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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]

Philosophers have spent a great deal of time debating whether intransitive preferences can be rational. I present a risky decision that poses a challenge for the defender of intransitivity. The defender of intransitivity faces a trilemma and must either: (i) reject the rationality of intransitive preferences, (ii) deny State‐wise Dominance, or (iii) accept the bizarre verdict that you can be required to pay to relabel the tickets of a fair lottery. If we take the first horn, then we have a synchronic refutation of intransitivity, an improvement on widely criticized diachronic arguments. I sketch possible responses that may rescue intransitivity and argue that each response is possible but generates an explanatory debt. I conclude by showing how the challenge here clarifies the foundations of decision theory without transitivity, conditional on some explanatory debt being payable. [ABSTRACT FROM AUTHOR]

We begin by showing that every theory of the value of uncertain prospects must have one of three unpalatable properties. Reckless theories recommend giving up a sure thing, no matter how good, for an arbitrarily tiny chance of enormous gain; timid theories permit passing up an arbitrarily large potential gain to prevent a tiny increase in risk; non‐transitive theories deny the principle that, if A$A$ is better than B$B$ and B$B$ is better than C$C$, then A$A$ must be better than C$C$. Having set up this trilemma, we study its horns. Non‐transitivity has been much discussed; we focus on drawing out the costs and benefits of recklessness and timidity when it comes to axiology, decision theory, and normative uncertainty. [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]

Expanding and managing current habitat and species protection measures is at the heart of the European biodiversity strategy. A structured approach is needed to gain insights into such issues is systematic conservation planning, which uses techniques from decision theory to identify places and actions that contribute most effectively to policy objectives given a set of constraints. Yet culturally and historically determined European landscapes make the implementation of any conservation plans challenging, requiring an analysis of synergies and trade-offs before implementation. In this work, we review the scientific literature for evidence of previous conservation planning approaches, highlighting recent advances and success stories. We find that the conceptual characteristics of European conservation planning studies likely reduced their potential in contributing to better-informed decisions. We outline pathways towards improving the uptake of decision theory and multi-criteria conservation planning at various scales, particularly highlighting the need for (a) open data and intuitive tools, (b) the integration of biodiversity-focused conservation planning with multiple objectives, (c) accounting of dynamic ecological processes and functions, and (d) better facilitation of entry-points and co-design practices of conservation planning scenarios with stakeholders. By adopting and improving these practices, European conservation planning might become more actionable and adaptable towards implementable policy outcomes. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'. [ABSTRACT FROM AUTHOR]

The author considers an optimization problem for the triharmonic equation under specific boundary conditions. As a result, the triharmonic Poisson integral is constructed in Cartesian coordinates for the upper half-plane. The asymptotic properties of this operator on Lipschitz classes in a uniform metric are analyzed. An exact equality is found for the upper bound of the deviation of the Lipschitz class functions from the triharmonic Poisson integral defined in Cartesian coordinates for the upper half-plane in the metric of space C. The results obtained in the article demonstrate how the methods of approximation theory relate to the principles of the optimal decision theory. [ABSTRACT FROM AUTHOR]

One of the major challenges facing decision-makers at the present time is obtaining complete information about the issue under study, due to the unstable conditions of the work environment that are beyond the control of decision-makers, which requires them to reach an optimal decision in light of these circumstances and fluctuations and to benefit from the data that is collected. Collected by specialists to determine the appropriate probability distribution corresponding to random cases of nature, here we are faced with the issue of making a decision in the event of risk because the probability distribution is a distribution linked to the data controlled by the conditions of the work environment, which entails a great risk. Decision makers bear the responsibility of choosing the optimal decision that reduces This risk is achieved and the greatest possible profit and the least possible loss are achieved. The issue of decision-making becomes more complex as the number of events increases, and we are in dire need of an ideal study of the issue that takes into account all the circumstances of the work environment. The concept of missed opportunity is very useful in analyzing the decision making under risk, after making the decision and the occurrence of events, the decision makers may regret and wish they had chosen actions different from those they chose at the beginning. To reduce the regret of the decision makers and minimize the expected lost opportunity, researchers in the field of classical operations research presented the criterion of the expected lost opportunity through which the decision can be determined. The ideal with the least percentage of regret. In this research, we present a neutrosophic vision of the expected opportunity loss criterion by taking the data of the issue under study. neutrosophic values are ranges whose lowest limit expresses profit in the worst conditions, and only the highest represents profit in the best conditions. [ABSTRACT FROM AUTHOR]

The three-way decision paradigm is a decision-making approach that is analogous to human reasoning. It involves a neutrosophic set as an efficient tool for handling ambiguity. Using cotangent similarity, the Hamming distance, and the innovative Vicis-Wave Hedges distance for ranking, this study proposes three novel three-way decision models based on the Bayes decision. In particular, single-valued neutrosophic numbers are employed to represent each loss function. The proposed models are then condensed into an algorithm. Subsequently, an example is presented to demonstrate the validity and rationality of the proposed models. Finally, we compare the three models. This research provides a new method for decision-making problems. [ABSTRACT FROM AUTHOR]

No existing normative decision theory adequately handles risk. Expected Utility Theory is overly restrictive in prohibiting a range of reasonable preferences. And theories designed to accommodate such preferences (for example, Buchak's (2013) Risk‐Weighted Expected Utility Theory) violate the Betweenness axiom, which requires that you are indifferent to randomizing over two options between which you are already indifferent. Betweenness has been overlooked by philosophers, and we argue that it is a compelling normative constraint. Furthermore, neither Expected nor Risk‐Weighted Expected Utility Theory allow for stakes‐sensitive risk‐attitudes—they require that risk matters in the same way whether you are gambling for loose change or millions of dollars. We provide a novel normative interpretation of Weighted‐Linear Utility Theory that solves all of these problems. [ABSTRACT FROM AUTHOR]

Many accounts of faith hold that faith is deference to an authority about what to believe or what to do. I show that this kind of faith fits into a more general account of faith, the risky‐commitment account. I further argue that it can be rational to defer to an authority even when the authority's pronouncement goes against one's own reasoning. Indeed, such deference is rational in typical cases in which individuals treat others as authorities. [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]

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value–step by step–in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample–accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model. Author summary: Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings or overall percent correct) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary operations and test human ability to execute such operations. In two of the tests human performance deviated only slightly (but systematically) from the predictions of BDT. In the third test, we use a novel method to measure the influence of each sample point provided to the human decision maker and compare it to the influence predicted by BDT. When we look at what human decision makers do–in detail–we find that they use sensory information very differently from what the normative BDT decision maker does. We advance an alternative non-Bayesian model that better predicts human performance. We propose that influence measures are a more sensitive way to discover discrepancies between human and optimal performance, than comparing overall performance. [ABSTRACT FROM AUTHOR]

There are two recognized classes of strategic-form symmetric games, both of which can be conveniently defined through the corresponding player symmetry groups. We investigate the basic properties of these groups and several related concepts. We generalize the notion of coveringness and adapt their results to characterize these player symmetry groups. We study the relationships between the coveringnesses of various symmetry groups. Our results demonstrate that these symmetry groups have rich mathematical structures that are of game theoretical and economic interests. [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 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]

As the imported cross-border e-commerce market is getting more competitive nowadays, the free shipping strategy, as a unique service model, plays an important role in the market share competition. Formulating a free shipping strategy to enable companies to obtain more orders and make more profits has become one of the imperative concerns of cross-border e-commerce companies. We will analyze the impact of increasing the free shipping threshold on consumer behavior and corporate profits, combine the free shipping solutions of four large cross-border e-commerce companies in the China, consider more realistic factors (i.e., commodity prices, order distribution, market competition, consumer characteristics), build a consumer purchase decision model and a consumer increase purchase model based on consumer decision theory, and provide decision support for its selection of a suitable free shipping strategy based on the real operating data of the cross-border e-commerce platform. The results indicate that raising the free shipping threshold within a certain range can help increase a company's average order price and gross profit margin as well as reduce corporate costs. However, an excessively high free shipping threshold weakens the stimulus for consumers to increase purchases and decreases the customer conversion rate. The closer the order price is to the free shipping threshold, the more likely it is that consumers will increase their purchases; the further the order price is from the free shipping threshold, the more likely it is that consumers will pay the shipping fee. This study provides a practical reference for decisions about cross-border e-commerce free shipping policies. [ABSTRACT FROM AUTHOR]

Longtermists argue we should devote much of our resources to raising the probability of a long happy future for sentient beings. But most interventions that raise that probability also raise the probability of a long miserable future, even if they raise the latter by a smaller amount. If we choose by maximising expected utility, this isn't a problem; but, if we use a risk-averse decision rule, it is. I show that, with the same probabilities and utilities, a risk-averse decision theory tells us to hasten human extinction, not delay it. What's more, I argue that morality requires us to use a risk-averse decision theory. I present this not as an argument for hastening extinction, but as a challenge to longtermism. [ABSTRACT FROM AUTHOR]

Collective animal behaviour is a subfield of behavioural ecology, making extensive use of its tools of observation, experimental manipulation and model building. However, a fundamental behavioural ecology approach, the application of optimality theory, has been comparatively neglected in collective animal behaviour. This article seeks to address this imbalance, by outlining an evolutionary theory framework for the discipline. The application of optimality theory to collective animal behaviour requires a number of questions to be addressed. First, what is the correct quantity to optimize? This can be achieved via a combination of considering the organisms' life history, alongside tools such as statistical decision theory and stochastic dynamic programming. Second, what mechanism is appropriate for optimal behaviour? This involves ensuring that models are self-consistent rather than assuming parameter values. Third, at what level of selection does optimization act? Selection acts on the individual except in very particular circumstances, yet collective animal behaviour phenomena are group level, thus introducing a risk of confusing at what level adaptive properties emerge. This article presents examples under each of the three questions, as well as discussing mismatches between theory and observation. In doing so, it is hoped that collective animal behaviour fully inherits the tools and philosophy of its parent discipline of behavioural ecology. • We undertake a review of collective animal behaviour using Tinbergen's four questions. • We thus identify a lack of optimality theory in collective animal behaviour. • We propose three new questions for optimality theory in collective animal behaviour. • Using these questions we review the literature for successes and missed opportunities. [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]

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]

Due to the problem of poor data integrity in the estimation of economic loss in road traffic accidents, the estimation accuracy is not high. In order to effectively improve the performance of economic loss estimation of road traffic accidents, a method of economic loss estimation of road traffic accidents considering grey situation decision is proposed. Based on the grey situation decision theory, the key indicators of economic loss in road traffic accidents are identified, and a comprehensive and complete evaluation index system of economic loss in road traffic accidents is constructed based on the identification results. Then, according to the index system, the economic loss model of road traffic accident personnel and the indirect economic loss model of traffic accidents are constructed to fully estimate the economic loss of road traffic accidents, effectively solve the problem of poor data integrity, and achieve accurate economic loss estimation of road traffic accidents. The experimental results show that the estimation accuracy of the proposed method is always higher than 90%, which can evaluate the economic loss caused by traffic accidents more comprehensively and deeply, and provide decision support for the prevention and compensation of traffic accidents. [ABSTRACT FROM AUTHOR]

Our decision-theoretic states are not luminous. We are imperfectly reliable at identifying our own credences, utilities and available acts, and thus can never be more than imperfectly reliable at identifying the prescriptions of decision theory. The lack of luminosity affords decision theory a remarkable opportunity — to issue guidance on the basis of epistemically inaccessible facts. We show how a decision theory can guarantee action in accordance with contingent truths about which an agent is arbitrarily uncertain. It may seem that such advantages would require dubiously adverting to externalist facts that go beyond the internalism of traditional decision theory, but this is not so. Using only the standard repertoire of decision-theoretic tools, we show how to modify existing decision theories to take advantage of this opportunity. These improved decision theories require agents to maximize conditional expected utility — expected utility conditional upon an agent's actual decision situation. We call such modified decision theories 'self-confident'. These self-confident decision theories have a distinct advantage over standard decision theories: their prescriptions are better. [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]

The role of causation and counterfactuals in causal decision theory is vexed and disputed. Recently, Brian Hedden (2023) argues that we should abandon causal decision theory in favour of an alternative: counterfactual decision theory. I argue that, pace Hedden, counterfactual decision theory is not a competitor to, but rather a version of, causal decision theory – the most popular version by far. I provide textual evidence that the founding fathers of causal decision theory (Stalnaker, Gibbard, Harper, Lewis, Skyrms, Sobel, and Joyce) all endorse counterfactual decision theories. I additionally discuss why these theories came to be called 'causal', rather than 'counterfactual'. And I argue that, properly understood, causal decision theory escapes Hedden's objections. [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]

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]

The pace of technological advancements has been rapidly increasing in recent years, with the advent of artificial intelligence, virtual/augmented reality, and other emerging technologies fundamentally changing the way human beings work. The adoption and integration of these advanced technologies necessitate teams with diverse disciplinary expertise, to help teams remain agile in an ever-evolving technological landscape. Significant disciplinary diversity amongst teams, however, can be detrimental to team communication and performance. Additionally, accelerated by the COVID-19 pandemic, the adoption and use of technologies that enable design teams to collaborate across significant geographical distances have become the norm in today's work environments, further complicating communication and performance issues. Little is known about the way in which technology-mediated communication affects the collaborative processes of design. As a first step toward filling this gap, the current work explores the fundamental ways experts from distinct disciplinary backgrounds collaborate in virtual design environments. Specifically, we explore the conversational dynamics between experts from two complementary yet distinct fields: non-destructive evaluation (NDE) and design for additive manufacturing (DFAM). Using Markov modeling, the study identified distinct communicative patterns that emerged during collaborative design efforts. Our findings suggest that traditional assumptions regarding communication patterns and design dynamics may not be applicable to expert design teams working in virtual environments. [ABSTRACT FROM AUTHOR]

Teff farming in Ethiopia is commonly seen as being dominated by men, with women playing supporting roles on some aspects of the growing process. This study is rooted in existing literature on drivers of Best Practices (BP) adoption and decision-making theory and is unique in that it focuses primarily on understanding how gender-specific factors influence decision-making on the adoption of BPs. To this end, the study assessed the intra-household gender dynamics at play within farming households in Amhara, Ethiopia, and their influence on deciding whether or not to adopt agricultural best practices for teff farming. These gender dynamics include the division of labor between women and men, intra-household decisionmaking processes, social and cultural norms and access factors (such as access to information, training, credit and control over income). Using data from a threeround quantitative survey with one woman and one man in 555 households, as well as focus group discussions and in-depth interviews, this study is uniquely placed to assess the impact of these gender-specific and intra-household factors on the adoption of best practices. The findings show that households where women are more involved in teff farming, have less input into decision-making, less control over income, and more access to information and adopt on average more best practices. However, there is significant heterogeneity when looking at individual best practices, with women's decision-making power or access to resources particularly important for specific practices such as sowing in rows. This study implies that designing more gender-sensitive agricultural programs and extension services in Ethiopia - specifically on practices relevant to women and men - can increase best practice adoption, with the ultimate aim of increasing productivity and income for teff farming households, and empowering women. Since male and female farmers are involved in different practices, access to resources and decision-making power have different impacts depending on the gender of the respondent and the practice analyzed, and there is no "one size fits all" solution to improve teff farming productivity. [ABSTRACT FROM AUTHOR]

My topic is how to make decisions when you possess foreknowledge of the consequences of your choice. Many have thought that these kinds of decisions pose a distinctive and novel problem for causal decision theory (CDT). My thesis is that foreknowledge poses no new problems for CDT. Some of the purported problems are not problems. Others are problems, but they are not problems for CDT. Rather, they are problems for our theories of subjunctive supposition. Others are problems, but they are not new problems. They are old problems transposed into a new key. Nonetheless, decisions made with foreknowledge illustrate important lessons about the instrumental value of our choices. Once we've appreciated these lessons, we are left with a version of CDT which faces no novel threats from foreknowledge. [ABSTRACT FROM AUTHOR]

This article explores the debate between Causal Decision Theory (CDT) and Evidential Decision Theory (EDT) in the context of Newcomb problems. It introduces Conditional Causal Decision Theory (CCDT) as a version of CDT that aims to align with pre-theoretic judgments about rationality in these problems. However, the author argues that CCDT produces incorrect results, suggesting that it is not superior to CDT or EDT. The article also discusses the concept of expected value in decision-making, provides a formula for calculating expected value, and uses examples to illustrate the concept. It examines the application of CCDT to smoking and the Fisher case, concluding that CCDT recommends quitting smoking but suggests smoking in the Fisher case. The article evaluates the expected values of acts in these scenarios and addresses Edgington's analysis, arguing that it does not support CCDT's recommendation to quit smoking in the Fisher case. It also discusses the influence of different factors on the expected value of smoking and highlights the issue of aligning CCDT with different calculations. The article further explores the relationship between CDT and EDT, arguing that they yield the same results in adequately specified decision problems. It discusses the relationship between cause and effect in decision problems and emphasizes the importance of adequately specifying decision problems. The article also discusses the assumptions made in decision problem formulation and the use of CDT in decision-making situations. It explores the expected values of acts according to different decision theories and the importance of considering the influence of [Extracted from the article]

We propose a quantum-mechanical model that represents a human system of beliefs as the quantised energy levels of a physical system. This model represents a novel perspective on opinion dynamics, recreating a broad range of experimental and real-world data that exhibit an asymmetry of opinion radicalisation. In particular, the model demonstrates the phenomena of pronounced conservatism versus mild liberalism when individuals are exposed to opposing views, mirroring recent findings on opinion polarisation via social media exposure. Advancing this model, we establish a robust framework that integrates elements from physics, psychology, behavioural science, decision-making theory, and philosophy. We also emphasise the inherent advantages of the quantum approach over traditional models, suggesting a number of new directions for future research work on quantum-mechanical models of human cognition and decision-making. [ABSTRACT FROM AUTHOR]

To make better decisions on approximation, we may need to increase reliable and useful information on different aspects of approximation. To enhance information about the quality and certainty of approximating the solution of an Apollonius-type quadratic functional equation, we need to measure both the quality and the certainty of the approximation and the maximum errors. To measure the quality of it, we use fuzzy sets, and to achieve its certainty, we use the probability distribution function. To formulate the above problem, we apply the concept of Z-numbers and introduce a special matrix of the form diag (A , B , C) (named the generalized Z-number) where A is a fuzzy time-stamped set, B is the probability distribution function, and C is a degree of reliability of A that is described as a value of A ∗ B . Using generalized Z-numbers, we define a novel control function to investigate H–U–R stability to approximate the solution of an Apollonius-type quadratic functional equation with quality and certainty of the approximation. [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]

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 this article, we investigate the relative performance of artificial neural networks and structural models of decision theory by training 69 artificial intelligence models on a dataset of 7080 human decisions in extensive form games. The objective is to compare the predictive power of AIs that use a representation of another agent's decision-making process in order to improve their own performance during a strategic interaction. We use human game theory data for training and testing. Our findings hold implications for understanding how AIs can use constrained structural representations of other decision makers, a crucial aspect of our 'Theory of Mind'. We show that key psychological features, such as the Weber–Fechner law for economics, are evident in our tests, that simple linear models are highly robust, and that being able to switch between different representations of another agent is a very effective strategy. Testing different models of AI-ToM paves the way for the development of learnable abstractions for reasoning about the mental states of 'self' and 'other', thereby providing further insights for fields such as social robotics, virtual assistants, and autonomous vehicles, and fostering more natural interactions between people and machines. [ABSTRACT FROM AUTHOR]

Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively demanding Bayesian computations for behaviours. Across two studies, we trained participants to estimate hidden locations of a target drawn from priors with different levels of uncertainty. In each trial, scattered dots provided noisy likelihood information about the target location. Participants showed that they learned the priors and combined prior and likelihood information to infer target locations in a Bayes fashion. We then introduced a transfer condition presenting a trained prior and a likelihood that has never been put together during training. How well participants integrate this novel likelihood with their learned prior is an indicator of whether participants perform Bayesian computations. In one study, participants experienced the newly introduced likelihood, which was paired with a different prior, during training. Participants changed likelihood weighting following expected directions although the degrees of change were significantly lower than Bayes-optimal predictions. In another group, the novel likelihoods were never used during training. We found people integrated a new likelihood within (interpolation) better than the one outside (extrapolation) the range of their previous learning experience and they were quantitatively Bayes-suboptimal in both. We replicated the findings of both studies in a validation dataset. Our results showed that Bayesian behaviours may not always be achieved by a full Bayesian computation. Future studies can apply our approach to different tasks to enhance the understanding of decision-making mechanisms. Author summary: Bayesian decision theory has emerged as a unified approach for capturing a wide range of behaviours under uncertainty. However, behavioural evidence supporting that humans use explicit Bayesian computation is scarce. While it has been argued that knowledge generalization should be treated as hard evidence of the use of Bayesian strategies, results from previous work were inconclusive. Here, we develop a marker that effectively quantifies how well humans transfer learned priors to a new scenario. Our marker can be applied to various tasks and thus can provide a path forwarding the understanding of psychological and biological underpinnings of inferential behaviours. [ABSTRACT FROM AUTHOR]

Many philosophers think that given the choice between saving the life of an innocent person and averting any number of minor ailments or inconveniences, it would be better to save the life. How, then, should one compare the risk of an innocent person's life to such minor ailments and inconveniences? If lives are infinitely more important than insignificant factors then any risk cannot be outweighed, and that is untenable. An alternative approach seems more promising: let the values of such insignificant factors be bounded, as then there will be well-behaved tradeoffs between insignificant things and the risk to an innocent life. We argue, however, that bounding the values of insignificant factors poses myriad problems. [ABSTRACT FROM AUTHOR]

Objective to analyze the possibility of using golf as a metaphor for teaching finance. Study reports on a pilot experience of teaching finance using golf as a metaphor. Golf can be a means of promoting the engagement of finance students. Theoretical framework: a normative and positive economic decision theory is used as a framework. Sports can be a means of engaging students in finance education programs. Golf, in particular, can fulfill this role and serve as a powerful tool for building and maintaining social networks relevant to high-level professionals. Methods: thirty undergraduate students voluntarily participated in a pilot teaching program over five days. The program included sessions (in the field) on the fundamentals of golf and financial decisions, and students looked for synergies between these two domains. Participants showed a high level of interest in both the sports practice sessions and the finance activities. Results: the results suggest that psychological errors common to golf and financial behavior--for example, overoptimism, overconfidence, and emotional judgments--can be diagnosed and addressed through sports practice. In addition, the participants' selfassessment indicates the possibility that the program can induce behaviors in line with the corporate environment. Conclusions: Despite golf's contribution to the teaching of business subjects, it is still absent from formal curricula. [ABSTRACT FROM AUTHOR]

Some risky multi-criteria group decision making problems include payoff and probability information. To deal with these problems, this study introduces a large scale multi-criteria group decision making model based on focus theory of choice. In this model, a group of experts' linguistic evaluations on multiple criteria are first collected to form linguistic distributions. The positive foci of the linguistic distributions are computed and aggregated into the alternatives' scores. It is noted that in this process the linguistic terms and probabilities are aggregated by using different rules. The positive foci of the alternatives' scores are computed and the optimal alternative is selected. A pollution treatment evaluation problem is solved by using the proposed model, and simulation experiments and comparative analysis are given. [ABSTRACT FROM AUTHOR]

In this manuscript, we develop TOPSIS (Technique for order of preference by similarity to ideal solution) method in the setting of bipolar fuzzy environment which has the ability to deal the data while keeping in view the positive and negative aspects. By using bipolar fuzzy sets, we establish the novel concept of rating the numerous preferences of any object described through the connection number(CN) of set pair analysis(SPA). In this regard, we extend the TOPSIS method based on the connection number(CN) of set pair analysis(SPA) in the frame of bipolar fuzzy sets. For the sake of verification, effectiveness and superiority of our method, we conduct the comparative study of some real life problem related to decision making theory. Moreover, we observe that our proposed method also fulfills the existing test criterions. [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]

People use language to influence others' beliefs and actions. Yet models of communication have diverged along these lines, formalizing the speaker's objective in terms of either the listener's beliefs or actions. We argue that this divergence lies at the root of a longstanding controversy over the Gricean maxims of truthfulness and relevance. We first bridge the divide by introducing a speaker model which considers both the listener's beliefs (epistemic utility) and their actions (decision-theoretic utility). We show that formalizing truthfulness as an epistemic utility and relevance as a decision-theoretic utility reconciles the tension between them, readily explaining puzzles such as context-dependent standards of truthfulness. We then test a set of novel predictions generated by our model. We introduce a new signaling game which decouples utterances' truthfulness and relevance, then use it to conduct a pair of experiments. Our first experiment demonstrates that participants jointly maximize epistemic and decision-theoretic utility, rather than either alone. Our second experiment shows that when the two conflict, participants make a graded tradeoff rather than prioritizing one over the other. These results demonstrate that human communication cannot be reduced to influencing beliefs or actions alone. Taken together, our work provides a new foundation for grounding rational communication not only in what we believe, but in what those beliefs lead us to do. [ABSTRACT FROM AUTHOR]

Purpose: As independent travel has become increasingly popular in recent years, factors influencing users' attitudes and behaviors should be explored. Besides, the rise of video platforms like YouTube, Bilibili and TikTok has led to the flourishing of user generated content (UGC), among which travel vlogs have become convenient and essential references for tourists before and while they travel on their own. Design/methodology/approach: This research used quantitative analysis through a survey designed for young people, as most vlog viewers are in those age groups, to explore the attitudes and perceptions of vlog preference, attitudes and cognizance of travel vlogs among different gender, guided by the decision-making theory and preference theory with 455 participants. Findings: Survey results showed that both genders have common choices regarding travel vlog preference, attitudes, sharing and cognizance. Yet, gender differences appear in some cognition of travel vlogs and specific preferences, such as information searching habits, trusty travel and information sources. Originality/value: Existing studies focus on the influence of platforms on users, but scant studies focus on user perceptions of vlogs as a new information carrier for young people in East Asia, especially regarding gender differences, which is crucial in media-based marketing. This research provides practical suggestions to marketers and vloggers to attract and engage different genders of young people and theoretical implications on decision and preference theories. [ABSTRACT FROM AUTHOR]

AbstractThe application of expected value as the first decision-theoretical criterion for evaluating risky lotteries is traditionally attributed to the 1654 correspondence between Pierre de Fermat and Blaise Pascal. However, an anonymous author of the XVth century Italian manuscript stored in the Vatican Apostolic Library calculated expected value by backward induction in a similar way to Pascal’s approach. This note argues that an even earlier anonymous XIVth century Italian manuscript calculates expected value by a different method resembling the direct rule (regula recta) described in 1202 by Leonardo of Pisa (Fibonacci) in his Liber Abaci as borrowed from the Arabs. [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]

In this article we discuss how causal decision theory should be modified to handle a class of problematic cases involving deterministic laws. Causal decision theory, as it stands, is problematically biased against your endorsing deterministic propositions (for example, it tells you to deny Newtonian physics, regardless of how confident you are of its truth). Our response is that this is not a problem for causal decision theory per se , but arises because of the standard method for assessing the truth of certain counterfactuals. The truth of deterministic laws is 'modally fragile' on the standard semantics for counterfactuals: if determinism is true and you were to do otherwise, the laws would be different. We provide two ways of avoiding this problem: (1) supplement the standard semantics for counterfactuals with impossible worlds, or (2) introduce rigid designators into the description of problematic decision situations. We argue that both of these approaches are well motivated and can be readily incorporated into Lewisian causal decision theory. [ABSTRACT FROM AUTHOR]

We start by considering the problem of estimating intrinsic distances on a smooth submanifold. We show that minimax optimality can be obtained via a reconstruction of the surface, and discuss the use of a particular mesh construction—the tangential Delaunay complex—for that purpose. We then turn to manifold learning and argue that a variant of Isomap where the distances are instead computed on a reconstructed surface is minimax optimal for the isometric variant of the problem. [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]