Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

Consistency improvement with a feedback recommendation in personalized linguistic group decision making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consistency is an important issue in linguistic decision making with various consistency measures and consistency improving methods available in the literature. However, existing linguistic consistency studies omit the fact that words mean different things for different people, that is, decision makers' personalized individual semantics (PISs) over their expressed linguistic preferences are ignored. Therefore, the aim of this article is to propose a novel consistency improving approach based on PISs in linguistic group decision making. The proposed approach combines the characteristics of personalized representation and integrates the PIS-based model in measuring and improving the consistency of linguistic preference relations. A detailed numerical and comparative analysis to support the feasibility of the proposed approach is provided

The method of judging satisfactory consistency of linguistic judgment matrix based on adjacency matrix and 3-loop matrix

Language phrases are an effective way to express uncertain pieces of information, and easily conforms to the language habits of decision makers to describe the evaluation of things. The consistency judgment of a linguistic judgment matrices is the key to analytic hierarchy process (AHP). If a linguistic judgment matrix has a satisfactory consistency, then the rank of the decision schemes can be determined. In this study, the comparison relation between the decision schemes is first represented by a directed graph. The preference relation matrix of the linguistic judgment matrix is the adjacency matrix of the directed graph. We can use the $n - 1$ st power of the preference relation to judge the linguistic judgment matrix whether has a satisfactory consistency. The method is utilized if there is one and only one element in the $n - 1$ st power of the preference relation, and the element 1 is not on the main diagonal. Then the linguistic judgment matrix has a satisfactory consistency. If there are illogical judgments, the decision schemes that form a 3-loop can be identified and expressed through the second-order sub-matrix of the preference relation matrix. The feasibility of this theory can be verified through examples. The corresponding schemes for illogical judgments are represented in spatial coordinate system

Furthering the multi-route model of alexithymia: a constructionist perspective

Alexithymia – difficulties identifying and describing feelings with an externally-oriented thinking style – is elevated in a range of psychiatric and neurodevelopmental conditions, and may explain co-occurring affective psychopathology. Past research has primarily focused on the role of interoceptive deficits in the development of alexithymia, yet the sole reliance on this interoceptive account may be insufficient to explain the aetiology of alexithymia in a wide array of alexithymic individuals. Chapter 1 first describes the alexithymia construct and its high co-occurrence with a wide range of mental health conditions. This is followed by an overview of the past literature on the link between interoceptive deficits and alexithymia, and its limitations in explaining the development of alexithymia in different clinical groups. The multi-route model of alexithymia is discussed, which posits that there are multiple psychological pathways underlying alexithymia. In particular, the language hypothesis of alexithymia proposes that language impairments predispose language impaired groups to alexithymia. To enrich the theoretical discussion, the theory of constructed emotion is considered, introducing novel areas for research on the representation and acquisition of emotion concepts in alexithymic individuals. Chapter 2 presents an integrated systematic review and meta-analysis on the relationships between alexithymia and (i) multi-domain language impairments, and (ii) emotional granularity. A modest association was found between alexithymia and language impairments, and elevated alexithymic traits were evident in language impaired groups relative to those with typical language in a small subset of studies. Alexithymia was associated with less fine-grained perception of emotional experience. Chapter 3 investigates the prospective relationship between childhood language impairments and alexithymic traits in adolescence using data from the SCALES cohort. Supporting the language hypothesis, boys with low language function at ages 4-5 and those who later met the diagnostic criteria of language disorders at ages 5-6 reported more difficulties differentiating emotions and paying less attention to others’ emotions at ages 12-13 than peers with typical language. Early structural language difficulties were consistently associated with elevated alexithymic traits in adolescence. Chapter 4 adopts a strength-based approach, using professional writers as a model to study the role of language talents in emotional self-awareness. Results showed that writers had very low levels of alexithymic traits when compared to non-writers, and this group difference was related to higher self-reported interoceptive accuracy in writers compared to non-writers. Both writers and non-writers showed similar structural organisation of emotion concepts, which did not significantly predict their alexithymic traits. Chapter 5 tests the link between alexithymia and emotion concept acquisition. Experiments 1 and 2 found no robust associations between alexithymia and emotion concept learning processes, but an indirect pathway between alexithymia and more stochastic choices through co-occurring anxiety symptoms. Experiment 3 found this same indirect pathway through anxiety when learning abstract non-emotion concepts, suggesting a general choice characteristic. Chapter 6 investigates the relationships between autistic and alexithymic traits and information gathering. In a sample of typically-developing youths (aged 6-25 years), autistic traits were consistently associated with more information gathering regardless of information type and cost of information gathering. Computational modelling suggested that this was related to later emergence of subjective cost of information gathering, promoting later guesses in those with higher autistic traits. Alexithymia was uniquely associated with inconsistent reporting of emotional responses to rewards and losses, and reduced gathering of emotional information when analysing parent-report measures, suggesting a novel treatment target. Finally, Chapter 7 summarises the key findings and discusses their theoretical and methodological implications with respect to a multi-route model of alexithymia. This is followed by a general discussion of the utility of the theory of constructed emotion. Future directions and clinical implications are also discussed. Together, this collection of work seeks to refine the theoretical framework of the multi-route model of alexithymia and highlights the importance of mechanism-focused research, with the ultimate goal of informing treatments for the wide array of alexithymic individuals

On Measuring Social Dynamics of Online Social Media

Due to the complex nature of human behaviour and to our inability to directly measure thoughts and feelings, social psychology has long struggled for empirical grounding for its theories and models. Traditional techniques involving groups of people in controlled environments are limited to small numbers and may not be a good analogue for real social interactions in natural settings due to their controlled and artificial nature. Their application as a foundation for simulation of social processes suffers similarly. The proliferation of online social media offers new opportunities to observe social phenomena “in the wild” that have only just begun to be realised. To date, analysis of social media data has been largely focussed on specific, commercially relevant goals (such as sentiment analysis) that are of limited use to social psychology, and the dynamics critical to an understanding of social processes is rarely addressed or even present in collected data. This thesis addresses such shortfalls by: (i) presenting a novel data collection strategy and system for rich dynamic data from communities operating on Twitter; (ii) a data set encompassing longitudinal dynamic information over two and a half years from the online pro-ana (pro-anorexia) movement; and (iii) two approaches to identifying active social psychological processes in collections of online text and network metadata: an approach linking traditional psychometric studies with topic models and an algorithm combining community detection in user networks with topic models of the social media text they generate, enabling identification of community specific topic usage

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems