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The integration of hydrogen vehicles into citizen-oriented energy communities presents a transformative opportunity to enhance energy resilience, sustainability, and democratization. With zero-emission profiles and rapid refueling capabilities, hydrogen vehicles are pivotal in advancing cleaner transportation solutions. However, uncertainties in driving patterns and refueling behaviors pose challenges to their seamless integration and management. This paper proposes a framework based on information gap decision theory (IGDT) to address these uncertainties within community hydrogen parking lots. These parking lots also function as community energy storage systems, utilizing electrolyzers, fuel cells, and hydrogen storage to manage both hydrogen and electrical energy. The approach facilitates energy sharing among prosumers while ensuring thermal comfort within the community. Results show that under a risk-averse strategy, the system tolerates up to 50% variability in travel distances without exceeding cost limits, while a risk-seeking strategy accommodates up to 60% variability at a 50% deviation factor. Proposed Model for Uncertainty Management in a Hydrogen-Integrated Prosumer-Oriented Citizen Energy Community. [Display omitted] • IGDT framework manages uncertainty in hydrogen vehicle travel behavior in CECs. • CECs model includes energy sharing, thermal comfort, and hydrogen parking. • Risk-averse strategy tolerates 50% travel variability without exceeding costs. • Risk-seeking strategy accommodates 60% variability at a 50% deviation factor. • A one-year case study demonstrates the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The IHK program is a general framework in asymptotic decision theory, introduced by Ibragimov and Hasminskii and extended to semimartingales by Kutoyants. The quasi-likelihood analysis (QLA) asserts that a polynomial type large deviation inequality is always valid if the quasi-likelihood random field is asymptotically quadratic and if a key index reflecting the identifiability is non-degenerate. As a result, following the IHK program, the QLA gives a way to inference for various nonlinear stochastic processes. This paper provides a reformed and simplified version of the QLA and improves accessibility to the theory. As an example of the advantages of the scheme, the user can obtain asymptotic properties of the quasi-Bayesian estimator by only verifying non-degeneracy of the key index. [ABSTRACT FROM AUTHOR]

A multistate mechanical device (MSMD) can achieve different functions at different operating states by changing its topological structure and the interaction among its elements. To facilitate conceptual design synthesis of MSMDs, this article reports the development of a prescriptive model based on past empirical studies, implementing it into a web-based software tool, and evaluating its usefulness through design experiments. The objectives include elaborating on an MSMD synthesis task representation scheme, proposing a method for storing and retrieving kinematic building blocks to support initial solution proposals, and introducing modification rules for refining semiworking initial solutions. The prescriptive model guides designers step by step, facilitating the search through a database of building blocks and modification rules. The resulting web-based tool automates this process and allows users to contribute to the database. An MSMD synthesis task example is provided to demonstrate the execution of the prescriptive synthesis process. Finally, an initial evaluation of the tool's usefulness is carried out with the help of design experiments involving external designers. The results obtained from the evaluation study are reported. [ABSTRACT FROM AUTHOR]

With the widespread adoption of renewable energy sources like wind power and photovoltaic (PV) power, uncertainties in the renewable energy output and the battery-swapping demand for electric heavy-duty trucks make it challenging for battery-swapping stations to optimize battery-charging management centrally. Uncoordinated large-scale charging behavior can increase operation costs for battery-swapping stations and even affect the stability of the power grid. To mitigate this, this paper proposes a multi-timescale battery-charging optimization for electric heavy-duty truck battery-swapping stations, taking into account the source–load–storage uncertainty. First, a model incorporating uncertainties in renewable energy output, time-of-use pricing, and grid load fluctuations is developed for the battery-swapping station. Second, based on day-ahead and intra-day timescales, the optimization problem for battery-charging strategies at battery-swapping stations is decomposed into day-ahead and intra-day optimization problems. We propose a day-ahead charging strategy optimization algorithm based on intra-day optimization feedback information-gap decision theory (IGDT) and an improved grasshopper algorithm for intra-day charging strategy optimization. The key contributions include the following: (1) the development of a battery-charging model for electric heavy-duty truck battery-swapping stations that accounts for the uncertainty in the power output of energy sources, loads, and storage; (2) the proposal of a day-ahead battery-charging optimization algorithm based on intra-day-optimization feedback information-gap decision theory (IGDT), which allows for dynamic adjustment of risk preferences; (3) the proposal of an intra-day battery-charging optimization algorithm based on an improved grasshopper optimization algorithm, which enhances algorithm convergence speed and stability, avoiding local optima. Finally, simulation comparisons confirm the success of the proposed approach. The simulation results demonstrate that the proposed method reduces charging costs by 4.26% and 6.03% compared with the two baseline algorithms, respectively, and improves grid stability, highlighting its effectiveness for managing battery-swapping stations under uncertainty. [ABSTRACT FROM AUTHOR]

High costs, technical difficulties, and policy uncertainties are the main challenges in carbon capture technology investments. Therefore, innovative financial products are required to develop projects that overcome these difficulties. Some issues must be considered when developing innovative financial products. An important problem in this process is that these features cannot possibly exist together in the new financial product, because each of these features incurs some costs. Therefore, identifying the most important features of innovative financial products is necessary. Accordingly, this study develops a new and innovative financial product to increase the effectiveness of investments in carbon capture technologies. For this purpose, a novel artificial intelligence (AI)-based fuzzy decision-making model is constructed. First, the weights of the experts were calculated by considering AI methodology. Second, the factors affecting investment in carbon capture technologies were weighted using a spherical fuzzy DEMATEL. Finally, the financial features required for investments were ranked using the spherical fuzzy ARAS method. This study's main contribution is its creation of a novel fuzzy decision-making model by integrating AI methodology with fuzzy decision-making theory. In this process, the weights of the experts are calculated using an AI approach. It is concluded that cost-effectiveness must be prioritized in the development of new financial products. Technological competence is another aspect that should be considered in this process. However, innovative financial products should include risk management and flexible financing. [ABSTRACT FROM AUTHOR]

The study aims to explore the factors that influence university students' behavioral intention (BI) and use behavior (UB) of generative AI products from an ethical perspective. Referring to ethical decision-making theory, the research model extends the UTAUT2 model with three influencing factors: ethical awareness (EA), perceived ethical risks (PER), and AI ethical anxiety (AIEA). A sample of 226 university students was analysed using the Partial Least Squares Structural Equation Modelling technique (PLS-SEM). The research results further validate the effectiveness of UTAUT2. Furthermore, performance expectancy, hedonistic motivation, price value, and social influence all positively influence university students' BI to use generative AI products, except for effort expectancy. Facilitating conditions and habit show no significant impact on BI, but they can determine UB. The three extended factors from the ethical perspective play significant roles as well. AIEA and PER are not key determinants of BI. However, AIEA can directly inhibit UB. From the mediation analysis, although PER do not have a direct impact on UB, it inhibits UB indirectly through AIEA. Ethical awareness can positively influence BI. Nevertheless, it can also increase PER. These findings can help university students better accept and ethically use generative AI products. [ABSTRACT FROM AUTHOR]

To counter the escalating threat of direct conflict with rogue nations, the use of sanctions packages has become a preferred tactical response. However, although targeted, there are significantly elevated spillover effects that can generate sectoral damage. While the literature on sanctions has focused on analyzing the effectiveness and the impact on the sanctioned and sanctioning country, spillover effects have not been addressed in the tourism and travel industry. Based on behavioral decision theory and modern portfolio theory, this study states hypotheses and confronts two potential results derived from each theory regarding the way negative consequences of sanctions spill over into airlines. Using the aviation sanctions packages derived from the Russia-Ukraine war in 2022 in different regions worldwide, the results indicate greater spillovers flow into airlines than other aviation-related corporations, into big firms than small firms, and with significant differential effects on each analyzed region. [ABSTRACT FROM AUTHOR]

We can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified. We seek such a justification. We begin with decision models, which map actions to distributions over outcomes but avoid additional causal assumptions. We then examine assumptions that could justify causal interventions, with a focus on symmetry. First, we introduce conditionally independent and identical responses (CIIR), a generalisation of the IID assumption to decision models. CIIR justifies identifying actions with interventions, but is often an implausible assumption. We consider an alternative: precedent is the assumption that "what I can do has been done before, and its consequences observed," and is generally more plausible than CIIR. We show that precedent together with independence of causal mechanisms (ICM) and an observed conditional independence can justify identifying actions with causal interventions. ICM has been proposed as an alternative foundation for causal modelling, but this work suggests that it may in fact justify the interventional interpretation of causal models. [ABSTRACT FROM AUTHOR]

Regarding the traditional lane-changing decision theory around the vehicle's intention to change lanes, less consideration of the behavioral interactions between vehicles, and the personalized driving preferences of different drivers, this paper proposes a dynamic game for a lane-changing decision-making method that considers human-like driving preferences. First, to match the multiperformance evaluation requirements in the lane-changing process of intelligent vehicles, a cost function including driving space, traffic efficiency, and driving comfort is constructed. Second, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) methods are used to conduct subjective and objective analyses on the next generation simulation (NGSIM) traffic data set to obtain the weight coefficients of multiple performance indicators of human-like driving preferences. The effects of different driving behaviors on lane-changing intentions and performance indexes are also studied. Finally, fuzzy control theory and intelligent driver model (IDM) are used to predict the driving behavior of interacting vehicles in the target lane, and the master-slave dynamic game theory and the particle swarm optimization algorithm are used to realize the behavioral interaction between the main vehicle and surrounding vehicles and to make the optimal lane-changing decisions. The research results show that the dynamic game lane-changing decision-making method of intelligent vehicles as proposed in this paper, which considers human-like driving preferences, can effectively meet the personalized requirements of different driving behaviors on driving space and traffic efficiency in the process of lane changing and improve the safety of intelligent vehicle lane-changing driving. Practical Applications: The lane change behavior strategy of intelligent vehicle is an important component of intelligent driving technology. Accurately identifying the driving style and uncertainty factors of surrounding vehicles and making corresponding lane-change decisions to ensure the driving safety of drivers are of great significance. Based on this, this paper proposes a dynamic game lane-change decision method considering human-like driving preferences. First, the lane-changing vehicle decision model is constructed from driving space, driving efficiency, and driving comfort. Second, through the analytic hierarchy process-criteria importance through intercriteria correlation (AHP-CRITIC) method, the weight of multiple performance indicators of human-like driving preferences is obtained from the three dimensions of indicator importance, indicator conflict, and data volatility from subjective and objective perspectives. Finally, based on fuzzy theory, the vehicle driving feature coefficient is obtained by taking the headway, speed coefficient, and acceleration and deceleration speed as the output. The behavior of the surrounding vehicles is predicted by the vehicle driving feature coefficient and intelligent driver model (IDM), and the lane-change decision is optimized by the prediction information of the surrounding vehicles, and the lane-change decision information is finally output. The lane-change decision method proposed in this paper considering human-like driving preferences can help intelligent vehicles realize multiperformance index evaluation demand analysis and lane-change interaction behavior research. [ABSTRACT FROM AUTHOR]

Neutrosophic sets are effectual logic represented to understand ambiguous and inconsistent information. They are frequently used to explain many types of partial or incomplete information. Researchers have given much attention to the decision-making theory and its associated methodologies based on uncertain linguistic factors. This article emphasizes the novel neutrosophic number approximations to handle linguistic variables and their application in multiple-attribute decision-making. Different approximation techniques are introduced in neutrosophic sets, but substantial data loss may occur. Hence, a hexagonal neutrosophic number was proposed to deal with information loss during approximation. Also, the comparison study with existing techniques is explored to show the effectiveness of the proposed approximation. The expected interval criterion was retained although an approximation was made to give more desirable features. An MCDM (Multi-Criteria Decision Making) problem is presented to demonstrate efficiency and simplicity with uncertain parameters. [ABSTRACT FROM AUTHOR]

In this paper, a robust fuzzy multi-objective framework is performed to optimize the dispersed and hybrid renewable photovoltaic-wind energy resources in a radial distribution network considering uncertainties of renewable generation and network demand. A novel multi-objective improved gradient-based optimizer (MOIGBO) enhanced with Rosenbrock's direct rotational technique to overcome premature convergence is proposed to determine the problem optimal decision variables. The deterministic optimization framework without uncertainty minimizes active energy loss, unmet customer energy, and renewable generation costs. The study also examines the impact of dispersed and hybrid renewable resources on solving the problem. In the robust optimization framework considering the deterministic obtained results, the focus is on determining the maximum uncertainty radius (MUR) of renewable resource generation and network demand based on the uncertainty risk. The MURs and system robustness are optimally determined using information gap decision theory (IGDT) and the MOIGBO, considering various uncertainty budgets under worst-case scenarios. The deterministic results indicate that the MOIGBO effectively balances the objectives and identifies the final solution within the Pareto front, according to fuzzy decision-making. The results also reveal that the dispersed case yields better objective values than the hybrid case. Furthermore, the MOIGBO outperforms MOGBO and multi-objective particle swarm optimization (MOPSO) in improving distribution network operations. The robust results show that maximum system robustness is achieved at 30% uncertainty risk due to forecasting errors, with MUR values of 0.54% for resource production and 12.56% for load demand. [ABSTRACT FROM AUTHOR]

The main conclusions of two classical theories of justice can be represented as solutions to a decision making problem. Rawls' second principle of justice is a result of applying the maximin criterion for decision-making behind the 'veil of ignorance'. The utilitarian principle of maximizing average utility, meanwhile, can be traced back to applying the Laplace criterion of decision making behind the veil of ignorance. This article makes explicit the different assumptions about preference measurement and assumptions about interpersonal comparisons of utility that need to be made to use either of these principles. Once these assumptions are made explicit, it becomes clear that the choice between theories of justice cannot be exclusively settled by an exchange of arguments for one kind of distributional arrangement over the other, but is also a question of what kind of measurement assumptions are more plausible in a given context. [ABSTRACT FROM AUTHOR]

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This paper presented a risk analysis using information gap decision theory (IGDT) in risk‐seeking and risk‐averse approaches to invest in the construction of virtual power plants (VPPs), considering the uncertainties of wind speed, solar radiation, and load demand. The construction of VPP has financial and operational risks, such as investment costs, maintenance, power exchange with upstream grid, unsupplied energy, and combined heat and power (CHP) system fuel for investors in terms of uncertainties related to load demand and various sources of distributed energy generation (distributed energy resources [DERs]). Investing risk analysis on an experimental system, including photovoltaic (PV) and wind, thermal, and combined power plants (CHP), battery energy storage system (BESS), and electric and thermal loads, using a multiobjective function to minimize the total cost of VPP construction in risk‐seeking modes (πo) and risk aversion (πc), was performed. The risk analysis involved 8% reducing/increasing of the total VPP construction investment cost in 2% steps for the risk‐seeking owner (ρ)/risk‐averse owner (σ). The results of this article showed that despite high uncertainty in the studied VPP, it is still possible to make a decision for risk‐seeking and risk‐averse investors to build VPP. In the risk‐averse mode, due to the increase in cost, there is more flexibility in choosing and using equipment, as well as in determining its amount. On the contrary, in risk‐seeking mode, VPP designer should determine the smallest radius of uncertainty (αwind, αpv, and αload) in ideal conditions, because to formulate a suitable investment proposal to use equipment and their quantity in a way that leads to cost savings is vital. The key numerical results include the following: (1) The base investment cost is 18,731.2 monetary units; (2) a risk‐averse scenario achieves a critical cost of 20,229.7 monetary units at a risk tolerance of σ = 0.08; (3) for a risk‐seeking scenario, the critical cost is 17,232.7 monetary units with a risk parameter ρ = 0.08; (4) in the risk‐averse case, optimal robustness ensures critical cost πc = 17,232.7 monetary units at risk parameter σ = 0.06 assurance at an uncertainty radius where αwind = 2.353%, αpv = 1.176%, and αload = 14.118%; (5) in the risk‐seeking case, optimal opportunity ensures critical cost πo = 17607.3 monetary units at risk parameter ρ = 0.06 assurance at an uncertainty radius where αwind = 2.353%, αpv = 17.647%, and αload = 18.824%; and (6) the harmony search algorithm (HSA) algorithm demonstrated an 18% faster convergence speed compared to genetic algorithm (GA). This approach makes sure VPP design stays strong and full of chances even when things get tough. It is a useful way to handle investment risk and get the best results when there is a lot of uncertainty. Finally, the actual numerical results applying the proved test case prove the introduced approach to be effective. [ABSTRACT FROM AUTHOR]

This paper proposes a robust scheduling approach for electric parking lots (EPLs) integrated with battery storage and wind power sources in distribution networks, aiming to minimize the cost‐to‐revenue function. The method is based on information gap decision theory with a risk aversion strategy (IGDT‐RAS) and takes into account uncertainties in network load and wind power. In deterministic scheduling, decision variables include the location and capacity of the EPLs and wind resources in the network, while in robust scheduling, the maximum uncertainty radius (UR) is determined using an improved flow direction optimization algorithm (IFDA), enhanced by an opposition learning strategy (OLS). The proposed method is applied to the 33‐ and 45‐bus networks. The deterministic approach results in a lower cost‐to‐revenue ratio, reduced energy losses, and improved reliability compared to traditional FDA, whale optimization algorithm (WOA), and particle swarm optimizer. In robust scheduling, for the 33‐bus network, the largest UR for load and wind power is 8.70% and 17.06%, respectively, while for the 45‐bus network, it is 8.45% and 32.36%, respectively. The robustness of the network against the worst‐case uncertainty scenario is demonstrated in the robust scheduling, and the superior performance of IGDT‐RAS over Monte Carlo simulation (MCS) is confirmed in achieving a reliable cost level. [ABSTRACT FROM AUTHOR]

Purpose: Due to the complexity and diversity of megaprojects, the architectural programming process often involves multiple stakeholders, making decision-making difficult and susceptible to subjective factors. This study aims to propose an architectural programming methodology system (APMS) for megaprojects based on group decision-making model to enhance the accuracy and transparency of decision-making, and to facilitate participation and integration among stakeholders. This method allows multiple interest groups to participate in decision-making, gathers various perspectives and opinions, thereby improving the quality and efficiency of architectural programming and promoting the smooth implementation of projects. Design/methodology/approach: This study first clarifies the decision-making subjects, decision objects, and decision methods of APMS based on group decision-making theory and value-based architectural programming methods. Furthermore, the entropy weight method and fuzzy TOPSIS method are employed as calculation methods to comprehensively evaluate decision alternatives and derive optimal decision conclusions. The workflow of APMS consists of four stages: preparation, information, decision, and evaluation, ensuring the scientific and systematic of the decision-making process. Findings: This study conducted field research and empirical analysis on a practical megaproject of a comprehensive transport hub to verify the effectiveness of APMS. The results show that, in terms of both short-distance and long-distance transportation modes, the decision-making results of APMS are largely consistent with the preliminary programming outcomes of the project. However, regarding transfer modes, the APMS decision-making results revealed certain discrepancies between the project's current status and the preliminary programming. Originality/value: APMS addresses the shortcomings in decision accuracy and stakeholder participation and integration in the current field of architectural programming. It not only enhances stakeholder participation and interaction but also considers various opinions and interests comprehensively. Additionally, APMS has significant potential in optimizing project performance, accelerating project processes, and reducing resource waste. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources (RESs) into power systems has introduced new complexities due to the inherent variability and uncertainty of these energy sources. In addition to the uncertainty in RES generation, the demand-side load of power systems is also subject to fluctuations, further complicating system operations. Addressing these challenges requires effective modeling and assessment techniques to quantify and mitigate the risks associated with system uncertainties. This paper evaluates the impact of various uncertainty modeling techniques on power system reliability with wind farm integration. Furthermore, this paper reviews the state of the art of the various uncertainty and risk modeling techniques in power systems. Through a detailed case study, the performance of these techniques in modeling uncertainties of wind speeds is analyzed. Based on the results, the integration of wind turbines improves the system's overall reliability when there is a reduction in conventional power plants (CPPs)' generation, which are dispatchable energy sources providing a stable and flexible supply. However, the generation of wind farms is associated with uncertainty. The results show Monte Carlo simulation combined with the K-Means method is consistently a more accurate uncertainty model for wind speeds, closely aligning with real-case scenarios, compared to other methods such as Markov Chain Monte Carlo (MCMC), robust optimization (RO), and information-gap decision theory (IGDT). [ABSTRACT FROM AUTHOR]

This study proposes an agent-based quantum-like model to investigate the individual selection among three or more lotteries while incorporating the decision-making risk and uncertainty. We extend the classical expected utility functions with quantum probabilities and construct the compound belief state to compare one specific lottery belief state against others. The involved decision-making process is represented formally by the comparison operator, which can be decomposed into a few subprocesses. We give an example of individual lottery selection from three lotteries to illustrate the model. Finally, we propose ways to select from more than three lotteries. [ABSTRACT FROM AUTHOR]

Introduction: The career choices of medical graduates vary widely between medical schools in the UK and elsewhere and are generally not well matched with societal needs. Research has found that experiences in medical school including formal, informal and hidden curricula are important influences. We conducted a realist evaluation of how and why these various social conditions in medical school influence career thinking. Methods: We interviewed junior doctors at the point of applying for speciality training. We selected purposively for a range of career choices. Participants were asked to describe points during their medical training when they had considered career options and how their thinking had been influenced by their context. Interview transcripts were coded for context-mechanism-outcome (CMO) configurations to test initial theories of how career decisions are made. Results: A total of 26 junior doctors from 12 UK medical schools participated. We found 14 recurring CMO configurations in the data which explained influences on career choice occurring during medical school. Discussion: Our initial theories about career decision-making were refined as follows: It involves a process of testing for fit of potential careers. This process is asymmetric with multiple experiences needed before deciding a career fits ('easing in') but sometimes only a single negative experience needed for a choice to be ruled out. Developing a preference for a speciality aligns with Person-Environment-Fit decision theories. Ruling out a potential career can however be a less thought-through process than rationality-based decision theories would suggest. Testing for fit is facilitated by longer and more authentic undergraduate placements, allocation of and successful completion of tasks, being treated as part of the team and enthusiastic role models. Informal career guidance is more influential than formal. We suggest some implications for medical school programmes. [ABSTRACT FROM AUTHOR]

Generative Bayesian Computation (GBC) methods are developed to provide an efficient computational solution for maximum expected utility (MEU). We propose a density-free generative method based on quantiles that naturally calculates expected utility as a marginal of posterior quantiles. Our approach uses a deep quantile neural estimator to directly simulate distributional utilities. Generative methods only assume the ability to simulate from the model and parameters and as such are likelihood-free. A large training dataset is generated from parameters, data and a base distribution. Then, a supervised learning problem is solved as a non-parametric regression of generative utilities on outputs and base distribution. We propose the use of deep quantile neural networks. Our method has a number of computational advantages, primarily being density-free and an efficient estimator of expected utility. A link with the dual theory of expected utility and risk taking is also described. To illustrate our methodology, we solve an optimal portfolio allocation problem with Bayesian learning and power utility (also known as the fractional Kelly criterion). Finally, we conclude with directions for future research. [ABSTRACT FROM AUTHOR]

A game problem of structural design is defined as a problem of playing against external circumstances. There are two classes of players, namely the "ordinal" and "cardinal" players. The ordinal players, designated as the "operator" and "nature", endeavor to, respectively, minimize or maximize the payoff function, operating within the constraints of limited resources. The fundamental premise of this study is that the action of player "nature" is a priori unknown. Statistical decision theory addresses decision-making scenarios where these probabilities, whether or not they are known, must be considered. The solution to the substratum game is expressed as a value of the game "against nature". The structural optimization extension of the game considers the value of the game "against nature" as the function of certain parameters. Thus, the value of the game is contingent upon the design parameters. The cardinal players, "designers", choose the design parameters. There are two formulations of optimization. For the single cardinal player, the pursuit of the maximum and minimum values of the game reduces the problem of optimal design. In the second formulation, there are multiple cardinal players with conflicting objectives. Accordingly, the superstratum game emerges, which addresses the interests of the superstratum players. Finally, the optimal design problems for games with closed forms are presented. The game formulations could be applied for optimal design with uncertain loading, considering "nature" as the source of uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly "human AI". This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. [ABSTRACT FROM AUTHOR]

In linguistic decision theory, multi-attribute group decision-making (MAGDM) of linguistic neutrosophic numbers (LNNs) is one of the crucial research topics. Existing LNN aggregation algorithms do not consider trigonometric periodicity. In this case, they cannot perform MAGDM problems including periodic/multitemporal applications in LNN scenarios. To fill this gap, this article aims to present a MAGDM technique using the LNN tangent Dombi aggregation operators (TDAOs) for addressing MAGDM issues including multitemporal/periodic applications in LNN scenarios. First, we present the linguistic tangent Dombi t-norm and t-conorm and the LNN tangent Dombi operation laws. Second, we propose the LNN tangent Dombi weighted average and geometric operators and investigate their properties. Third, a MAGDM technique is built based on the two presented operators to tackle MAGDM problems in a LNN scenario. Lastly, the built MAGDM technique is applied to a choice of new energy design schemes in Ningbo City, China, and then its efficiency and appropriateness are verified by comparing with the existing MAGDM technique in the LNN scenario. [ABSTRACT FROM AUTHOR]

In the present work, we have investigated the problem of estimating the parameters of several exponential distributions with ordered scale parameters under linex loss function. The study includes the cases of known as well as unknown location parameters. For both cases, we have considered class of equivariant estimators. Then, sufficient conditions have been obtained under which the equivariant class of estimators improves upon the usual estimators. Using the established results, it has been shown that the restricted maximum likelihood estimator is inadmissible. Finally, for both cases, simulation study has been conducted to compare the risk performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

Decision makers (DMs) are often viewed as autonomous in the majority of multicriteria group decision making (MCGDM) situations, and their psychological behaviors are seldom taken into account. Once more, we are unable to prevent both positive and negative flows of varying alternative preferences due to the nature of attributes or criteria in complicated decision-making problems. However, DMs' perspectives are likely to affect one another in complicated MCGDM issues, and they frequently use subjective limited rationality while making decisions. The multicriteria quantum decision theory-based group decision making integrating the TODIM-PROMETHEE II strategy under linguistic Z-numbers (LZNs) is designed to overcome the aforementioned problems. In our established technique, the PROMETHEE II controls the positive and negative flows of distinct alternative preferences, the TODIM method manages the experts' personal regrets over a criterion, and the quantum probability theory (QPT) addresses human cognition and behavior. Because LZNs can convey linguistic judgment and trustworthiness, we provide expert LZNs for their viewpoints in this work. We determine the criterion weights for each expert after first obtaining their respective expert weights. Second, to represent the limited rational behaviors of the DMs, the TODIM-PROMETHEE II approach is introduced. It is employed to determine each alternative's dominance in both positive and negative flows. Third, a framework for quantum possibilistic aggregation is developed to investigate the effects of interference between the views of DMs. The views of DMs are seen in this procedure as synchronously occurring wave functions that affect the overall outcome by interfering with one another. The model's efficacy is then assessed by a selection of renewable energy case studies, sensitive analysis, comparative analysis, and debate. [ABSTRACT FROM AUTHOR]

This commentary seeks to improve the design and analysis of trials undertaken to obtain approval of drugs for treatment of rare diseases. Methodological analysis reveals that use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Hypothesis testing is inattentive to the relative magnitudes of losses to patient welfare when types 1 and 2 errors occur. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. Rarity of a disease makes it difficult to enroll the number of trial subjects needed to meet the statistical power standards for drug approval. Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judging sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. Using nearness to optimality to set sample size and making approval decisions to minimize distance from optimality would particularly benefit the evaluation of drugs for treatment of rare diseases. It would enable a dramatic reduction in sample size relative to current norms, without compromising the clinical informativeness of trials. • The use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. • Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judge sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. [ABSTRACT FROM AUTHOR]

A regression model of predictor trade-offs is described. Each regression parameter equals the expected change in Y obtained by trading 1 point from one predictor to a second predictor. The model applies to predictor variables that sum to a constant T for all observations; for example, proportions summing to T = 1.0 or percentages summing to T = 100 for each observation. If predictor variables sum to a constant T for all observations and if a least squares solution exists, the predicted values for the criterion variable Y will be uniquely determined, but there will be an infinite set of linear regression weights and the familiar interpretation of regression weights does not apply. However, the regression weights are determined up to an additive constant and thus differences in regression weights β v − β v ∗ are uniquely determined, readily estimable, and interpretable. β v − β v ∗ is the expected increase in Y given a transfer of 1 point from variable v∗ to variable v. The model is applied to multiple-choice test items that have four response categories, one correct and three incorrect. Results indicate that the expected outcome depends, not just on the student's number of correct answers, but also on how the student's incorrect responses are distributed over the three incorrect response types. A regression model of predictor trade-offs is described. Each regression parameter equals the expected change in Y obtained by trading 1 point from one predictor to a second predictor. The model applies to predictor variables that sum to a constant T for all observations; for example, proportions summing to T = 1.0 or percentages summing to T = 100 for each observation. The model is designed for the study of decisions involving trade-offs, compositional variables, and contrasts between pairs of predictor coefficients. [ABSTRACT FROM AUTHOR]

Models of content-sharing behavior on online social media platforms typically represent content spread as a diffusion process modeled on contagious diseases; users' behavior is modeled with single-agent decision theory. However, social media platforms are interactive spaces where users care about reactions to, and further spread of, the content they post. Thus, social media interaction falls under the intended use cases for game theory. In contrast to existing models leaving strategic reasoning out, we capture agents' social media decisions within a cognitive hierarchy framework, which can be interpreted as making formally precise how agents make strategic choices based on mutual expectations of rationality. Analytically, we identify limit cases in which a platform can be swamped with content that no agents personally like but all expect to elicit reactions (think obvious fake-news). We then use agent-based simulations to show that a range of more realistic cases give rise to similar outcomes. [ABSTRACT FROM AUTHOR]

This paper examines problems in decision theory where the number of alternatives and states of nature are finite. Previous studies have defined the concept of "the value of perfect information for the problem" (VPIP). This metric allows us to obtain an upper bound on the amount a decision-maker would be willing to pay for perfect information under the specific conditions of a problem. This bound is particularly important when the decision is unrepeatable, providing a more accurately adjusted measure than the one traditionally obtained with "the expected value of perfect information" (EVPI). Supported by linear programming, this work proposes a sensitivity analysis of these bounds by seeking to identify the intervals in which the problem values can vary without essentially modifying the structure of the problem. Specifically, the study aims to determine how this variation might affect the EVPI and VPIP bounds, as well as the difference in the price a decision-maker would be willing to pay for perfect information if any of the problem values were altered. By identifying alternatives and scenarios taking into account the role they play in the problem, this research classifies the data involved in a finite decision problem to ensure the conclusions can be understood as generally as possible. Although the proposed sensitivity analysis is applied to the oil-drilling problem, a classic in decision theory, the conclusions of this work have potential applications in improving environmental decision-making processes. [ABSTRACT FROM AUTHOR]

In 1961 Daniel Ellsberg published an article titled "Risk, Ambiguity, and the Savage Axioms" in the Quarterly Journal of Economics, which became a seminal contribution to the theory of decision-making under uncertainty. This paper analyzes Ellsberg's 1961 classic, situates it within the context of decision-making theory in the 1950s and early 1960s and within the development of Ellsberg's ideas, and provides an overview of the experimental and theoretical literature to which it gave rise. [ABSTRACT FROM AUTHOR]

In the case of perishable products whose demand is time-sensitive, the decisions on order and sale policies have to be appropriately made to minimize the costs of product decay. The markdown policy, widely adopted by retailers, is a well-known sales policy that effectively manages the revenue and cost of perishable inventories. A significant decision in this policy is to determine an optimal time to mark down the price and boost demand. Moreover, trade credits serve as short-term financing through which the seller sets a deadline for the buyer to pay for the purchased products. Once given a trade credit, the buyer is enabled to increase the order and make a better profit out of sales before meeting the credit deadline. If the optimal quantity of the order exceeds the capacity of the retailer's warehouse, another warehouse should be rented. In this context, the present study aims to simultaneously examine markdown and credit decisions for perishable products in a two-warehouse inventory model. For this purpose, a set of theorems is used to obtain optimal sales and purchase decisions under integrated decision-making, and the proposed model is solved with numerical examples. Finally, sensitivity analyses are conducted to evaluate the effects of parameter variations on decision variables. The numerical results demonstrate the significance of a combination of credit financing and markdown policies for perishable inventories. It has also been shown that time-diminishing demand is beneficial for supply chains. The proposed model can help managers make optimal decisions on trade credit and markdown policies. [ABSTRACT FROM AUTHOR]

This study applies Info-Gap Decision Theory (IGDT) to manage uncertainties in early-stage lifecycle assessment (LCA) in the building sector, focusing on carbon emissions and cost optimization. The building industry significantly contributes to global carbon emissions, making robust LCA models crucial for achieving environmental improvements. Traditional LCA methods often overlook deep uncertainties, leading to unreliable outcomes. To address this, this research integrates IGDT, providing a non-probabilistic approach that enhances decision-making under uncertainty. The study develops an optimization model that considers uncertainties in material choices, supplier selection, and transportation logistics, demonstrated through a case study of a Science and Technology Expo Pavilion in Chongqing, China. The results show that manufacturing processes are the main source of carbon emissions, with transportation having a smaller but notable impact. Significant emission reductions can be achieved by using alternative materials like fly ash and volcanic ash in cement production. Strategic supplier selection, based on the cost per ton of CO2 reduction, balances environmental impact with economic feasibility. IGDT provides a robust framework for managing uncertainty, helping building projects to achieve sustainability targets even under deep uncertainty, thereby supporting the industry's efforts towards net-zero emissions. [ABSTRACT FROM AUTHOR]

Several philosophers have proposed Knowledge-Based Decision Theories (KDTs)—theories that require agents to maximize expected utility as yielded by utility and probability functions that depend on the agent's knowledge. Proponents of KDTs argue that such theories are motivated by Knowledge-Reasons norms that require agents to act only on reasons that they know. However, no formal derivation of KDTs from Knowledge-Reasons norms has been suggested, and it is not clear how such norms justify the particular ways in which KDTs relate knowledge and rational action. In this paper, I suggest a new axiomatic method for justifying KDTs and providing them with stronger normative foundations. I argue that such theories may be derived from constraints on the relation between knowledge and preference, and that these constraints may be evaluated relative to intuitions regarding practical reasoning. To demonstrate this, I offer a representation theorem for a KDT proposed by Hawthorne and Stanley (2008) and briefly evaluate it through its underlying axioms. [ABSTRACT FROM AUTHOR]

Philosophers' two favorite accounts of rational choice, Evidential Decision Theory (EDT) and Causal Decision Theory (CDT), each face a number of serious objections. Especially troubling are the recent charges that these theories are dynamically inconsistent. I note here that, under the epistemic assumptions that validate these charges, every decision theory that satisfies a pair of attractive postulates is doomed to a similar fate and then survey various lessons rational choice theorists might opt to draw from this. [ABSTRACT FROM AUTHOR]

The importance of assessing the sustainability of companies and their activities is increasing. Especially since the implementation of the new Corporate Social Reporting Directive, even more companies are committed to reporting on the impacts of their businesses on sustainability. This is a challenge especially concerning the social dimension of sustainability. Some frameworks present various relevant social criteria that can be used as a basis for assessment. However, these do not provide detailed suggestions for handling the numerous methodological challenges of such a multi-criteria assessment: identification and selection of the relevant stakeholders, categories and indicators to measure the impacts on social sustainability, weighting and aggregation of these criteria, etc. Therefore, this paper contributes to the methodology of social sustainability assessment by presenting a procedure model for this specific assessment task. The novelty of the model results from its foundation by a review of methods for selection, normalisation, weighting and aggregation of social criteria, existing decision theory-based procedure models, as well as a stakeholder-oriented catalogue of criteria. The procedure model is structured hierarchically by subdividing the overall social sustainability-assessment task into different levels: stakeholders, categories, and indicators. Furthermore, appropriate methods are suggested for the single steps of the procedure model. [ABSTRACT FROM AUTHOR]

The underground plastic vertical drains (PVDs) are a significant problem for shield machines in tunneling construction. At present, the main method to deal with PVDs is to manually adjust the parameters of the shield machine. To ensure that a shield machine autonomously recognizes and adjusts the control in soil layers containing PVDs, this study constructs a shield machine advance and rotation state-space model utilizing Bayesian decision theory for the judgment of excavation conditions. A Bayesian model predictive control (Bayes-MPC) method for the shield machine is proposed, followed by a simulation analysis. Finally, a validation experiment is conducted based on a Singapore subway project. Compared with traditional methods, the method proposed in this paper has better performance in the simulation, and it also has demonstrated effectiveness and accuracy in experiments. The research outcomes can provide a reference for the adaptive assistance system of shield machines excavating underground obstacles. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of estimating the larger location parameter of two general location families of distributions from a decision-theoretic perspective. The criteria of minimizing the risk function and the Pitman nearness, under a general bowl-shaped loss function, are considered. Inadmissibility of certain location and permutation equivariant estimators is proved and dominating estimators are obtained. It follows that a natural estimator δ c 0 (a plug-in estimator based on the best location equivariant estimators of (unordered) location parameters) is inadmissible, under certain conditions on underlying densities, and the loss function. A class of dominating estimators is provided. We also consider a class D of linear and, location and permutation equivariant estimators and obtain a subclass D 0 ( ⊆ D )of estimators that are admissible within the class D of estimators. We observe that the natural estimator δ c 0 is a boundary estimator in D 0 . Further, using the IERD technique of Kubokawa (1994), we obtain an estimator dominating over another natural estimator δ b 0 , that is another boundary estimator in D 0 . Additionally, under the generalized Pitman nearness criterion with a general bowl-shaped loss function, we show that two natural estimators are inadmissible and obtain improved estimators. The results are applied to specific loss functions, and explicit expressions for dominating estimators are obtained. We illustrate applications of these results to normal and exponential distributions for specified loss functions. A simulation study is also conducted to compare risk performances of different competing estimators. Finally, we present a real-life data analysis to illustrate a practical application of the findings of the paper. [ABSTRACT FROM AUTHOR]

Decision theory requires agents to assign probabilities to states of the world and utilities to the possible outcomes of different actions. When agents commit to having the probabilities and/or utilities in a decision problem defined by objective features of the world, they may find themselves unable to decide which actions maximize expected utility. Decision theory has long recognized that work-around strategies are available in special cases; this is where dominance reasoning, minimax, and maximin play a role. Here we describe a different work around, wherein a rational decision about one decision problem can be reached by “interpolating” information from another problem that the agent believes has already been rationally solved.

As large language models (LLMs) continue to demonstrate remarkable abilities across various domains, computer scientists are developing methods to understand their cognitive processes, particularly concerning how (and if) LLMs internally represent their beliefs about the world. However, this field currently lacks a unified theoretical foundation to underpin the study of belief in LLMs. This article begins filling this gap by proposing adequacy conditions for a representation in an LLM to count as belief-like. We argue that, while the project of belief measurement in LLMs shares striking features with belief measurement as carried out in decision theory and formal epistemology, it also differs in ways that should change how we measure belief. Thus, drawing from insights in philosophy and contemporary practices of machine learning, we establish four criteria that balance theoretical considerations with practical constraints. Our proposed criteria include accuracy, coherence, uniformity, and use, which together help lay the groundwork for a comprehensive understanding of belief representation in LLMs. We draw on empirical work showing the limitations of using various criteria in isolation to identify belief representations. [ABSTRACT FROM AUTHOR]

This study assesses the moderating effect of dark personality traits on the relationship between the conditioning factors of the theory of planned behavior and auditors' judgment and decision-making. A survey-type study was conducted, and 311 auditors' responses were analyzed using partial least-squares multi-group analysis. The results indicate that dark personality traits negatively impact the relationship between subjective norms, perceived behavioral control, and auditors' judgment and decision-making, suggesting auditors with high dark traits are inclined to insensitivity, impulsiveness, and manipulation. As for the attitude of skepticism, dark personality traits positively impacted the relationship between skepticism and auditors' judgment. Our study contributes by developing a model incorporating the moderating effect of personality traits on the theory of planned behavior, helping auditing firms and regulatory bodies to improve processes and strengthen the reliability of accounting information. [ABSTRACT FROM AUTHOR]

This paper identifies nine factors affecting agricultural sustainable development by reading extensive literature and invites five experts to assess these factors using the pythagorean neutrosophic linguistic variable. Some of these factors can directly reduce the environmental footprint of agriculture, improve soil health, and promote biodiversity, while others can indirectly integrate to support the adoption of sustainable practices, further mitigating environmental degradation. Collectively, these factors reinforce ecological balance and play a critical role in advancing agricultural sustainable development. To unravel the complex relationships among these factors, a novel decision theory model is proposed, integrating pythagorean neutrosophic set (PNS) with Weighted Influence Nonlinear Specification System (WINGS) and Best-Worst Method (BWM). In addition, we not only categorized all factors into cause-and-effect factors, but also constructed a network relationship diagram based on them. The study shows that agricultural modernization (Y6) is the most important factor and land remediation (Y2) is the most influential factor. This integrated approach can more effectively address the common challenges of uncertainty and linguistic ambiguity in decision-making scenarios. Combining PNS with WINGS helps make the interactions and importance of factors more apparent, which is particularly suitable for analyzing key factors that promote agricultural sustainability. The incorporation of BWM further ensures the model's accuracy and objectivity. This method provides a more comprehensive and accurate reflection of decision-makers' opinions and judgments, improving decision-making efficiency, and can be widely applied not only in agriculture but also to other decision-making problems. [ABSTRACT FROM AUTHOR]

The shipping industry is featured by high carbon emissions. The 2023 IMO Strategy on Reduction of GHG Emissions from Ships sets forth the global goals of shipping decarbonization. Shipping decarbonization involves complicated issues of economy, technology, policy and law etc., and implies the conflicts between economic interests and environmental interests, between individual interests and public interests, between individual States' interests and international common interests and between current interests and long-term interests. This research suggests that balancing such conflicting interests need to follow the principle of prioritizing the international public environmental interests while taking into account the other interests because protection of environmental interests should be taken as the basic value orientation in shipping decarbonization governance and the principle of collaborating governmental intervention and market mechanisms by reference to the theory on the relationship between government and market in economics. Under the guidance of these principle, by reference to the equilibrium analysis method in economics and following the progressive decision theory in management, this research demonstrates that the main pathways in achieving such balance may include: making strategic plan and basic policy for reducing GHG emissions from ships by the government, implementing economic incentive policies such as tax incentives and fiscal subsidies, implementing ship energy efficiency measures, prudently implementing shipping carbon emissions trading mechanism, accelerating the establishment of alternative marine fuel supply chain, innovating alternative marine fuel technology and ship propulsion technology, and actively engaging in international cooperation. [ABSTRACT FROM AUTHOR]

Introduction: Efforts are underway to transform food systems in light of their contributions to global challenges like climate change. However, food systems are highly complex, involve noteworthy place-based challenges, and there is often debate and disagreement among experts over appropriate technologies or interventions to prioritize. Tracking progress, and understanding these differences, is thus a critical need. Methods: We surveyed food systems experts in eight countries about their preferences for 20 different food system transformation strategies and their sentiment regarding whether current initiatives are sufficient to meet 2030 goals for climate and biodiversity. Results: Expert sentiment is overwhelmingly negative, and experts are concerned about multiple "transformation gaps," including gaps in ambition, strategy, and implementation. Expert rankings for 20 strategies vary notably among countries and in ways that do not match those same experts' rankings for the strength of the science behind each lever. Factor analysis reveals four distinct theories of change informing experts' subjective biases: transformation via technical optimization, via smallholder support, via nature-positive solutions, and via supply chain enabling conditions. Discussion: These findings provide insights for navigating the complexities of food system transformation and illustrate the influence on our strategies of preconceptions and biases in how we have come to understand the nature of the challenge. [ABSTRACT FROM AUTHOR]

AbstractBayesian clustering typically relies on mixture models, with each component interpreted as a different cluster. After defining a prior for the component parameters and weights, Markov chain Monte Carlo (MCMC) algorithms are commonly used to produce samples from the posterior distribution of the component labels. The data are then clustered by minimizing the expectation of a clustering loss function that favors similarity to the component labels. Unfortunately, although these approaches are routinely implemented, clustering results are highly sensitive to kernel misspecification. For example, if Gaussian kernels are used but the true density of data within a cluster is even slightly non-Gaussian, then clusters will be broken into multiple Gaussian components. To address this problem, we develop Fusing of Localized Densities (FOLD), a novel clustering method that melds components together using the posterior of the kernels. FOLD has a fully Bayesian decision theoretic justification, naturally leads to uncertainty quantification, can be easily implemented as an add-on to MCMC algorithms for mixtures, and favors a small number of distinct clusters. We provide theoretical support for FOLD including clustering optimality under kernel misspecification. In simulated experiments and real data, FOLD outperforms competitors by minimizing the number of clusters while inferring meaningful group structure. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]