Search

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

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

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

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

This paper presents an in-depth examination of the concept of probability in a historical context, from its earliest postulations as expressed by Leibniz and Bernoulli to more modern theoretical constructs such as Savage's subjective probability theory, Kolmogorov's axioms and Bayesian calculations. A common belief shared by some of these prominent figures is that discrepancies and misconceptions between many theoretical frameworks of the field are not caused by logical or statistical failures—instead, as Savage wrote in his fundamental work, much of the disagreement is merely terminological, and would disappear under sufficiently sharp analysis. As such, the review is supplemented with an analysis of terminology that is focused on eliminating ambiguity and discrepancies between common formal definitions like "belief" and "certainty" within different theories. Aside from making the complex relations and closely-related conceptions more palatable to readers, this also serves to strengthen the formalized bases underlying theories examined in the paper. [ABSTRACT FROM AUTHOR]

On taking the intermediate value theorem (IVT) and its converse as a point of departure, this paper connects the intermediate value property (IVP) to the continuity postulate typically assumed in mathematical economics, and to the solvability axiom typically assumed in mathematical psychology. This connection takes the form of four portmanteau theorems, two for functions and the other two for binary relations, that give a synthetic and novel overview of the subject. In supplementation, the paper also surveys the antecedent literature both on the IVT itself, as well as its applications in economic and decision theory. The work underscores how a humble theorem, when viewed in a broad historical frame, bears the weight of many far-reaching consequences; and testifies to a point of view that the apparently complicated can sometimes be under-girded by a most basic and simple execution. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has contributed to a global crisis in long-term care (LTC) with devastating consequences for residents, families and health professionals. In Ontario, Canada the severity of this crisis has prompted some care partners to move residents home with them for the duration or a portion of the pandemic. This type of care transition, from LTC to home care, was highly unusual pre-pandemic and arguably suboptimal for adults with complex needs. This paper presents the findings of a qualitative study to better understand how residents, care partners, and health professionals made care transition decisions in Ontario's LTC settings during the pandemic. Methods: Semi-structured interviews were conducted with 32 residents, care partners and health professionals who considered, supported or pursued a care transition in a LTC setting in Ontario during the pandemic. Crisis Decision Theory was used to structure the analysis. Results: The results highlighted significant individual and group differences in how participants assessed the severity of the crisis and evaluated response options. Key factors that had an impact on decision trajectories included the individuals' emotional responses to the pandemic, personal identities and available resources. Conclusions: The findings from this study offer novel important insights regarding how individuals and groups perceive and respond to crisis events. [ABSTRACT FROM AUTHOR]

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

The concept of the hybrid structure, as an extension of both soft sets and fuzzy sets, has gained significant attention in various mathematical and decision-making domains. In this paper, we delve into the realm of hemirings and investigate the properties of hybrid h-bi-ideals, including prime, strongly prime, semiprime, irreducible, and strongly irreducible ones. By employing these hybrid h-bi-ideals, we provide insightful characterizations of h-hemiregular and h-intra-hemiregular hemirings, offering a deeper understanding of their algebraic structures. Beyond theoretical implications, we demonstrate the practical value of hybrid structures and decision-making theory in handling real-world problems under imprecise environments. Using the proposed decision-making algorithm based on hybrid structures, we have successfully addressed a significant real-world problem, showcasing the efficacy of this approach in providing robust solutions. [ABSTRACT FROM AUTHOR]

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

It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base ranks, such inferences typically rely on statistical models having entity-specific parameters. These can be treated as random effects in hierarchical models characterizing variation among the entities. In this paper, we are particularly interested in the problem of ranking basketball players in terms of their contribution to team performance. Using data from the National Basketball Association (NBA) in the United States, we find that many players have similar latent ability levels, making any single estimated ranking highly misleading. The current literature fails to provide summaries of order relationships that adequately account for uncertainty. Motivated by this, we propose a Bayesian strategy for characterizing uncertainty in inferences on order relationships among players and lineups. Our approach adapts to scenarios in which uncertainty in ordering is high by producing more conservative results that improve interpretability. This is achieved through a reward function within a decision theoretic framework. We apply our approach to data from the 2009-2010 NBA season. [ABSTRACT FROM AUTHOR]

Blaise Pascal famously calculated the expected value of a risky lottery in the 1654 correspondence with Pierre de Fermat in the context of the so-called points problem. Pascal solved this problem by backward induction, whereas Fermat—by counting combinations. This paper analyzes a more complex version of the points problem from an anonymous XVth century Italian manuscript stored in the Vatican Apostolic Library. In this manuscript, the problem of points is solved by backward induction in a similar way to Pascal's train of thought. In this light, Pascal's pioneering contribution may be not as novel as it is traditionally believed. [ABSTRACT FROM AUTHOR]

Concerning the development of computational tools and solutions as a decision-making aid, this paper presents the results of the waspasWEB project, which strives to provide decision-makers with a readily accessible mechanism to employ the weighted aggregated sum product assessment (WASPAS) method. The social contribution of the project encompasses the development of a user-friendly and publicly accessible internet tool, as well as a package launched on the Comprehensive R Archive Network (CRAN) to serve the community of users of the R language. The use of operational research methodologies is crucial to justify decisions, and this effort seeks to advance the adoption of such methodologies, offering managers, researchers, and the general public an intuitive and easily accessible multi-criteria decision-making tool. In this way, we present the technical specifications, usability, and interactivity of the user with the computational platform, being validated its viability through a hypothetical case study. At the end of the research, it exposes the limitations and feasibility of the proposed computational model along with future research. [ABSTRACT FROM AUTHOR]

This paper focuses on risk‐averse‐based optimal operation of a grid‐connected hybrid energy system (HES) composed of photovoltaic (PV), diesel generator, and battery storage system (BSS). For this goal, information gap decision theory (IGDT) is used to model load demand uncertainty. The aim of the optimal operation is to minimize cost of PV/diesel/BSS by optimal determination of the power purchased from the electricity grid. Since in the risk‐averse strategy, load demand has an undesirable impact on the objective function, the decision maker attempts to maximize the uncertainty radius in a way that any deviation of the uncertain parameter leads to an objective function value which is not worse than the critical value. Over the case studies (considering different radiations), simulation results indicate that in the risk‐neutral strategy, at high, medium, and low radiations, the operation cost is 28.88, 36.10, and 42.63$, respectively. In the risk‐averse strategy, when the radiation is high, by increase of the deviation factor from 0.1 to 0.25, the optimal uncertainty radius increases from 6.98% to 15.72% (increase of around 125%) and the operation cost increases from 31.768 to 36.101$. When the radiation is low, by increase of the deviation factor from 0.1 to 0.25, the uncertainty radius increases from 8.64% to 16.9% (increase of around 96%) and the operation cost increases from 46.895 to 53.291$. [ABSTRACT FROM AUTHOR]

Interval type-2 trapezoidal fuzzy (IT2TrF) number is a powerful tool to depict fuzzy information. Information measures methods have received more and more attention in recent years as they play an important role in decision-making theory. A new multi-attribute decision-making (MADM) method supported by IT2TrF information measures is investigated in this paper under the IT2TrF information environment. Firstly, three axiomatic definitions of IT2TrF information measures are introduced, which include information entropy, similarity measure and cross-entropy. Secondly, with the help of the exponential function, we formulate some information measure formulas, which are followed by the proofs that the exponential entropy, exponential similarity measure and exponential cross-entropy fit the three axiomatic definitions. Subsequently, a novel IT2TrF MADM method is designed, in which the IT2TrF exponential entropy and cross-entropy are utilized to generate the attribute weights, the IT2TrF exponential similarity measure is employed to obtain the closeness degree of the ideal solution and derive the most satisfying solution. Lastly, we provide a numerical example of corporate investment to demonstrate the applicability and feasibility of the proposed MADM method. The robustness and merits of the developed MADM method are highlighted by the comparative analysis. [ABSTRACT FROM AUTHOR]

The social and economic sciences are grounded on the basic assumption that social life, decision-making behavior, and consciousness are classical physical and therefore material phenomena. Quantum social science, a new research area, which refers to the knowledge and interpretations of quantum physics, is challenging this assumption. This paper gives an overview of quantum social science and explains quantum decision theory on the one hand with a focus on the cognitive biases first elaborated by Kahneman & Tversky, and on the other hand by Darwin´s theory of evolution. [ABSTRACT FROM AUTHOR]

As an important branch of modern decision-making theory, multi-attribute decision-making (MADM) plays an important role in various fields. Classic MADM methods can provide a ranking of alternatives, and decision-makers need to evaluate the level subjectively based on the ranking results. Because of the limitation of knowledge, this is likely to lead to potential individual losses. Three-way decision (3WD) theory has good classification ability. Therefore, this paper proposes a dynamic hybrid multi-attribute 3WD (MA3WD) model. First, a new scheme for constructing loss functions is proposed from the perspective of gray relational analysis (GRA), which is an accurate and objective way to describe the relationship between loss functions and attribute values. Then, conditional probabilities are determined by employing the gray relational analysis technique for order preference by similarity to the ideal solution (GRA-TOPSIS). With these discussions, a GRA-based hybrid MA3WD model for a single period is proposed by considering multi-source information. Furthermore, by extending the single-period scenario to a multi-period one, we construct a dynamic hybrid MA3WD model, which can obtain the final three-way decision rules as well as the results of each period and each attribute. Finally, the proposed method is applied to the case of performance evaluation of elderly-care services to demonstrate the effectiveness of the method, and comparative analyses are given to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Soil environmental quality assessment traditionally focuses on a single objective. Therefore, related results only reflect the pollution of the assessment objective and ignore the interaction and relationships between soil, ecology, and human health. In this study, we collected and filtered data about heavy metal content (Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni) in city soils from more than 1000 published papers. Spatial multi-criteria decision-making theory was used to establish the combined evaluation model of soil environmental quality using the geochemical accumulation, ecological risk, non-carcinogenic risk, and carcinogenic risk indexes. Our results showed that the pollution degree and elements causing most pollution were significantly different when these different methods were used for assessment. The proposed framework was able to unify the overall pollution trend in regional soil environmental quality assessment and could output more robust and reasonable assessment results of soil, ecology, and human health. The random forest model was used to analyze the potential sources for excessive accumulation of heavy metal elements in soils, and the results showed that the main potential sources were the proportion of secondary industry and urbanization rate, each with an average contribution ratio of 20%. This study provides comprehensive and specific information for the prevention and control of soil heavy metal pollution in China. [ABSTRACT FROM AUTHOR]

Underscoring the economic significance of the Knightian distinction between risk and uncertainty, Don Katzner forcefully challenges the continued dominance of the expected utility model based on subjective probability in macroeconomic analysis and offers in its place a simple yet elegant model of decision making inspired by the pioneering work of G.L.S. Shackle. In doing so, Katzner lends support to a research program to identify a more coherent and empirically grounded theory of decision making under uncertainty. Our paper makes three contributions to this program. First, we argue that the appropriate choice of a model of individual behavior under uncertainty cannot be adjudicated merely on logical, a priori grounds, but must ultimately be based on consistency with observed behavior. There are many internally consistent models and their external conditions of validity hardly ever rely on inconceivable scenarios. Second, while Katzner's model certainly provides a plausible foundation for understanding choice under uncertainty, we suggest that it is somewhat underdetermined. It requires further theoretical articulation to yield testable predictions which differentiate it from competing approaches. Third, we discuss recent experimental findings that may need to be addressed by any satisfactory theoretical account of decision making under uncertainty. [ABSTRACT FROM AUTHOR]

With the vast amount of data collected on football and the growth of computing power, many games involving decision choices can be optimized. The underlying rule is the maximization of an expected utility of outcomes and the law of large numbers. The data available allows one to compute with high accuracy the probabilities of outcomes of actions, and the well defined points system in the game allows for a specification of the terminal utilities. With some well established decision theory we can optimize choices for each single play level. A full exposition of the theory and analysis is presented in the paper. [ABSTRACT FROM AUTHOR]

Operational research is a scientific discipline related to the decision theory that allows determining solutions for specific problems related to, for example, widely understood transport. Increasingly popular in this field are issues related to the domain of the green urban transport. In order to support the decision-making process in this area, methods of multi-criteria decision analysis (MCDA) are used more and more often. However, if we solve a specific problem using different MCDA methods, we get different rankings, as each method has a different methodological basis. Therefore, the challenge is how to make a reliable decision. This paper presents a numerical example from the green urban transport domain, which is solved by six different MCDA methods that return a complete ranking. We measure the similarity of these rankings using coefficients rw and WS, and then we propose a simple way of determining a compromise solution. The obtained compromise ranking is guaranteed to be the best match to the selected MCDA methods' rankings, which is proved in the paper. Finally, possible directions for further development work are identified. [ABSTRACT FROM AUTHOR]

The day surgery management mode is mainly decentralized management, with clinical departments as the unit, and with reference to the experience of inter project operation management in benchmark hospitals, the empirical management is implemented. With the development of day surgery, the extensive decentralized management mode has been unable to meet the needs of the current day surgery development situation. At first, the paper carefully analyzes the existing problems in the day surgery process in the day ward of the Children’s Hospital of Nanjing Medical University. And then, the concerns of doctors, nurses, anesthesiologists and other hospital staff in day ward, children and their parents are considered. Based on Bayesian decision-making theory, this paper optimizes the pre-admission evaluation of sick children and hospitalization process for day surgery in Nanjing Children’s Hospital. Moreover, the specific optimization process is designed. After the optimizations, it can be seen that the time consumption of each step of the hospitalization process in day surgery is reduced. Thus, the hospital stay of sick children are significantly reduced, and the operation cost is reduced. In addition, the first preoperative preparation time and the average time of receiving children were reduced in day ward. The satisfaction of children’s parents was significantly improved. [ABSTRACT FROM AUTHOR]

For practical decisions on common recurring maintenance actions, the information from routine inspections form a decision basis for the bridge manager. It is often difficult to assess whether this information is sufficient for deciding on a repair action, or if more information is needed. For many bridges the information for supporting decisions may be limited, although those bridges cause large yearly maintenance costs for the society. The purpose of the study presented in this paper is to show how two different models for decision making based on Bayesian decision theory, a point-in-time decision model and a sequential updating decision model, can be used to improve the decision-making process for common maintenance decisions. The models use information from routine inspections and incorporates time dependent aspects such as material degradation and time value of money to improve the decision-making process. The focus is on presenting the methodology with a case study of a concrete bridge in Sweden where the edge beams may have to be replaced. Three assessment approaches are considered: (i) no assessment, (ii) desktop evaluation and (iii) measurements. The main finding is that sequential updating decision making will provide a higher benefit than a point-in-time decision, and thus give higher Value of Information. This value becomes even higher when the measurements are selected for the assessment. The results also show that the edge beams should be replaced. The general approach presented can be applicable to many decision scenarios related to maintenance of deteriorating structures. [ABSTRACT FROM AUTHOR]

Recently, vehicle-to-vehicle (V2V) based localization has attracted much attention due to its potential to achieve high accuracy. However, the error caused by non-line-of-sight (NLOS) propagation significantly affects the localization performance. In this paper, a novel method combining V2V communication and NLOS identification is proposed to improve accuracy of vehicle localization in NLOS scenarios. First, the algorithm identifies NLOS links with statistical methods. In this step, identification is conducted by decision theory or z-test, which depends on whether priori NLOS information is known or not. After that, NLOS links are discarded. Using the information including length of remaining links and GPS positions of surrounding vehicles, the estimated positions of target vehicles can be obtained by multilateration. It is shown by simulations that the proposed algorithm outperforms several classical methods in accuracy. Specifically, when priori NLOS information is available, 80% of vehicles have a localization error less than 5 m. For the case where NLOS information cannot be obtained, the algorithm still have good performance, and the ratio of vehicles having error within 5 m reaches 70%. [ABSTRACT FROM AUTHOR]

Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving. [ABSTRACT FROM AUTHOR]

Solar energy and bioenergy are two leading renewable forms of energy in the move toward a near-zero-emission electric power industry. Concentrated solar power units coupled with thermal storage and biomass power plant offer dispatchable electricity, raising their ever-growing role in future renewable-dominated networks. This paper proposes a day-ahead and intraday dispatch model for maximizing the profit of an Integrated Biomass-Concentrated Solar (IBCS) system considering the synergies arising from their coupled operation. To sensibly capture uncertainty and decision sequence of real-life electricity markets, a two-stage stochastic structure is proposed, while the solar-related uncertainty is involved using Information Gap Decision Theory (IGDT). The model is complemented with a novel multi-objective architecture based on the compound of IGDT and Conditional Value-at-Risk (CVaR), which allows handling risk exposure to both stochastic and IGDT inputs. The Pareto strategies in the multi-objective model are extracted through an expanded form of the $\epsilon$ -constraint method, whereas a posteriori approach based upon the out-of-sample assessment is applied to derive the optimal dispatch pattern among the generated Pareto strategies. The simulation results demonstrate that: 1) the proposed integrated dispatch model achieves substantial profitability, and 2) the performance of the suggested CVaR-IGDT model is superior to conventional approaches. [ABSTRACT FROM AUTHOR]

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

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

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]

That we may rely on our knowledge seems like a platitude. Yet, the view that knowledge is sufficient for permissible reliance faces a major challenge: when much hangs on whether we know, relying on our knowledge seems to license irrational action. Unfortunately, extant proposals to meet this challenge (Hawthorne & Stanley, 2008; Williamson, 2005a; Schulz, 2017, 2021b) either fail to make the correct predictions about high-stakes cases or, as I will argue, face a substantial objection. In this paper, I will offer two novel proposals for defending the sufficiency of knowledge for permissible reliance. My favoured proposal, which I call Flexible Fallibilism, is based on the idea that stakes can affect how we rely on our knowledge and how our knowledge determines what is rational for us to do. Besides making the correct predictions about high-stakes cases, I will argue that Flexible Fallibilism also provides us with a knowledge-based account of when to simplify our practical reasoning. Afterwards, I will present another proposal, which I call Dual Infallibilism, that combines two recent claims by Moss (2013, 2018) and Jackson (2019a) about credal knowledge and the metaphysics of doxastic states. While the offered proposals share various virtues, I will argue that we ultimately should prefer Flexible Fallibilism. [ABSTRACT FROM AUTHOR]

Utilitarianism is the view that as far as morality goes, one ought to choose the option which will result in the most overall well-being—that is, that maximises the sum of whatever makes life worth living, with each person's life equally weighted. The promise of utilitarianism is to reduce morality to one simple principle, easily incorporated into policy analysis, economics and decision theory. However, utilitarianism is not popular amongst moral philosophers today. This is in large part due to the influence of Williams's 'Integrity Objection' (1973). Though the Integrity Objection has been influential in turning philosophers against utilitarianism, it is also difficult to make precise, evidenced by the myriad interpretations in the literature. In this paper I interpret the objection as holding that agents who accept utilitarianism cannot, as a matter of psychology, be committed to their projects. I explore other interpretations, finding some to be inconsistent with Williams's approach, and others to be relatively easily answerable by the utilitarian. The psychological problem I identify is harder for utilitarians to avoid, though I have begun to offer a response in other work. [ABSTRACT FROM AUTHOR]

Fragmentation in health systems leads to discontinuities in the provision of health services, reduces the effectiveness of interventions, and increases costs. In international comparisons, Germany is notably lagging in the context of healthcare (data) integration. Despite various political efforts spanning decades, intersectoral care and integrated health data remain controversial and are still in an embryonic phase in the country. Even more than 2 years after its launch, electronic health record (elektronische Patientenakte ; ePA) users in Germany constitute only 1 per cent of the statutorily insured population, and ongoing political debates suggest that the path to broader coverage is fraught with complexities. By exploring the main stakeholders in the existing (fragmented) health system governance in Germany and their sectoral interests, this paper examines the implementation of ePA through the lens of corporatism, offering insights based on an institutional decision theory. The central point is that endeavours to better integrate health data for clinical care, scientific research and evidence-informed policymaking in Germany will need to address the roles of corporatism and self-governance. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in decision aiding problems, aiming at rating a set of objects with respect to several dimensions, called criteria. A rating problem statement consists on partitioning a set of objects into predefined ordered equivalence classes, called categories, identified by ratings. Rating problems are widely studied in the literature, either based on the utility theory, rough sets or the majority principle. The existing methods based on the majority principle present some disadvantages potentially leading to an unconvincing rating because challenged by contradictory pairwise comparisons. In this work, we present a new method providing a "convincing" (challenge-free) rating over a set of studied objects, based on the aggregation of positive and negative reasons, respectively supporting and opposing a rating. The method exploits comparisons among the objects and the profiles characterizing the categories as well as comparisons among the objects. [ABSTRACT FROM AUTHOR]

The theoretical literature on risk aversion and Expected Utility Theory is extensive; however, the analysis of this behaviour with natural experiments could be more comprehensive. In this paper, we use data from 120 episodes of the Portuguese version of the TV game show The Price is Right, namely from The Wheel game, to explore risk aversion as well as the impact of gender in decision-making. The Wheel game has straightforward rules and huge expected payoffs. All contestants have access to the same information and distributions of uncertainty, making it a unique field laboratory to conduct an experimental test of rational decision theory. The objective is to infer the risk aversion levels of decision-makers from their choice to turn the wheel and the influence of gender on risk attitudes. There is a widespread view that women are more risk-averse than men. However, we could not reject the hypothesis that women and men have the same level of risk aversion. Nevertheless, we have evidence that contestants are more risk-averse than risk-seeking. The omission bias, loss aversion and regret can explain that behaviour. [ABSTRACT FROM AUTHOR]

Covariance matrix estimation is a fundamental statistical task in many applications, but the sample covariance matrix is suboptimal when the sample size is comparable to or less than the number of features. Such high‐dimensional settings are common in modern genomics, where covariance matrix estimation is frequently employed as a method for inferring gene networks. To achieve estimation accuracy in these settings, existing methods typically either assume that the population covariance matrix has some particular structure, for example, sparsity, or apply shrinkage to better estimate the population eigenvalues. In this paper, we study a new approach to estimating high‐dimensional covariance matrices. We first frame covariance matrix estimation as a compound decision problem. This motivates defining a class of decision rules and using a nonparametric empirical Bayes g‐modeling approach to estimate the optimal rule in the class. Simulation results and gene network inference in an RNA‐seq experiment in mouse show that our approach is comparable to or can outperform a number of state‐of‐the‐art proposals. [ABSTRACT FROM AUTHOR]

When planning a two‐arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in‐trial patient benefit. In such cases, Bayesian response‐adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone. [ABSTRACT FROM AUTHOR]

That a standard of medical care must outline services that benefit the patient is relatively uncontroversial. However, one must determine how the practices outlined in a medical standard of care should benefit the patient. I will argue that practices outlined in a standard of medical care must not detract from the patient's well-functioning and that clinicians can refuse to provide services that do. This paper, therefore, will advance the following two claims: (1) a standard of medical care must not cause dysfunction, and (2) if a physician is medically rational to not provide some service which fails to meet the above condition (i.e. fails to be a standard of medical care), then she may refuse to do so. I then apply my thesis to the prescription of puberty blockers to children with gender dysphoria. [ABSTRACT FROM AUTHOR]

There is an ever-growing need for companies and manufacturing systems to be flexible in order for them to adapt to the rapid changes and increasing uncertainty in society, markets, and supply chains. Several studies suggest that Industry 4.0 solutions foster shorter innovation/development cycles, resource efficiency, individualization on demand, faster decision making, and, finally, higher flexibility in production. However, little is still known about the relationship between Industry 4.0 and manufacturing flexibility. One of the existing gaps in the literature is the lack of a methodology to assess the potential impact of Industry 4.0 solutions on manufacturing flexibility and companies' profitability. This paper contributes to closing such a gap from a theoretical perspective. First, it conceptualized the role of flexibility in manufacturing systems. Then, the relationship between Industry 4.0 solutions and manufacturing flexibility was analyzed on a conceptual level, in which the I–T–O model was used to distinguish between the needed and available flexibility. Lastly, based on a decision-theory-based procedure model, a methodological approach for evaluating Industry 4.0 solutions is suggested. This methodological approach is intended to support transparent assessments tailored to different technological concepts as well as the affected types of flexibility (available and needed). It is able to integrate existing methods from decision and investment appraisal theory as well as specific flexibility-related approaches. The results of the paper will be useful for both academicians and practitioners. Besides enabling quantitative evaluations, the methodological approach can be used by companies as a structured path to explore the possible ways they can increase their manufacturing flexibility. [ABSTRACT FROM AUTHOR]

In this paper, an approach for selection of materials in metal additive manufacturing based on three-way decision-making is proposed. The process of this approach is divided into three stages. First, a decision matrix for a material selection problem in metal additive manufacturing is established based on the basic components of the problem and normalised via a ratio model and a unified rule. Second, the summary loss function, conditional probability, and expected losses of each alternative material are calculated according to the weighted averaging operator, grey relational analysis, and the three-way decision theory, respectively. Third, the three-way decision-making results for the problem are generated according to the developed generation rules and the best material for the problem is selected based on the generated results. The application of the approach is illustrated via a material selection example in metal additive manufacturing. The effectiveness of the approach is demonstrated via a quantitative comparison with several existing approaches. The demonstration results suggest that the proposed approach is as effective as the existing approaches and is more flexible and advantageous in solving a material selection problem in metal additive manufacturing. [ABSTRACT FROM AUTHOR]

The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster–Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms. [ABSTRACT FROM AUTHOR]

Emergency evacuation simulation is significant for architectural design and emergency plan implementation. To explore the influence of evacuees' physical behavior and evacuees' decisions on the evacuation process, as well as to address the problems of traditional emergency evacuation models with insufficient detail of the situation (realism), low reusability, poor operability, and lack of subsequent scalability, this paper first analyzed pedestrian characteristics in emergencies. To describe pedestrian decision-making in an emergent evacuation situation, a multi-agent design based on decision theory was proposed, solving the multi-agent decision-making problem in an emergency evacuation environment by the A* algorithm. Then the designed multi-agent was embedded into the social force model by AnyLogic software. Finally, the model reproduces the pedestrian evacuation process in an emergency evacuation situation on the built platform, depicting three kinds of typical behaviors: pedestrian partnering, obstacle avoidance, and exit competition. In addition, this study also analyzed a large student apartment building by example and proposed corresponding optimization solutions to improve its evacuation capacity through simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy and reserve scheduling model for power systems with high penetration of wind turbines (WTs). The objective of the proposed model is to minimize the total operation cost of the system while static and dynamic security is guaranteed by preserving the frequency nadir, RoCoF, and quasi‐steady‐state frequency in the predefined range. Likewise, a supervisory, control, and data acquisition (SCADA) system is developed which allows Vanadium Redox Flow Batteries (VRFBs) to continuously communicate and participate in the primary frequency response. To cope with the uncertainties, adaptive information gap decision theory is used that ensures a target operating cost for the risk‐averse operator of the power system. The proposed scheduling model is applied on a modified IEEE 39 bus test system to verify the impacts of the fast reserve provided by the VRFBs in the dynamic frequency security enhancement of the power system with high penetration of WTs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a class of general pretest estimators for the univariate normal mean. The main mathematical idea of the proposed class is the adaptation of randomized tests, where the randomization probability is related to a shrinkage parameter. Consequently, the proposed class includes many existing estimators, such as the pretest, shrinkage, Bayes, and empirical Bayes estimators as special cases. Furthermore, the proposed class can be easily tuned for users by adjusting significance levels and probability function. We derive theoretical properties of the proposed class, such as the expressions for the distribution function, bias, and MSE. Our expressions for the bias and MSE turn out to be simpler than those previously derived for some existing formulas for special cases. We also conduct simulation studies to examine our theoretical results and demonstrate the application of the proposed class through a real dataset. [ABSTRACT FROM AUTHOR]

This paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm named combination search strategy (CSS)‐based improved particle swarm optimisation (IPSO) to address the resource allocation problem of multiple jammers. First, the authors propose a CSS to effectively broaden the limited search range of the two‐step solving framework. This method not only simplifies the solution framework but also considers the combined relationship between beam pointing and transmit power to determine the global solution to the original problem directly. Second, the authors propose the IPSO because the complexity of the decision variables is increased by CSS. This method can change the focus of searching the optimal solution at different stages, correct the direction of particle evolution over time and avoid the interference between the variables. Finally, this study simulates the problem of resource allocation of multiple jammers based on the CSS‐IPSO. Based on the simulation results, the combined search strategy can obtain better resource allocation results in a short time, and the IPSO algorithm can further improve the accuracy and stability of the resource allocation results. [ABSTRACT FROM AUTHOR]

This consideration is critical in our study because judges' decisions can affect individual arrestees. Instead of engaging with this philosophical debate, we examine Dawid's recommendation to use the statistical decision theory for answering the following questions: (1) 'should we present PSA to the judge before she makes her decision, or not? As explained above, to decide on the use of PSA, policy makers need to understand if PSA helps judges achieve their goal of preventing a negative outcome while avoiding unnecessarily harsh decisions. Finally, several commentators asked why our study design did not involve blinding of judges either to the intervention or to the fact of the study. [Extracted from the article]