to capture these two characteristics of the decisional process in different institutional contexts. Using a twofold geometrical institutional setting, we simulate the impact of disease outbreaks to check whether countries exhibit idiosyncratic effects, depending on their institutional frameworks. Main findings are that the degree of pluralism is not necessarily associated with worse performances in managing emergencies, provided that the political debate is mature enough.

Suggested Citation

  • Biondo, A.E. & Brosio, G. & Pluchino, A. & Zanola, R., 2022. "Authoritarianism vs. democracy: Simulating responses to disease outbreaks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
  • Handle: RePEc:eee:phsmap:v:594:y:2022:i:c:s0378437122000784
    DOI: 10.1016/j.physa.2022.126991
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    References listed on IDEAS

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    1. Morrow, James D. & De Mesquita, Bruce Bueno & Siverson, Randolph M. & Smith, Alastair, 2008. "Retesting Selectorate Theory: Separating the Effects of W from Other Elements of Democracy," American Political Science Review, Cambridge University Press, vol. 102(3), pages 393-400, August.
    2. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. Stasavage, David, 2020. "Democracy, Autocracy, and Emergency Threats: Lessons for COVID-19 From the Last Thousand Years," International Organization, Cambridge University Press, vol. 74(S1), pages 1-17, December.
    5. Guillaume Deffuant & Frederic Amblard & Gérard Weisbuch, 2002. "How Can Extremism Prevail? a Study Based on the Relative Agreement Interaction Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-1.
    6. A. Pluchino & V. Latora & A. Rapisarda, 2006. "Compromise and synchronization in opinion dynamics," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 169-176, March.
    7. Cassan, Guilhem & Steenvoort, Milan Van, 2021. "Political regime and COVID 19 death rate: ecient, biasing or simply dierent autocracies?," CAGE Online Working Paper Series 539, Competitive Advantage in the Global Economy (CAGE).
    8. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    9. Ida R. Hoos, 1971. "Information Systems and Public Planning," Management Science, INFORMS, vol. 17(10), pages 658-671, June.
    10. Santo Fortunato, 2005. "On The Consensus Threshold For The Opinion Dynamics Of Krause–Hegselmann," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 259-270.
    11. Ilan Alon & Matthew Farrell & Shaomin Li, 2020. "Regime Type and COVID-19 Response," FIIB Business Review, , vol. 9(3), pages 152-160, September.
    12. Santo Fortunato & Vito Latora & Alessandro Pluchino & Andrea Rapisarda, 2005. "Vector Opinion Dynamics In A Bounded Confidence Consensus Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 16(10), pages 1535-1551.
    13. José Cheibub & Jennifer Gandhi & James Vreeland, 2010. "Democracy and dictatorship revisited," Public Choice, Springer, vol. 143(1), pages 67-101, April.
    14. Guilhem Cassan & Milan Van Steenvoort, 2021. "Political Regime and COVID 19 death rate: efficient, biasing or simply different autocracies ?," Papers 2101.09960, arXiv.org.
    15. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
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    1. Giorgio Brosio, Riccardo Pelosi, Roberto Zanola, 2022. "Short-term exit from pandemic restrictions: did European countries' speed converge?," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(2), pages 145-159, December.

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