Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

Theoretical optimisation of IT/IS investments: A research note

The justification of Information Technology (IT) is inherently fuzzy, both in theory and practice. The reason for this is due to the largely intangible dimensions of IT projects. In view of this, this research note presents the results of on-going research, in the application of Fuzzy Cognitive Mapping (FCM), as a tool to identify complex functional interrelationships associated with the justification of IT. This paper presents a theoretical functional model which describes these relationships, and by using an FCM, further interrelationships are developed in the context of justifying IT projects. A procedure which would address the optimisation of these intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a process for Investment Justification