Abstract
Decision making is inherent to mankind, as human beings daily face situations in which they should choose among different alternatives by means of reasoning and mental processes. Many of these decision problems are under uncertain environments with vague and imprecise information. This type of information is usually modelled by linguistic information because of the common use of language by the experts involved in the given decision situations, originating linguistic decision making. The use of linguistic information in decision making demands processes of Computing with Words to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish such processes in an accurate and interpretable way. The good performance of linguistic computing dealing with uncertainty has caused a spread use of it in different types of decision based applications. This paper overviews the more significant and extended linguistic computing models due to its key role in linguistic decision making and a wide range of the most recent applications of linguistic decision support models.
Highlights
Human activities are very diverse and it is fairly common the necessity in many of them of decision making processes
This paper overviews the most wide-spread methodologies of Computing with words (CW) used in linguistic decision making 16,35,37,89,97, including a short list of those 5,47,84,87,88 that are interesting for specific decision situations but they have not been intensively used yet
Aggregation operators based on this linguistic model are the Linguistic Ordered Weighted Averaging (LOWA) operator 36, the Linguistic Weighted Disjunction (LWD), Linguistic Weighted Conjunction (LWC), the Linguistic Weighted Averaging (LWA) 34, the Linguistic Aggregation of Majority Additive (LAMA) operator 73 and the Majority Guided Induced Linguistic Aggregation Operators 41
Summary
Human activities are very diverse and it is fairly common the necessity in many of them of decision making processes. Many decision problems cannot be solved in this way because decisions might be related to changing environments, the existence of vagueness and uncertainty in the decision framework, and so on The latter problems, so-called illstructured problems 114, are quite common in real problems of the aforementioned disciplines. This paper overviews the most wide-spread methodologies of CW used in linguistic decision making 16,35,37,89,97, including a short list of those 5,47,84,87,88 that are interesting for specific decision situations but they have not been intensively used yet. It further presents in depth the most recent decision applications based on CW over the last years regarding real world applications.
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