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We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

Purpose: Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution. Design/methodology/approach: Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function. Findings: Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved. Originality/value: This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial. [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]

Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder. [ABSTRACT FROM AUTHOR]

I respond to a dilemma for Causal Decision Theory (CDT) under determinism, posed in Adam Elga's paper "Confessions of a Causal Decision Theorist". The treatment I present highlights (i) the status of laws as predictors, and (ii) the consequences of decision dependence, which arises natively out of Jeffrey Conditioning and CDT's characteristic equation. My argument leverages decision dependence to work around a key assumption of Elga's proof: to wit, that in the two problems he presents, the CDTer must employ subjunctive‐suppositional (rather than evidential) transformations of a shared prior. [ABSTRACT FROM AUTHOR]

Moral options are permissions to do less than best, impartially speaking. In this paper, we investigate the challenge of reconciling moral options with the ideal of justifiability to each individual. We examine ex-post and ex-ante views of moral options and show how they might conflict with this ideal in single-choice and sequential-choice cases, respectively. We consider some ways of avoiding this conflict in sequential-choice cases, showing that they face significant problems. [ABSTRACT FROM AUTHOR]

Historically, economic models of memory have neglected a pivotal aspect: decay, the established notion that memories deteriorate over time, resulting in a loss of fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other intuitively appealing memory phenomena. We apply the framework to both finite time and dynamic programming settings, deriving a wide range of results with a focus on comparative statics and asymptotic behaviors. Many of our results can also be applied to multi-dimensional rational inattention settings where individuals have to split their attention across several information sources. [ABSTRACT FROM AUTHOR]

This paper is a discussion note on Isaacs, Hájek and Hawthorne (2022) , which claims to offer a new motivation for imprecise probabilities, based on the mathematical phenomenon of non-measurability. In this note, I clarify some consequences of that proposal. In particular, I show that if the proposal is applied to a bounded 3 -dimensional space, then one has to reject at least one of the following: • If A is at most as probable as B and B is at most as probable as C ⁠ , then A is at  most as probable as C ⁠. • Let A ∩ C = B ∩ C = ∅ ⁠. A is at most as probable as B if and only if (A ∪ C) is at  most as probable as (B ∪ C) ⁠. But rejecting either statement seems unattractive. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles driven by mobile edge computing, the service requests are offloaded to the roadside units (RSUs) via wireless network, reducing the service latency and enhancing the utilization of resources of RSUs. However, the high mobility of vehicles leads to the frequent switching of services, decreasing the quality of service, which fails to meet the requirements of latency-sensitive vehicular services. In this paper, we proposed a latency-aware service migration method with decision theory, named LSMD. Specifically, we first model the network architecture and introduce the transmission and computation of the service requests in detail. Then, considering the high mobility of vehicles, we analyze the dynamic change of vehicle locations and transform the service migration problem into an uncertain decision optimization problem. Afterward, we find the optimal service migration strategy with the objectives of minimizing the service latency and balancing the workload on RSUs. Finally, numerical experiment results on real-world datasets demonstrate that our method outperforms the other two baselines. [ABSTRACT FROM AUTHOR]

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real‐time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts. This article is categorized under:Algorithmic Development > Ensemble MethodsApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Big Data Mining [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]

In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%. [ABSTRACT FROM AUTHOR]

In the present paper, we propose a new approach on 'distributed systems': the processes are represented through total orders and the communications are characterized by means of biorders. The resulting distributed systems capture situations met in various fields (such as computer science, economics and decision theory). We investigate questions associated to the numerical representability of order structures, relating concepts of economics and computing to each other. The concept of 'quasi-finite partial orders' is introduced as a finite family of chains with a communication between them. The representability of this kind of structure is studied, achieving a construction method for a finite (continuous) Richter–Peleg multi-utility representation. [ABSTRACT FROM AUTHOR]

This paper is concerned with investigating the adequacy of using the p-value as an estimator for the set specified by the null hypothesis in the Exponential distribution. It is shown that the p-value is an admissible estimator in the one-sided test of the location parameter. When the one-sided test of the scale parameter is considered, the p-value is found to be a generalized Bayes estimator with infinite Bayes risk. However, it is very difficult to find an estimator that dominates it. When the parameter space is restricted, the modified p-value is an admissible estimator in the one-sided test of the scale parameter and performs better than the usual p-value. Although the usual p-value is generally inadmissible in the two-sided test, it can be useful as an estimator in this type of test for the scale parameter. [ABSTRACT FROM AUTHOR]

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]

In the development of innovative and high-performance products, design expertise is a critical factor. Nevertheless, novel manufacturing processes often frequently lack an accessible comprehensive knowledge base for product developers. To tackle this deficiency in the context of emerging additive manufacturing processes, substantial design knowledge has already been established. However, novel additive manufacturing processes like continuous fiber-reinforced material extrusion have often been disregarded, complicating the process's wider dissemination. The importance of design knowledge availability is paramount, as well as the need for user-friendly design knowledge preparation, standardized structure, and methodological support for accessing the accumulated knowledge with precision. In this paper, we present an approach that provides formalized opportunistic and restrictive design knowledge, ensuring both the comprehensive exploitation of process-specific potentials and the consideration of restrictive limitations in the construction of components. Opportunistic knowledge, presented as principle cards, is systematically derived, prepared, and made accessible. Moreover, an access system is developed to ensure the comprehensive utilization of process-specific potentials throughout the development process. Furthermore, we propose linking these principles through a synergy and conflict matrix, aiming to consider synergistic principles and identify potential conflicts at an early stage. Additionally, an approach to provide restrictive design knowledge in the form of a design rule catalog is proposed. The application of the knowledge system is demonstrated exemplarily using a weight-optimized component. [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]

Simple Summary: Cells continually sense and receive signals from the environment and respond accordingly. Due to biological noise, however, the response is not always as expected. Such a response can induce a different cell fate and may disrupt some cellular functions. In the presence of noise, cells may either mistakenly perceive non-existent signals and act accordingly, or may ignore the actual signals and do nothing. We label these two as false alarm and signal miss events, respectively. In this paper, we consider an important signaling system with one input and two outputs to show how the likelihood of false alarm and signal miss events can be computed, using the experimentally measured joint response of the two outputs of the signaling system. The two system outputs are the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2), whereas the system input is the tumor necrosis factor (TNF). These molecules are highly involved in essential processes such as cell survival, cell death, and viral replication. The introduced methodology and the measured false alarm and miss probabilities using experimental data can model complex cellular decision-making processes and provide insight into how they may contribute to the development of some pathological conditions. A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions. [ABSTRACT FROM AUTHOR]

Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example. [ABSTRACT FROM AUTHOR]

The paper discusses a possible way of justifying the existence of a multiplicity of cryptomorphic axiomatic theories. It is argued that what renders this multiplicity inevitable are the applications of the theory. They give rise to difficulties whose solution necessitates the application of different conceptual tools. The paper is organized as follows: (§1) introduces the concept of cryptomorphism and the canonic example for the phenomenon: matroid theory; (§2) discusses five cryptomorphic approaches to the definition of rationality in the framework of decision theory: through utility functions, weak orders, choice operators, layered permutations, and pop-stack sortability; (§3) shows that each of these different approaches can serve as a basis for the introduction of a different modification of the original theory. This establishes the heuristic value of cryptomorphic approaches in the domain of decision theory. [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]

On-farm experimentation to guide fertilizer recommendations is a potential precision agriculture tool. There is, however, no agreement on the optimal way to conduct on-farm experimentation, which motivated this paper. Optimal on-farm experimentation is addressed using fully Bayesian decision theory. Monte Carlo integration was used, assuming a linear stochastic plateau model with spatially correlated plateau parameters. The spatially varying coefficient model was used to guide the application of site-specific nitrogen. For the Monte Carlo simulation, the true economic optimal nitrogen value was held constant in each plot. Of the designs considered, experimenting on 15 out of 100 plots of a field with treatment levels that were wider than the true optimal levels and with fewer plots at the lowest nitrogen level maximized the farmers' profit over several years. The third year was the best time to quit experimenting. Much current work experiments on the whole field. With a spatially varying coefficient model, much information is gained without experimenting on every plot. The recommended approach is thus to experiment on a few plots scattered throughout the field. [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]

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]

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 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]

The relationship between decision theory and the theory of natural selection in evolutionary biology offers a fertile ground for philosophical inquiry. A topic that has recently been addressed in the philosophical literature is the connection between decision-theoretic and biological discussions of risk. The paper adds to this literature by drawing attention to a distinction between two different notions of risk originating in the economic literature and by exploring their relationship in a biological context. More specifically, the paper shows that the two notions of risk can part ways in models of risk-sensitive foraging theory. The paper also draws attention to an important difference in contemporary explanations of the apparent lack of empirical success of rational choice theory and risk-sensitive foraging theory. [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]

Frank Ramsey's philosophy of science is considered abstruse due to the incompleteness and difficulty of his paper "Theories". This has not prevented various authors from arguing that Ramsey is committed to meaning holism for scientific theories, and that his philosophy of science is anti-realist but anti-reductionist. However, it is unclear exactly how meaning holism works for Ramsey, and how he can be both anti-realist and anti-reductionist. I argue that clarity can be gained on both issues by examining Ramsey's philosophy of science through a reconstruction of his decision theory compatible with his later philosophical beliefs. I develop an account of how credences can be formed over singular, theoretical propositions despite those propositions being fictions. Credences are ultimately measured by preferences over conditionals whose antecedents are the verification conditions of theoretical propositions and outcomes are elements of a privileged partition on an agent's possibility space induced by the language of the theory. Those verification conditions are the observational elements formed from the unions of this induced partition. Meaning holism is explained as the sensitivity of theoretical propositions to their verification conditions. And anti-realism and anti-reductionism can be maintained due to theoretical propositions forming a finer partition of possibility space than observational propositions, which prevents the former from being truth-functions of the latter. [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]

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]

First-price sealed-bid auctions have been modeled using a utility function that takes into account bidders' regret in case of winning or losing the auctioned item. However, that modeling does not consider bidders' wealth which is an important determinant of bidders' behavior in these auctions. In this paper, we apply an adversarial risk analysis approach for these auctions and find solutions using non-strategic play and level-k thinking solution concepts assuming n bidders. We define new regret parameters and a modified utility function to incorporate the effect of bidders' wealth on their bidding behavior. In our modeling, we assume that the auctioned item is a normal item and has a reserve price. We give numerical examples to illustrate our methodology for each solution concept. [ABSTRACT FROM AUTHOR]

Orthodox decision theory is fanatical in the way it treats small probabilities of enormous value, if unbounded utility functions are allowed. Some have suggested a fix, Nicolausian discounting, according to which outcomes with small enough probabilities should be ignored when making decisions. However, there are lotteries involving only small-probability outcomes, none of which should intuitively be ignored. So the Nicolausian discounter needs a procedure for distinguishing the problematic cases of small-probability outcomes from the unproblematic ones. In this paper, I present a dilemma for Nicolausian discounting: the view must be coupled either with a procedure that delivers fanatical verdicts of its own, as bad as those of orthodox decision theory, or with one that entails intransitive cyclic preferences. [ABSTRACT FROM AUTHOR]

In this paper I propose and motivate a logic of the interdefined concepts of making true and control, understood as intensional propositional operators to be indexed to an agent. While bearing a resemblance to earlier logics in the tradition, the motivations, semantics, and object language theory differ on crucial points. Applying this logic to widespread formal theories of agency, I use it as a framework to argue against the ubiquitous assumption that the strongest actions or options available to a given agent must always be pairwise incompatible. The conclusion is that this assumption conflicts with failures of higher order control of agents over their degree or precision of control, failures exhibited by such imperfect agents as ourselves. I discuss models in this setting for understanding such imperfectly self-controlling agents. In an appendix, I prove several relevant results about the logic described, including soundness and completeness both for it and for certain natural extensions. [ABSTRACT FROM AUTHOR]

• A new simulation methodology, intended bounded rationality methodology, is proposed. • Evaluation cost-benefit of different aspects of decision support techniques. • Classification of errors that a decision support technique can generate is proposed. • Consistent erroneous judgments and inconsistent correct judgments are possible. • A case where consistency condition in AHP provides maximum 5% of the expected return. Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as "expert", little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered. [ABSTRACT FROM AUTHOR]

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]

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]

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations. • This paper proposes a bivariate intensity measure to estimate the seismic fragility of buildings. • An optimized hybrid vulnerability matrix and surface of typical buildings were established. • The 2008 and 2009 Dachaidan and Haixi earthquakes in Qinghai Province, China, were reported. [ABSTRACT FROM AUTHOR]

A limitation of the common measures of diagnostic test performance, such as sensitivity and specificity, is that they do not consider the relative importance of false negative and false positive test results, which are likely to have different clinical consequences. Therefore, the use of classification or prediction measures alone to compare diagnostic tests or biomarkers can be inconclusive for clinicians. Comparing tests on net benefit can be more conclusive because clinical consequences of misdiagnoses are considered. The literature suggested evaluating the binary diagnostic tests based on net benefit, but did not consider diagnostic tests that classify more than two disease states, e.g., stroke subtype (large-artery atherosclerosis, cardioembolism, small-vessel occlusion, stroke of other determined etiology, stroke of undetermined etiology), skin lesion subtype, breast cancer subtypes (benign, mass, calcification, architectural distortion, etc.), METAVIR liver fibrosis state (F0- F4), histopathological classification of cervical intraepithelial neoplasia (CIN), prostate Gleason grade, brain injury (intracranial hemorrhage, mass effect, midline shift, cranial fracture). Other diseases have more than two stages, such as Alzheimer's disease (dementia due to Alzheimer's disease, mild cognitive disability (MCI) due to Alzheimer's disease, and preclinical presymptomatics due to Alzheimer's disease). In diseases with more than two states, the benefits and risks may vary between states. This paper extends the net-benefit approach of evaluating binary diagnostic tests to multi-state clinical conditions to rule-in or rule-out a clinical condition based on adverse consequences of work-up delay (due to false negative test result) and unnecessary workup (due to false positive test result). We demonstrate our approach with numerical examples and real data. [ABSTRACT FROM AUTHOR]

The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence. [ABSTRACT FROM AUTHOR]

In this paper, a three-level framework is proposed to determine the optimal scheduling of a Multi-Area Active Distribution Network in the presence of inter-area Soft Open Points (SOPs). In this framework, the uncertainty of renewable generation and forecasted demand is modeled using information gap decision theory in a risk-averse manner. Coordinated scheduling of Controllable Distributed Generators (CDGs) and SOPs, inter-area energy exchanges and energy trading with upstream network considering the uncertainties are the contribution of the presented method. To improve the computational efficiency and to achieve the optimal solution, the scheduling problem is modeled as a second-order conic programming in which the operational and security constraints of the network, CDG limitations, and operational constraints of SOPs are accurately modeled and the problem is solved by executing CPLEX solver in MATLAB environment. A case study on IEEE 33-bus test system showcases the superiority of the proposed model compared to meta-heuristic algorithms such as particle swarm optimization, genetic algorithm, and gravitational search algorithm. [ABSTRACT FROM AUTHOR]