How Effective are Tabletop Role-Playing (Serious) Games in Understanding and Validating the Predictive Capabilities of Disaster Response Agent-based Models?
DOI:
https://doi.org/10.54536/ajmri.v3i4.2937Keywords:
Agent-based models, Disaster response, Tabletop exercise, Role-playing games, ValidationAbstract
Tabletop Role-Playing Games (TRPGs) for disaster response management are collaborative exercises in which participants take on the roles of characters, guided by a “Game Master.” TRPG are useful tools for training, improving the preparedness of emergency responders, and understanding/validating Agent-Based Models (ABMs). This study evaluates the knowledge of participants and assessed the effectiveness of utilizing TRPG in validating a NetLogo model. Medical doctors and master’s students were divided into two groups, who participated in the exercise based on defined disaster response strategies. The central components of the ABM were transformed into a Disaster Response Tabletop Role-Playing Game (DRTRPG). Medical doctors and master’s degree students were assigned to two groups in the exercise that is based on defined disaster response strategies. Multichoice pre- and post-assessment tests were used to evaluate each group’s knowledge, while a performance scoring matrix was used to generate data from in-game activity. Data was analyzed using Pandas and MS Excel data analytical tools. Pre and post-assessment test results (n = 8) showed an 11.5% and 3.1% increase in knowledge for medical doctors and students on disaster and healthcare decision-making, respectively. Visual comparison between the ABM and DRTRPG results reveals partial model validity. However, statistical comparison shows a clear variation in the outcomes. The “Alternate” policy strategy resulted in better disaster response outcomes. The design and implementation of the DRTRPG resulted in a collaborative understanding and communication of the ABMS model with promising results. However, there are significant statistical differences between both methodologies, highlighting the challenge of validating the predictive capabilities of ABMS using RPGs.
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