Decision Support Based on Human-Computer Collective Intelligence: Analysis of Methodologies and Ontology Model

Recent technological and social changes associated with the appearance of the Internet of things, cloud computing, crowdsourcing technology, and the transition to the sharing economy have created the potential for organizing communities of machines and humans providing collective intelligence. Decision making by such communities increases the efficiency of this process, because the communities make better decisions than decisions that the participants of the communities would have made alone. Currently, solutions to support the collective intelligence generated by human-machine communities do not exist. One of the problems to organize the decision-making process in a human-machine community is the problem of interoperability of participants. The purpose of this research is the development of an ontological model for decision support that would provide the interoperability of the participants of decision-making process and provide their independence of any decision-making technology. Multiple decision-making methodologies have been analyzed to achieve the research purpose. As a result of this analysis, two types of methodologies have been identified: (1) methodologies that support decision making on how to solve a specific problem(s), and (2) methodologies that support decision making on how to manage resources. These methodologies served as sources to identify ontological concepts relevant for modeling the process of finding a problem solution and for distributing the participants' role functions. An ontological model of decision support based on the identified concepts has been developed. This model provides the participants of the human-machine community with an understanding of the decision-making problem and enables interactions between these participants.