A decision support model for management of fuzziness in global risk assessment

Solving decision-making problems requires efficient handling of uncertainties. This task has been usually performed by means of expert systems which are based on classical logic and, therefore, need special methods such as heuristic approaches, probability theory, possibility theory, and fuzzy theory. The later approach, fuzzy reasoning and logic, offers a more natural way of handling uncertainty since it is similar to human logical reasoning. In this paper, we develop a fuzzy logic model for assessment and prediction of country risk. This fuzzy method provides a systematic approach to analyzing a target country. By its nature, the decision making for global market involves various uncertain criteria; therefore, the fuzzy approach is suitable for this kind of analysis. The advantages of the approach are inclusion of economic data, consideration of political/social factors, and the ability to handle exact and fuzzy data

Supporting Complex Business Decisions with a Fuzzy Mobile Assistant

Researchers have recognized the importance of semantic expressiveness in understanding and solving complex problems and have identified a need to incorporate reasoning about uncertainty into decision tools that assist business managers. This study extends current approaches and develops new tools to allow artificial mobile assistants to manage rule-driven consultations by capturing and recommending problem solutions through natural language interfaces. A prototype assistant is described to support fuzzy knowledge representations and fuzzy rule-based consultations. The prototype’s application is implemented in a Windows 8 mobile device and applied in a case study of business outsourcing decisions

The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making

We present the induced 2-tuple linguistic generalized ordered weighted averaging (2-TILGOWA) operator. This new aggregation operator extends previous approaches by using generalized means, order-inducing variables in the reordering of the arguments and linguistic information represented with the 2-tuple linguistic approach. Its main advantage is that it includes a wide range of linguistic aggregation operators. Thus, its analyses can be seen from different perspectives and we obtain a much more complete picture of the situation considered and are able to select the alternative that best fits with with our interests or beliefs. We further generalize the operator by using quasi-arithmetic means, and obtain the Quasi-2-TILOWA operator. We conclude this paper by analysing the applicability of this new approach in a decision-making problem concerning product management.linguistic decision making, linguistic generalized mean, 2-tuple linguistic owa operator, 2-tuple linguistic aggregation operator

The intuitionistic fuzzy multi-criteria decision making based on inclusion degree

This paper introduces a new intuitionistic fuzzy multicriteria decision making method of evaluation based on degree of inclusion of two intuitionistic fuzzy sets. We have called the new technique TOPIIS (Technique to Order Preference by Inclusion of Ideal Solution). The technique is applied to develop an effective employee performance appraisal

Some views on information fusion and logic based approaches in decision making under uncertainty

Decision making under uncertainty is a key issue in information fusion and logic based reasoning approaches. The aim of this paper is to show noteworthy theoretical and applicational issues in the area of decision making under uncertainty that have been already done and raise new open research related to these topics pointing out promising and challenging research gaps that should be addressed in the coming future in order to improve the resolution of decision making problems under uncertainty

Temporal fuzzy association rule mining with 2-tuple linguistic representation

This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern. The novel application of the 2-tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms. Iterative Rule Learning (IRL) with a Genetic Algorithm (GA) simultaneously induces rules and tunes the membership functions. The discovered rules were compared with those from a traditional method of discovering fuzzy association rules and results demonstrate how the traditional method can loose information because rules occur at the intersection of membership function boundaries. New information can be mined from the proposed approach by improving upon rules discovered with the traditional method and by discovering new rules