An agent-based model to assess citizens' acceptance of COVID-19 restrictions. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Falcone, Rino; Sapienza, Alessandro
• Is Part Of: Journal of simulation. Volume 17:Issue 1(2023)
• Page Start: 105
• Page End: 119
• Publication Date: 2023-01-02
• Subjects: COVID-19 -- trust -- multi-agent system -- social simulation; Operations research -- Periodicals; Mathematical models -- Periodicals; Simulation methods -- Periodicals; 511.805
• DOI: 10.1080/17477778.2021.1965501
• Languages: English
• ISSNs: 1747-7778

Abstract

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.