 An agent-based model to assess citizens’ acceptance of COVID-19 restrictionsRino Falcone and Alessandro Sapienza Journal of Simulation, 2023, vol. 17, issue 1, 105-119 Abstract: Italy was the first European state affected by COVID-19. Despite many uncertainties, citizens chose to trust the authorities and their trust was pivotal. This research aims to investigate the contribution of Italian citizens’ trust in Public Institutions and how it influenced the acceptance of the necessary counter measures. Applying linear regression to a dataset of 4260 Italian respondents, we modelled trust from its main cognitive components, with particular reference to competence and willingness. Therefore, exploiting agent-based modelling, we investigated how these components affected trust and how trust evolution influences the acceptance of these restrictive measures. Our analysis confirms the key role of competence and willingness as cognitive components of trust. Results also suggest that a generic attempt to raise the average trust, besides being challenging, may not be the best strategy to increase compliance. Furthermore, reasoning at category level is a fundamental to identify the best components on which to invest. Date: 2023 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:tjsmxx:v:17:y:2023:i:1:p:105-119 Ordering information: This journal article can be ordered from DOI: 10.1080/17477778.2021.1965501 Access Statistics for this article Journal of Simulation is currently edited by Christine Currie More articles in Journal of Simulation from Taylor & Francis Journals |
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