Bayesian Network Applications in Decision Support Systems

Decision support systems are designed to provide decision makers with a view of the present and the future under alternative scenarios. A decision support system is different from a dashboard application representing current conditions and trends using a set of indicators and descriptive statistics. This paper focuses on decision support systems implementing Bayesian networks, with three case studies presenting applications in different areas. The first case study is about the integration of mobility data available from Google with hospitalization data related to COVID-19. This data from the pandemic era provides an impact assessment of non-pharmaceutical interventions such as the closure of airports. A second case study is from a website usability assessment with data from web surfing characteristics. A third application is a conflict resolution politography application where economic, demographic, and other types of data are analyzed to create a data-driven narrative for decision makers and researchers. These three different examples show how Bayesian networks are used in different contexts to support decision support systems. The paper is about decision support systems and Bayesian networks, with examples of implementation. It begins with an introduction to general decision support systems, then case studies, and concludes with a section describing future research pathways.