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The article focuses on publishing papers in various fields of logic, including philosophical and non-classical logics, algebraic logic, applications in computer science, linguistics, while providing detailed guidelines for manuscript submissions, formatting, references, and artwork.

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Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims to rank a set of cloud service providers based on the multi-criteria decision-making (MCDM) method. The suggested method's applicability is verified by comparing the outcomes with two established methodologies: SWARA and MARCOS methods under the type-2 neutrosophic number set (T2NNS) environment to calculate the importance of evaluation criteria and ranking the alternatives of cloud providers. A sensitivity analysis was executed to check the robustness of this model by examining the effect of criteria weights on the ranking of the alternatives. [ABSTRACT FROM AUTHOR]

Incommensurability is often introduced with the small improvement argument. Options A and B are shown to be incommensurable when it is neither the case that A is preferred to (or better than) B nor that B is preferred to (or better than) A, but a slightly improved version of A (A+) is still not preferred to B. Since A+ is preferred to A, but not to B, we must also conclude that it is also true that A and B are not indifferent (or equally good). Such incommensurable options seem incompatible with orthodox decision theory (and various forms of value theory) but options that obey the pattern described by this argument seem ubiquitous: my choice between lemon tarts and eclairs at my favourite pastry shop might exhibit this pattern, but so could my choice between jobs or careers. In trying to accommodate incommensurable options within various frameworks, philosophers have argued that we must preserve certain central features of the phenomenon. Among them is the supposed "hardness" of at least some incommensurable options: even if perhaps one would need to be a rather anxious gourmet to describe the choice between lemon tarts and eclairs as hard, the choice among careers could potentially be agonizing. However, it is not clear in which way choices among incommensurable options are "hard," nor how and whether such hardness poses problems for the various accounts of incommensurable choices. To complicate matters, the deontic verdicts for choices between incommensurable options seem to be relatively straightforward: one appealing view is that in such circumstances I am rationally permitted to choose any option that is not worse than another option. This paper aims to provide a sharper formulation of at least a version of the hardness problem, to argue that various theories of incommensurability fail to account for the hardness of some incommensurable choices, and to propose that the theory of instrumental rationality I develop in Rational Powers in Action, aided by a Kantian insight, promises to provide an adequate explanation of the hardness of choice among incommensurable options. [ABSTRACT FROM AUTHOR]

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work. [ABSTRACT FROM AUTHOR]

Since its publication in 2014, L. A. Paul's book Transformative Experience has sparked many discussions in philosophy and beyond. Her main argument is that experiences we have not had before can transform us epistemically (i.e., we learn something we could not learn without the experience) and personally (i.e., our point of view changes radically). This has implications for decision theory in particular, but also for other fields. The present paper provides an overview of how transformative experiences are thought to challenge rational choice and how authors have responded to this challenge (with a focus on the epistemic transformation). Furthermore, it also shows how the concept of transformative experience has been fruitful beyond the decision‐theoretical context, with a particular focus on illness, medical treatment, and the ethical issues involved. Finally, the paper presents open questions in the literature on transformative experiences. [ABSTRACT FROM AUTHOR]

In engineering-design problems, usually, there are multiple goals with different units, continuous and discrete variables, nonlinear equations, nonconvex equations, and coupled decisions. Ideally, all goals' target are reached simultaneously within the feasible space. However, the optimal solution may not be available. To deal with all those complexities, a modeling strategy named "satisficing" was proposed in the 1980s. The satisficing strategy allows designers to find "good enough" but may not be optimal solutions. In this paper, we review the publications applying the satisficing strategy on engineering-design problems, and categorize the methods regarding the design stages they manage. We define the methods dealing with all four design stages--formulation, approximation, solution, and evaluation--as the whole process satisficing methods. We review the publications using the whole process satisficing strategy in great detail. In the past 30 years, the whole process satisficing strategy has been improved and applied to a wide variety of engineering-design problems, based on which derived methods, concepts, and platforms are developed. We generalize the specialties, advantages, and scope of applications of the methods in the whole process satisficing strategy. We expect this paper provides information on when and how designers may apply satisficing for their problems. [ABSTRACT FROM AUTHOR]

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

In this study, ten questions measuring distributive justice on classical Likert and neutrosophic Likert scales consisting of two subdimensions—distributive and procedural justice—were used. Participants responded to the same questions for both the classical Likert and neutrosophic Likert scales within a single survey, with the neutrosophic method applied, for the first time, to the questions included in the scale. The neutrosophic scale responses were answered in percentages to resemble natural language, and the answers received for each question were reduced to the range [−1, 1] to grade the agreement approach through a score function used in neutrosophic decision-making theory. In this study, the neutrosophic scale, a scaling method with strong theoretical foundations, was compared with the traditional Likert scale. The results of the statistical analyses (exploratory factor analysis, reliability analysis, neural network analysis, correlation analysis, paired samples t-test, and one-way and two-way ANOVAs) and evaluations of the scales were compared to measure organizational justice within a single study. In this article, the symmetric and non-symmetric properties of statistical analysis that are specific to this paper in addition to general symmetric and non-symmetry properties are discussed. These symmetric and non-symmetric features are conceptualized according to the features on which each statistical analysis focuses. Finally, although this study presents a new area of research in the social sciences, we believe that the neutrosophic Likert scale and survey approach will contribute to collecting detailed and sensitive information on many topics, such as economics, health, audience perceptions, advertising responses, and product, market, and service purchase research, through the use of score functions. [ABSTRACT FROM AUTHOR]

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency. [ABSTRACT FROM AUTHOR]

The Analytic hierarchy process (AHP) is a widely used multi-criteria decision theory, and most AHP relies on the judgments of experts to derive priority scales. However, the judgments of experts may be subjective. Using machine learning algorithms for decision-making can be more objective, but machine learning algorithms are strongly related to the collected data and not being flexible enough. This paper tries to combine experts' judgments with algorithmic judgments to improve the bias of experts' judgments while still making decision-making flexible. In this paper, the authors introduce the ordered pair of normalized real numbers (OPNs) into the AHP method for the first time and propose the fuzzy analytic hierarchy process with the OPNs (OFAHP). The OFAHP uses OPNs to combine experts' judgments with those of machine learning algorithms and then make decisions by OPNs. Experiments on real data sets show that the proposed method can get reasonable decision results. Moreover, when the experts' judgments are wrong or invalid, the judgments given by the machine learning algorithm can correct the experts' judgments to obtain a reasonable decision-making result. [ABSTRACT FROM AUTHOR]

In recent years, various medical diagnosis problems have been addressed from the perspective of multi-attribute decision making. Among them, the three-way decision theory can provide a novel scheme to solve medical diagnosis issues under the framework of multi-attribute decision making via considering transforming relationships between loss functions and decision matrices. In this paper, we primarily explore a three-way decision method with tolerance dominance relations in ordered decision information systems. In existing three-way decision models, all objects can be divided into two states, we utilize decision attributes to obtain the set of two states in ordered decision information systems. Then, in order to improve the accuracy of patient classifications, the paper simultaneously considers the influence of loss and gain functions for each object, and uses loss and gain functions to obtain net profit functions as new measurement functions. Meanwhile, a class of three-way decisions in terms of multi-attribute decision making rules based on a tolerance dominance relation is established. In light of the proposed three-way decision method, we further construct a multi-attribute decision making method by using tolerance dominance relations and the constructed method is applied to a medical diagnosis issue of Lymphography. Finally, a comparison analysis and an experimental evaluation are performed to illustrate the feasibility and effectiveness of the presented methodology. [ABSTRACT FROM AUTHOR]

In the face of increasingly complex data forms and decision-making problems, the uncertainty of information poses a major challenge to multi-attribute decision-making methods. How to effectively organize information and serve realistic decision-making problems has attracted extensive attention in the academic circles. In view of this, based on the distribution law of random variables, we put forward the basic concept of probability numbers and construct a general framework, including the concepts of type, order, item, isomorphism and isomerism, same domain and same distribution of probability numbers. On this basis, we further define the expectation and variance formula of probability numbers, and its operation rules are defined for the same type of probability numbers. To compare the dominance and inferiority of probability numbers further accurately, we put forward the concepts of dominance degree and comparability degree of probability numbers, so that decision makers can realize the ranking of probability numbers by calculating the comprehensive dominance degree. In view of the related concepts of probability numbers, we summarize the properties and theorems of probability numbers and prove them. In addition, a probability numbers-based multi-attribute decision-making framework model is proposed to solve the multi-attribute decision-making problem. Decision makers can select appropriate sub-models to construct personalized multi-attribute decision-making methods according to actual needs. At the end of the paper, we apply the method to the multi-attribute decision case of campus express stations evaluation and verify the scientificity and rationality of the evaluation method. The concept of probability numbers and its decision model proposed in this paper extend the concept category of numbers, enrich the multi-attribute decision-making method based on probability numbers, and have certain reference significance for further research of uncertain decision theory and method. [ABSTRACT FROM AUTHOR]

Design education has traditionally relied heavily on physical integration as it involves a lot of hands-on work, group critiques, and collaborative projects, but the COVID-19 pandemic has fundamentally shifted the way teaching is done, which resulted in many institutions adapting to remote teaching and learning environments. This has created challenges for design educators who have had to find ways to evaluate students' progress in the absence of in-person interactions. In this paper, we are proposing a dashboard visualization approach that helps educators monitor the progression of the entire class of students using artificial intelligence (AI) by tracking a time-based evolution of a design statement. This approach uses various natural language processing (NLP) models to produce stock-like charts, which represent students' and student groups' progression through a series of divergence and convergence phases. These charts become a form of design artifact that allows educator(s) to gain a bird's-eye view of the class and react to groups that may require assistance; at the same time, it becomes a qualitative means of evaluation and comparison across students and groups. Toward the end, this paper also showcases a web-based platform that is publicly available using such methodology, a case study that applied so methodology and recommendations of future works possible. [ABSTRACT FROM AUTHOR]

Increasingly, our cities are confronted with crises. Fuelled by climate change and a loss of biodiversity, increasing inequalities and fragmentation, challenges range from social unrest and outbursts of violence to heatwaves, torrential rainfall, or epidemics. As crises require rapid interventions that overwhelm human decision-making capacity, AI has been portrayed as a potential avenue to support or even automate decision-making. In this paper, I analyse the specific challenges of AI in urban crisis management as an example and test case for many super wicked decision problems. These super wicked problems are characterised by a coincidence of great complexity and urgency. I will argue that from this combination, specific challenges arise that are only partially covered in the current guidelines and standards around trustworthy or human-centered AI. By following a decision-centric perspective, I argue that to solve urgent crisis problems, the context, capacities, and networks need to be addressed. AI for crisis response needs to follow dedicated design principles that ensure (i) human control in complex social networks, where many humans interact with AI; (ii) principled design that considers core principles of crisis response such as solidarity and humanity; (iii) designing for the most vulnerable. As such this paper is meant to inspire researchers, AI developers and practitioners in the space of AI for (urban) crisis response – and other urgent and complex problems that urban planners are confronted with. [ABSTRACT FROM AUTHOR]

Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios. [ABSTRACT FROM AUTHOR]

Multi-attribute group decision-making (MAGDM) is one of the research hotspots in human cognitive and decision-making theory. However, there are still challenges to the existing MAGDM methods in modeling uncertain linguistics of decision-makers' (DMs') cognitive information and objectively obtaining weights. Therefore, this paper aims to develop a new MAGDM method considering incomplete known weight information under spherical uncertain linguistic sets (SULSs) to model uncertain information in MAGDM problems. The method mainly includes the following aspects. Firstly, a new concept, which enables an intuitive evaluation of neutral membership and hesitancy degrees at the linguistic evaluation, has been is first developed for capturing the more uncertain information. Secondly, the cosine similarity measure (CSM) and cross-entropy measure (CEM) are widely used to measure ambiguous information because of their robustness of measurement results. The CSM and CEM are extended to SULSs to calculate the DMs' and attributes weights quantitively, respectively. Thirdly, in terms of effective integration of fuzzy information to obtain more accurate decision results, the Hamy mean (HM) and dual Hamy mean (DHM) operators are valued due to their consideration of the interrelationships between inputs. Two extension operators, named spherical fuzzy uncertain linguistic weight HM and DHM, are proposed to integrate spherical fuzzy uncertain linguistic information in the third stage. In the experiment, a decision case is presented to illustrate the applicability of the proposed method, and results show the effectiveness, flexibility and advantages of the proposed method are demonstrated by numerical examples and comparative analysis. [ABSTRACT FROM AUTHOR]

The growth of investments in tourism and increase in production due to increased tourist demand has a positive synergistic effect on all sectors of the country's economy. That encourages investment activity, the creation of new jobs, the expansion of trade, and an increase in wages. A part of the income comes through taxes to the state budget. A synergistic effect occurs consequently. This research showed that the system of organization and development of the tourist services market represents a combination of financial and economic structures, models, and management methods. With the help of it, social, group coordination, and private interest arising from the growth of the tourist product market are achieved. The paper presents the methods and tools that enable the assessment of the region's competitiveness in the tourist and recreational services provisions and an overview of the main factors that influence the demand for these services. The consistency and objectivity of the considered problems in their mutual connection and interdependence are ensured by considering general economic principles and methods of analyzing the legality of economic development, logical analysis, decision theory methods, set theory, historical method, abstract-logical method, statistical methods (consideration of indicators in dynamics, grouping, method of detailing), methods of comparison, comparison, etc. The study is based on economic literature, works of contemporary scientists, the legislative framework of the Russian Federation, materials of state statistical authorities, materials of scientific conferences, and the information potential of the Internet. [ABSTRACT FROM AUTHOR]

In the existing conflict analysis models, they used a triangular fuzzy number on [0, 1] to describe the range of an agent's attitude towards an issue, but there are still some shortcomings in describing the specific attitude and degree of conflict represented by the triangular fuzzy number. In this paper, the conflict analysis model is extended, improved and perfected. Firstly, the expectation of triangular fuzzy number is used in the [-1, 1] triangular fuzzy information system to reasonably express the specific attitudes represented by a triangular fuzzy number. Secondly, the weights of each issue are obtained by using the Sugeno measure, which determines the total attitude of the agent towards all issues. Thirdly, the relationship between agents is obtained with the help of the weighted distance of triangular fuzzy numbers. Finally, the thresholds α and β are calculated by means of triangular fuzzy decision theory rough sets. [ABSTRACT FROM AUTHOR]

The paper is an addition to the intensionalist approach to decision theory, with emphasis on game theoretic modelling. Extensionality in games is an a priori requirement that players exhibit the same behavior in all algebraically equivalent games on pain of irrationality. Intensionalism denies that it is always irrational to play differently in differently represented (described, understood) but algebraically equivalent versions of a game. I offer a framework to integrate game non-extensionality with the more familiar idea of linguistic non-extensionality from philosophy of language, followed by applications of it based on toy examples of well-known game models. I argue that the notion of what I call "Intensional Nash Equilibrium" is, in effect, very useful in understanding human decision-making. [ABSTRACT FROM AUTHOR]

This paper concerns the incomplete pairwise comparison matrix produced from judgments of experts in the Analytic Hierarchy Process (AHP). In such situation, we have to estimate the missing elements with suitable values by ensuring the consistency of the pairwise matrix. Metaheuristics are powerful tool to solve optimization problems. Then, improved genetic algorithm (GA) is proposed to recover the missing judgments until a satisfactory level of consistency is reached. Experiment results are performed with incomplete pairwise matrices with different sizes showing the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Multisensor information fusion technology is a symbol of scientific and technological progress. This paper is aimed at discussing the performance of virtual reality (VR) technology in the environmental art design of multisensor information fusion technology. This paper prepares some related work in the early stage and then lists the algorithms and models, such as the multisensor information fusion model based on VR instrument technology, and shows the principle of information fusion and GPID bus structure. This paper describes the multisensor information fusion algorithm to analyze DS evidence theory. In the evidence-based decision theory, the multisensor information fusion process is the calculation of the qualitative level and/or confidence level function, generally calculating the posterior distribution information. In addition to showing its algorithm, this paper also shows the data flow of the multisensor information fusion system through pictures. Then, this paper explains the design and construction of garden art environment based on active panoramic stereo vision sensor, shows the relationship of the four coordinates in an all-round way, and shows the interactive experience of indoor and outdoor environmental art design. Then, this paper conducts estimation simulation experiments based on EKF and shows the results, and it is concluded that the fusion data using the extended Kalman filter algorithm is closer to the actual target motion data and the accuracy rate is better than 92%. [ABSTRACT FROM AUTHOR]

Bayesian decision models use probability theory as a commonly technique to handling uncertainty and arise in a variety of important practical applications for estimation and prediction as well as offering decision support. But the deficiencies mainly manifest in the two aspects: First, it is often difficult to avoid subjective judgment in the process of quantization of priori probabilities. Second, applying point-valued probabilities in Bayesian decision making is insufficient to capture non-stochastically stable information. Soft set theory as an emerging mathematical tool for dealing with uncertainty has yielded fruitful results. One of the key concepts involved in the theory named soft probability which is as an immediate measurement over a statistical base can be capable of dealing with various types of stochastic phenomena including not stochastically stable phenomena, has been recently introduced to represent statistical characteristics of a given sample in a more natural and direct manner. Motivated by the work, this paper proposes a hybrid methodology that integrates soft probability and Bayesian decision theory to provide decision support when stochastically stable samples and exact values of probabilities are not available. According to the fact that soft probability is as a special case of interval probability which is mathematically proved in the paper, thus the proposed methodology is thereby consistent with Bayesian decision model with interval probability. In order to demonstrate the proof of concept, the proposed methodology has been applied to a numerical case study regarding medical diagnosis. [ABSTRACT FROM AUTHOR]

The Two Envelopes Problem is a beautiful and quite confusing problem in decision theory which is ca. 35 years old and has provoked at least 150 papers directly addressing the problem and displaying a surprising variety of different responses. This paper finds decisive progress in an approach of Priest and Restall in 2003, contends that the recent papers having appeared since did not really go beyond that paper, argues further that Priest's and Restall's solution is still not complete, and proposes a completion of their solution. If the analysis is correct, this work has the potential of laying the Two Envelopes Problem at rest. [ABSTRACT FROM AUTHOR]

The uncertainty of intuitionistic fuzzy numbers (IFNs) is further enhanced by the existence of the degree of hesitation (DH). The shortcomings of existing researches are mainly reflected in the following situations: when comparing IFNs, the comparison rules of IFNs are difficult to apply to the comparison of any two IFNs, or the relevant methods do not fully consider the uncertainty expressed by DH. Thus, the rationality of the decision results needs to be improved. On the other hand, multi-attribute decision making (DADM) based on IFNs is often not objective due to the need to determine the attribute weight. Moreover, the strict condition of attribute aggregation of classical dominance relation makes it a method that fails considering the practical application. Aiming at the comparison problem of IFNs, this paper takes probability conversion as the starting point and proposes an IFN comparison method based on the area method, which can better deal with the comparison problem of "either superior or inferior" IFNs. In addition, aiming at the MADM problem of an intuitionistic fuzzy information system, we propose an intuitionistic fuzzy probabilistic dominance relation model and construct the MADM method under the probabilistic dominance relation. The series properties of IFNs and probabilistic dominance relation were summarized and proved, which theoretically ensured the scientificity and rigor of the method. The results show that the comparison and ranking method of IFNs proposed in this paper can be applied to the comparison of any two IFNs, and the dominance degree of IFNs is presented in the form of probability, which is more flexible and practical than the classical method. The probabilistic dominance relation method based on IFNs avoids the problem of determining attribute weights subjectively or objectively, and the decision maker can reflect decision preference by adjusting decision parameters to better match the actual problem. The application of this model to a campus express site evaluation further verifies the feasibility of the proposed method and the rationality of the results. In addition, various extension problems of the model and method proposed in this paper are discussed, which pave the way for future related research. This paper constructs a complete decision-making framework through theoretical analysis and application from practical problems, which provides a reference for enriching and improving uncertain decision-making theory and the MADM method. [ABSTRACT FROM AUTHOR]

Energy communities are one of the vital puzzle pieces of future smart cities. This paper proposes a novel structure for a sustainable smart city by integrating energy communities in a multi‐carrier energy network. Each energy community has a manager; the so‐called Energy Community Managers (ECM), who trades energy with the upstream Multi‐Carrier Network Operator (MCNO). On the other hand, MCNO participates in the upstream energy markets to satisfy the demands of energy communities by maximizing its own profit. Therefore, ECMs and MCNO should solve a bilevel optimization problem associated with some common variables at both levels such as: energy carrier price and the amount of energy carrier exchange. In fact, MCNO is the leader and ECMs are the followers of such a bilevel optimization problem. Strong duality is employed to convert the bilevel optimization into a single level, while uncertainties are modelled by information gap decision theory and a scenario‐based approach. Sensitivity analysis shows that the thermal energy selling price and the gas buying price are the most crucial influencing on the profit of MCNO by 3.22% and −3.91%, respectively. Furthermore, the obtained results indicate that the risk attitude of the multi‐carrier energy network operator has a critical role in the total profit. [ABSTRACT FROM AUTHOR]

The complexities of managing multinational corporations (MNCs) have been increasing over a period of time. Developing a new controlling model for MNCs is critical and is required for their effective management. This paper analyses the ‘evolutionary theory of multinational corporation’ as well as the ‘business network theory’. Exploring the ‘Evolutional model of controlling’ based on multi-factor analysis, the paper examines the assumptions of the controlling concept, and wherever possible, redefines them. It extends the approach by incorporating additional functions and features including reverse knowledge transfer and measuring controlling effectiveness. Additional indirect parameters for the effective measurement of controlling are analysed. The drawbacks of controlling are mentioned and gaps are identified to develop a new model of controlling. The newer model is based on management approaches, decision making theory and an analogy of the human nervous system. [ABSTRACT FROM AUTHOR]

Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively. [ABSTRACT FROM AUTHOR]

This review looks at the development of decision science and behavioral economics, tracing the chronological progression of these disciplines and their symbiotic fusion in elucidating our comprehension of economic choices. It starts by discussing the limitations of traditional economic theories that assume rational and profitmaximizing behavior, highlighting the need for a more empirically anchored approach. The paper traces the development of decision theory amidst uncertainty, beginning with Blaise Pascal's notion of expected value, progressing to Daniel Bernoulli's expected utility, and later formalized by John von Neumann and Oskar Morgenstern. This journey culminates in the contributions of Daniel Kahneman and Amos Tversky, who introduced the concept of subjective expected utility. The paper acknowledges the inclusion of uncertainty surrounding delayed payoffs and discusses the role of cognitive biases and heuristics--mental shortcuts--in decisionmaking, showing how they affect our economic choices. The authors also show how these insights have been used in real-world settings, such as nudging, a technique used to subtly guide one's behavior. [ABSTRACT FROM AUTHOR]

When is it legitimate for a government to ‘nudge’ its citizens, in the sense described by Thaler and Sunstein (2008)? In their original work on the topic, Thaler and Sunstein developed the ‘as judged by themselves’ (or AJBT) test to answer this question (Thaler and Sunstein 2008, p. 5). In a recent paper, Paul and Sunstein (2019) raised a concern about this test: it often seems to give the wrong answer in cases in which we are nudged to make a decision that leads to what Paul calls a personally transformative experience, that is, one that results in our values changing (Paul 2014). In those cases, the nudgee will judge the nudge to be legitimate after it has taken place, but only because their values have changed as a result of the nudge. In this paper, I take up the challenge of finding an alternative test. I draw on my aggregate utility account of how to choose in the face of what Ullmann-Margalit (2006) calls big decisions, that is, decisions that lead to these personally transformative experiences (Pettigrew 2019, Chapters 6 and 7). [ABSTRACT FROM AUTHOR]

According to Paul (Transformative experience, 1st edn, Oxford University Press, 2014), transformative experiences pose a challenge to decision theory since their value cannot be anticipated. Building on Pettigrew's (in: Lambert, Schwenkler (eds) Becoming someone new: essays on transformative experience, choice, and change, Oxford University Press, pp 100–121, 2020) redescription, this paper presents a new approach to how and when transformative decisions can nevertheless be made rationally. Thanks to fundamental higher-order facts that apply to any kind of experience, an agent always at least knows the general shape of the utility space. This in combination with the knowledge about the non-transformative alternative in the choice set can enable rational decision-making despite the presence of a transformative experience. For example, this paper's approach provides novel arguments for why gender transition (cf. McKinnon in Res Philosophica 92(2):419–440, 2015) or staying childfree (cf. Barnes in Philos Phenomenol Res 91(3):775–786, 2015) can be rational. [ABSTRACT FROM AUTHOR]

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM--an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision--theory, experiments, and applications). [ABSTRACT FROM AUTHOR]

Each of us has a story which we can think of as a set of beliefs, hopefully consistent. We make our decisions in view of our beliefs which may be probabilistic, in the general case, but simple yes or no as in this paper. Our beliefs are our envelope just as the shell of a tortoise is its envelope. Decision theory—or single agent game theory tells us when to make the best choice in a game of us against nature. But nature has no desire to further or frustrate our efforts. Nature is mysterious but not malign. Things change when there are other agents involved. Then the best thing for us to do will depend on what they do. And they will think the same. And we predict their actions in terms of what we think their beliefs are. But how do we coordinate with others whose beliefs are different? This paper addresses the issue of working together despite different beliefs. [ABSTRACT FROM AUTHOR]

This paper provides a novel target threat assessment model that utilizes TOPSIS and three-way decision-making under a single-valued neutrosophic environment. The presented model provides theoretical support for combat decision-making in complex battlefield environments with uncertain information. The model employs single-valued neutrosophic sets to handle uncertain data, which enhances the descriptive ability of information. The maximum deviation method is used to calculate attribute weight factors, which highlights the importance of each attribute. The final target threat ranking is obtained based on the relative closeness coefficient of each target. Furthermore, the proposed model constructs a multi-attribute aggregation loss function matrix for each target, sets the risk avoidance coefficient under the knowledge of the battlefield condition, and calculates the decision threshold of each target using three-way decision theory. This method produces the classification of the target choice. The numerical examples and comparison analysis demonstrate that the suggested model can handle ambiguous scenario information effectively and reasonably, transform traditional decision-making ranking results into three-way classification findings, and provide a rationale for choosing an attacking target. [ABSTRACT FROM AUTHOR]

The issue of distributivity of aggregation operators is crucial for many different areas such as fuzzy sets and fuzzy logic, pseudo-analysis and measure theory, and particulary in the decision making theory. The problem of distributivity of an operator form a special class of uni-nullnorms over a general uninorm is being addressed through this paper. The class in question consists of uni-nullnorms with continuous and Archimedean underlying t-norms and t-conorms, and the results presented here are a natural continuation and extension of some previous works with an emphasis on a much wider class of uninorms as inner operators. [ABSTRACT FROM AUTHOR]

In the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES. [ABSTRACT FROM AUTHOR]

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]

Decisions where there is not enough information for a well-informed decision due to unidentified consequences, options, or undetermined demarcation of the decision problem are called decisions under great uncertainty. This paper argues that public policy decisions on how and if to implement decision-making processes based on machine learning and AI for public use are such decisions. Decisions on public policy on AI are uncertain due to three features specific to the current landscape of AI, namely (i) the vagueness of the definition of AI, (ii) uncertain outcomes of AI implementations and (iii) pacing problems. Given that many potential applications of AI in the public sector concern functions central to the public sphere, decisions on the implementation of such applications are particularly sensitive. Therefore, it is suggested that public policy-makers and decision-makers in the public sector can adopt strategies from the argumentative approach in decision theory to mitigate the established great uncertainty. In particular, the notions of framing and temporal strategies are considered. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines. [ABSTRACT FROM AUTHOR]

The authors consider the current issues of the optimal decision theory, namely, the analysis of the asymptotic properties of the Fourier transform of the summatory Abel–Poisson function. The Fourier transform considered in the paper is based on the solution of the classical Laplace's equation in polar coordinates (in the middle of the unit circle) with the corresponding boundary conditions. This Fourier transform of the summatory Abel–Poisson function is defined on the classes of fractional differential functions. The asymptotic estimates are obtained in the paper for this Fourier transform, which is an important element in solving many applied optimization problems. [ABSTRACT FROM AUTHOR]

Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions. [ABSTRACT FROM AUTHOR]

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China's digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China's provincial digital economy. [ABSTRACT FROM AUTHOR]

Copyright of Retos, Revista de Ciencias Administrativas y Económicas is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The most critical and purely heuristic assumption about priority vector estimation on the basis of pairwise comparisons is that which states a positive relationship between the consistency of decision makers' judgments and the quality of estimates of their priorities. As this issue constitutes the area of interest of the Multi-Criteria Decision Making theory in relation to AHP, it's examined in this paper via Monte Carlo simulations from the perspective of a new measure of PCM consistency i.e. Index of Square Logarithm Deviations. It needs to be emphasized that such problems of applied mathematics have been already studied via computer simulations as the only way of this phenomenon examination. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of decision-making in the context of judgment-based (subjective) deselection using decision theory concepts developed by Daniel Kahneman in collaboration with others. Judgment-based deselection is considered from multiple perspectives using this approach. Situating the deselection context within loss aversion, status quo bias, and the endowment effect (products of prospect theory and behavioral economics) may explain the psychological reluctance accompanying deselection. The role of feedback as a condition for developing intuitive expertise demonstrates the limited potential for developing expertise in subjective deselection. Heuristics and biases employed in decision-making and decision fatigue may account for inconsistencies in deselection decisions. [ABSTRACT FROM AUTHOR]