TABSAOND: A technique for developing agent-based simulation apps and online tools with nondeterministic decisions

Abstract Agent-based simulators (ABSs) have successfully allowed practitioners to estimate the outcomes of certain input circumstances in several domains. Although some techniques and processes provide hints about the construction of these systems, some aspects have not been discussed yet in the literature. In this context, the current approach presents a technique for developing ABSs. Its focus is to guide practitioners in designing and implementing the decision-making processes of agents in nondeterministic scenarios. As an additional technological innovation, the ABSs are deployed as both mobile apps and online tools. This work illustrates the current approach with two case studies in the fields of (a) health and welfare and (b) tourism. These case studies have also been developed with the most similar technique from the literature for comparing both techniques. The presented technique improved the simulated outcomes in terms of their similarity with the real ones. The obtained ABSs were more efficient and reliable for large amounts of agents (e.g. 10,000 – 400,000 agents). The development time was lower. Both the framework and the implementation of a case study are freely distributed as open-source to facilitate the reproducibility of the experiments and to assist practitioners in applying the current approach.