Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Evolution of psychological diversity in anthropoids

Differential psychologists rightly identified evolutionary theory as a unifying framework for explaining the origins and persistence of individual differences in a wide array of human psychological characteristics. Psychological diversity occurs on multiple levels, including between species, populations, generations, and individuals. Each level reveals the outcome of evolutionary processes at different temporal scales. I embrace a range of methods and results from quantitative and population genetics, developmental evolution, and phylogenetically grounded comparative psychology to explore how personality evolves in humans and nonhuman primates. At the level of species, I compared personality structure derived from rater assessments for four species of macaques and found a consistent, core set of personality dimensions (Dominance, Confidence, and Friendliness) describing these species. At the population level, I studied the relationship in humans between fertility/longevity trade-offs and the average personality of a country and found that Neuroticism and Agreeableness exhibit adaptively plasticity to life-history conditions. At the level of families, I estimated the quantitative genetic structure of personality in orang-utans and found that, like humans, a large portion of the phenotypic variance was explained by non-additive genetic effects. I examined between generation changes in personality by testing whether personality traits in humans are genetically correlated with fitness and found that in modern environments personality evolves very slowly. Finally, I translated current conceptual models of biological reactivity and stress response into mathematical models of developmental evolution and determined that evolution would select highly resilient phenotypes but that variation could be maintained by skew in the distribution of underlying genetic factors. From these results I broadly conclude that primate personality structure is generally conserved among species, mean personality levels change only very slowly between human generations, and that this evolution results in a genetic basis of personality that is characterized by epistasis. The evolution of individual differences has much to gain from the rigorous application of evolutionary methodology

Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure