Talking about the likelihood of risks: an agent-based simulation of discussion processes in risk workshops

Clemens Harten, Matthias Meyer and Lucia Bellora-Bienengräber

Journal of Accounting & Organizational Change, 2021, vol. 18, issue 1, 153-173

Abstract: Purpose - This paper aims to explore drivers of the effectiveness of risk assessments in risk workshops. Design/methodology/approach - This study uses an agent-based model to simulate risk assessments in risk workshops. Combining the notions of transactive memory and the ideal speech situation, this study establishes a risk assessment benchmark and then investigates real-world deviations from this benchmark. Specifically, this study models limits to information transfer, incomplete discussions and potentially detrimental group characteristics, as well as interaction patterns. Findings - First, limits to information transfer among workshop participants can prevent a correct consensus. Second, increasing the required number of stable discussion rounds before an assessment improves the correct assessment of high but not low likelihood risks. Third, while theoretically advantageous group characteristics are associated with the highest assessment correctness for all risks, theoretically detrimental group characteristics are associated with the highest assessment correctness for high likelihood risks. Fourth, prioritizing participants who are particularly concerned about the risk leads to the highest level of correctness. Originality/value - This study shows that by increasing the duration of simulated risk workshops, the assessments change – as a rule – from underestimating to overestimating risks, unraveling a trade-off for risk workshop facilitators. Methodologically, this approach overcomes limitations of prior research, specifically the lack of an assessment and process benchmark, the inability to disentangle multiple effects and the difficulty of capturing individual cognitive processes.

Keywords: Risk assessment; Risk likelihood; Risk workshops; Simulation; Agent-based modeling; Bayesian networks (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jaocpp:jaoc-11-2020-0197

DOI: 10.1108/JAOC-11-2020-0197

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