Abstract

     In the last decades, the art and science of multi-attribute decision-making (MADM) have witnessed significant developments and have found applications in many active areas. A lot of research has demonstrated the ability of cognitive techniques in dealing with complex and uncertain decision information. The purpose of representing human cognition in the decision-making process encourages the integration of cognitive psychology and multi-attribute decision-making theory. Due to the emergence of research on cognitively inspired MADM methods, we make a comprehensive overview of published papers in this field and their applications. This paper has been grouped into five parts: we first conduct some statistical analyses of academic papers from two angles: the development trends and the distribution of related publications. To illustrate the basic process of cognitively inspired MADM methods, we present some underlying ideas and the systematic structure of this kind of method. Then, we make a review of cognitively inspired MADM methods from different perspectives. Applications of these methods are further reviewed. Finally, some challenges and future trends are summarized. This paper highlights the benefits of the synergistic approach that is developed based on cognitive techniques and MADM methods and identifies the frontiers in this field.

Introduction

     Real-world decision-making problems are often too complex to be considered through a single criterion. In order to solve problems with multiple attributes, multi-attribute decision-making (MADM) methods are developed, which signifcantly enhance the ability of decision-making methods. As a cognitive process, MADM refers to selecting the optimal solution by evaluating alternatives in the presence of multiple attributes. The number of alternatives for MADM problems is predetermined and limited. Basically, MADM methods can be roughly grouped into two categories [1]: (1) Outranking techniques. The outranking methods are based on pairwise comparison of alternatives, like ELECTRE (Elimination et Choix Traduisant la Realité in French, Elimination and Choice Expressing the Reality) [2] and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) [3]. (2) Multi-attribute utility and value theories. This kind of method usually assigns a utility to each alternative and includes UTA (utility additives) [5], AHP (analytic hierarchy process) [6], ANP (analytic network process) [7], MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) [8], etc. There is a growing body of literature that recognizes the importance of MADM methods in many fields of human life, such as healthcare [9, 10], transportation [11–۱۴], manufacturing [15–۱۸], and business [20, 21].

Conclusions

     The emergence of cognitively inspired MADM methods enriches the theoretical framework in MADM and greatly promotes the development of intelligent decision-making. It presents excellent modeling ability in processing complex information, which makes it of signifcant importance to MADM both from theoretical and practical points of view.

     In this paper, we have gone through the recent contributions about cognitively inspired MADM methods and attempted to provide a comprehensive review in this feld. Firstly, some statistical analyses of published papers regarding cognitively inspired MADM have been unfolded from two aspects: the development trend and the distribution of related publications. We have seen a steady increase in the number of academic papers in this feld. As to this fact, more investors have their sights on the cognitively inspired MADM methods in recent years. After that, the system architecture of cognitively inspired MADM has been illustrated. Then, a comprehensive overview of diferent types of cognitively inspired MADM approaches has been provided, which concludes the contributions of these methods to modern decisionmaking. After analyzing the theoretical knowledge, practical applications have also been summarized, which concludes that cognitively inspired MADM methods make great efects on practical applications. We have ended by discussing some challenges and future trends in this feld.