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

Evidential decision theory (EDT) says that the choiceworthiness of an option depends on its evidential connections to possible outcomes. Causal decision theory (CDT) holds that it depends on your beliefs about its causal connections. While Newcomb cases support CDT, Arif Ahmed has described examples that support EDT. A new account is needed to get all cases right. I argue that an option A's choiceworthiness is determined by the probability that a good outcome ensues at possible A‐worlds that match actuality in the facts causally unaffected by your decision (the "unaffected facts"). Moreover, you should evaluate A on the assumption that A is compossible with the unaffected facts. This view entails that you should use EDT when evaluating A on the assumption that the unaffected facts determine your action, but use CDT when assessing A on the opposite assumption. A's choiceworthiness equals a weighted average of these conditional assessments. The weights are determined by your beliefs about whether the unaffected fact determine your action. This account gets both Newcomb and Ahmed cases right. According to an influential view, whether you take the unaffected facts to determine your action can make a difference to whether you can regard yourself as free and the action as being under your control. While my account is neutral on this issue, it entails that whether you take the unaffected facts to determine your action is important in a different way: it matters to whether you should follow EDT or CDT. [ABSTRACT FROM AUTHOR]

Probabilistic linguistic term set (PLTS), which consists of multiple linguistic terms and their probabilities, has been proposed to tackle qualitative information in informs of linguistic expressions. Since experts are inclined to compare alternatives in pairs, the probabilistic linguistic preference relation (PLPR), a matrix whose elements are PLTSs, has attracted wide attention since it was first introduced in 2016. Fruitful research achievements regarding PLPRs have been generated, especially in group decision making (GDM). This paper reviews 88 selected articles published from 2016 to June 4, 2023 regarding PLPRs, and presents a review of researches on theory and applications of PLPRs. First, we conduct a bibliometric study of these selected articles in terms of publication and citation trends, most productive countries/regions, categories of publications and keyword co-occurrence relationships. Next, the theory of PLPRs including definition, missing element deduction, consistency checking, prioritization, and decision-making analysis methods are recalled. We also review real-world applications of these publications and find that PLPRs have been mainly applied in four areas, including healthcare management, project evaluation, environment and energy management, and emergency management. In the end, we propose future research directions related to the probabilistic linguistic decision-making theory and provide insights for researchers and practitioners who have an interest in complex linguistic decision making. [ABSTRACT FROM AUTHOR]

This article is devoted to methods of processing random processes. This task becomes particularly relevant in cases where the random process is broadband and non-stationary; then, the measurement of a random process can be associated with an assessment of its probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation with a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real-time pace (for example, radio telemetry systems in spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods, to determine probability characteristics in this case is not possible. In addition, the on-board computing systems of spacecraft operate under conditions of restrictions on mass-dimensional characteristics and energy consumption. Therefore, there is a need to apply accelerated methods of processing measured random processes. This article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall's statistics is considered, as are methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. This article presents the results of statistical modeling using the Mathcad program. [ABSTRACT FROM AUTHOR]

David Lewis argued that Newcomb's Problem and the Prisoner's Dilemma are "one and the same problem" or, to be more precise, that the Prisoner's Dilemma is nothing else than "two Newcomb problems side by side" (Lewis Philosophy and Public Affairs 8:235–240, 1979: 235). It has been objected that his argument fails to take into account certain epistemic asymmetries which undermine the one-problem thesis. Sobel (1985) acknowledges that many tokens satisfy the structural requirements of both problems, while questioning the generality of the thesis. Bermúdez (Analysis 73:423–429, 2013), on the other hand, argues that there is a deeper structural conflict between the two problems in the sense that the epistemic requirements that give Newcomb's Problem its force are precisely those that prevent it from being a Prisoner's Dilemma. I argue that the epistemic asymmetry objections raised by Sobel and Bermúdez fail to undermine the one-problem thesis. A different type of objection raised by Bermúdez, one which relies on the contrast between parametric and strategic choices, fares no better. Although NP is a problem that has been primarily studied for its implications to decision theory, it contains enough game theoretic elements to justify the claim that a juxtaposition of two such problems can be thought of as a strategic game. [ABSTRACT FROM AUTHOR]

The degrees of belief of rational agents should be guided by the evidence available to them. This paper takes as a starting point the view—argued elsewhere—that the formal model best able to capture this idea is one that represents degrees of belief using Dempster–Shafer belief functions. However, degrees of belief should not only respect evidence; they also guide decision and action. Whatever formal model of degrees of belief we adopt, we need a decision theory that works with it: that takes as input degrees of belief so represented. The task of this paper is to develop such a decision theory for the belief function model of degrees of belief. This is not the first paper to attempt that task, but compared to the existing literature it takes a more abstract route to its destination, via a consideration of the very idea of rational decision making in light of one's beliefs and desires. After presenting the new decision theory and comparing it to existing views, the paper goes on to consider diachronic decision situations. [ABSTRACT FROM AUTHOR]

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

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

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

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

Existing methods for optimal control struggle to deal with the complexity commonly encountered in real-world systems, including dimensionality, process error, model bias and data heterogeneity. Instead of tackling these system complexities directly, researchers have typically sought to simplify models to fit optimal control methods. But when is the optimal solution to an approximate, stylized model better than an approximate solution to a more accurate model? While this question has largely gone unanswered owing to the difficulty of finding even approximate solutions for complex models, recent algorithmic and computational advances in deep reinforcement learning (DRL) might finally allow us to address these questions. DRL methods have to date been applied primarily in the context of games or robotic mechanics, which operate under precisely known rules. Here, we demonstrate the ability for DRL algorithms using deep neural networks to successfully approximate solutions (the “policy function” or control rule) in a non-linear three-variable model for a fishery without knowing or ever attempting to infer a model for the process itself. We find that the reinforcement learning agent discovers a policy that outperforms both constant escapement and constant mortality policies—the standard family of policies considered in fishery management. This DRL policy has the shape of a constant escapement policy whose escapement values depend on the stock sizes of other species in the model. [ABSTRACT FROM AUTHOR]

The quality of the input data is amongst the decisive factors affecting the speed and effectiveness of recurrent neural network (RNN) learning. We present here a novel methodology to select optimal training data (those with the highest learning capacity) by approaching the problem from a decision making point of view. The key idea, which underpins the design of the mathematical structure that supports the selection, is to define first a binary relation that gives preference to inputs with higher estimator abilities. The Von Newman Morgenstern theorem (VNM), a cornerstone of decision theory, is then applied to determine the level of efficiency of the training dataset based on the probability of success derived from a purpose-designed framework based on Markov networks. To the best of the author's knowledge, this is the first time that this result has been applied to data selection tasks. Hence, it is shown that Markov Networks, mainly known as generative models, can successfully participate in discriminative tasks when used in conjunction with the VNM theorem. The simplicity of our design allows the selection to be carried out alongside the training. Hence, since learning progresses with only the optimal inputs, the data noise gradually disappears: the result is an improvement in the performance while minimising the likelihood of overfitting. [ABSTRACT FROM AUTHOR]

Statistical treatment rules map data into treatment choices. Optimal treatment rules maximize social welfare. Although some finite sample results exist, it is generally difficult to prove that a particular treatment rule is optimal. This paper develops asymptotic and numerical results on minimax-regret treatment rules when there are many treatments. I first extend a result of Hirano and Porter (Econometrica 77:1683–1701, 2009) to show that an empirical success rule is asymptotically optimal under the minimax-regret criterion. The key difference is that I use a permutation invariance argument from Lehmann (Ann Math Stat 37:1–6, 1966) to solve the limit experiment instead of applying results from hypothesis testing. I then compare the finite sample performance of several treatment rules. I find that the empirical success rule performs poorly in unbalanced designs, and that when prior information about treatments is symmetric, balanced designs are preferred to unbalanced designs. Finally, I discuss how to compute optimal finite sample rules by applying methods from computational game theory. [ABSTRACT FROM AUTHOR]

The Maximin and Choquet expected utility theories guide decision-making under ambiguity. We apply them to hypothesis testing in incomplete models. We consider a statistical risk function that uses a prior probability to incorporate parameter uncertainty and a belief function to reflect the decision-maker's willingness to be robust against the model's incompleteness. We develop a numerical method to implement a test that minimizes the risk function. We also use a sequence of such tests to approximate a minimax optimal test when a nuisance parameter is present under the null hypothesis. [ABSTRACT FROM AUTHOR]

We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by Kitagawa et al. in (2022) "Treatment Choice with Nonlinear Regret". We find that optimal rules are always fractional, irrespective of the width of the identified set and precision of its estimate. The optimal treatment fraction is a simple logistic transformation of the commonly used t-statistic multiplied by a factor calculated by a simple constrained optimization. This treatment fraction gets closer to 0.5 as the width of the identified set becomes wider, implying the decision maker becomes more cautious against the adversarial Nature. [ABSTRACT FROM AUTHOR]

The statistical decision theory pioneered by (Wald, Statistical decision functions, Wiley, 1950) has used state-dependent mean loss (risk) to measure the performance of statistical decision functions across potential samples. We think it evident that evaluation of performance should respect stochastic dominance, but we do not see a compelling reason to focus exclusively on mean loss. We think it instructive to also measure performance by other functionals that respect stochastic dominance, such as quantiles of the distribution of loss. This paper develops general principles and illustrative applications for statistical decision theory respecting stochastic dominance. We modify the Wald definition of admissibility to an analogous concept of stochastic dominance (SD) admissibility, which uses stochastic dominance rather than mean sampling performance to compare alternative decision rules. We study SD admissibility in two relatively simple classes of decision problems that arise in treatment choice. We reevaluate the relationship between the MLE, James–Stein, and James–Stein positive part estimators from the perspective of SD admissibility. We consider alternative criteria for choice among SD-admissible rules. We juxtapose traditional criteria based on risk, regret, or Bayes risk with analogous ones based on quantiles of state-dependent sampling distributions or the Bayes distribution of loss. [ABSTRACT FROM AUTHOR]

Soft sets were introduced as a means to study objects that are not defined in an absolute way and have found applications in numerous areas of mathematics, decision theory, and in statistical applications. Soft topological spaces were first considered in Shabir and Naz ((2011). Computers & Mathematics with Applications 61 (7) 1786–1799) and soft separation axioms for soft topological spaces were studied in El-Shafei et al. ((2018). Filomat 32 (13) 4755–4771), El-Shafei and Al-Shami ((2020). Computational and Applied Mathematics 39 (3) 1–17), Al-shami ((2021). Mathematical Problems in Engineering 2021). In this paper, we introduce the effective versions of soft separation axioms. Specifically, we focus our attention on computable u-soft and computable p-soft separation axioms and investigate various relations between them. We also compare the effective and classical versions of these soft separation axioms. [ABSTRACT FROM AUTHOR]

Clinical curative effect is the core value and fundamental pursuit of medicine. The data-driven clinical quantitative evaluation model is an important research issue in clinical efficacy evaluation. The existing clinical efficacy evaluation methods have made a lot of contributions to the structured information of static attributes, but less attention is paid to the heterogeneous information with dynamic and random characteristics. This paper discusses a class of clinical efficacy evaluation decision-making problems that contain both time-series and dynamic characteristics and uses the three-way decision principle to describe these characteristics. The group decision-making theory is introduced to aggregate the evaluation information of time series dynamic features generated in the decision-making process. A time series dynamic three-way group decision model for clinical efficacy evaluation is constructed, the optimal decision rules with minimum expected loss objective are calculated and proofed in the proposed model, and then the optimal treatment alternative is obtained. Finally, taking the decision-making problem of clinical efficacy evaluation of traditional Chinese medicine (TCM) treatment of atopic dermatitis as the research object, based on the actual clinical data of Guangdong Provincial Hospital of TCM, the decision-making process and steps of the theoretical model constructed in this paper are verified and applied. The data-driven quantitative clinical evaluation model has achieved better application effects than the actual clinical scenarios. The research results in this paper provide new ideas and theoretical methods for real-world clinical efficacy evaluation research. [ABSTRACT FROM AUTHOR]

Abstract A model was developed to forecast and compare changes in species presence assessed with either eDNA or traditional observations. We use it to explore how ecosystem conditions could affect the suitability of eDNA for Environmental Impact Assessment. First, a deterministic model simulated the dynamics of the impacted population (called “receptor” in EIA) and their shed DNA fragment concentrations. Second, random distributions of receptor organisms and eDNA fragment quantities at steady state were simulated within the impacted spatial domain (called “project area”). Then, simple random samplings were performed for both the receptor and eDNA. Third, post‐sampling processes (eDNA extraction, amplification, and analysis) were simulated to estimate the taxon detection probability. Fourth, we simulated an impact by modifying the growth, mortality, and mobility (null, passive, and active) parameters of the receptor taxon. eDNA detection probability curves were then estimated for a range of environmental sample volumes by fitting a Weibull cumulative distribution function. A F‐like statistic compared detection curves before and after impact. Statistically significant differences were detected with eDNA in impact scenarios where receptor taxon growth rate decreased and receptor mobility was null or passive. In scenarios where the project area accumulates DNA shed from multiple categories of the same taxon (e.g., from dead organisms if mortality increased or when individuals can cross project area boundaries), it is difficult to assess impact. Our study shows that results obtained from eDNA sampling will not always agree with an impact classically assessed on a receptor population. One reason is that sources of the total eDNA pool are not identified. The modeling highlights the need: to do preliminary testing of sample sizes, to develop new approaches that will identify sources from the pool of extracted DNA, and to improve descriptions of the ecogeochemical processes required to forecast shed DNA reactivity.

The evaluation of risk and uncertainty in the context of software project management is the subject of this paper. This paper discusses the difficulties faced by project managers in handling uncertainty brought on by the complex nature of software projects and the ever evolving requirements of technology. A review of the literature, data production, visualization, statistical analysis, and mathematical modeling are included in this study. The goal of this study is to create a methodical approach to assist project managers in making decisions by considering the inherent uncertainty in software development and to find approaches and procedures that may successfully reduce risks, improve decision-making, and eventually result in the implementation of successful projects. The following tasks were carried out: to evaluate risk and uncertainty by examining the state-of-the-art in decision theory and its applications in software project management; to develop an integrated strategy that blends Monte Carlo Simulation with Decision Trees to assess risk and uncertainty in software project management; to generate data, visualize it, and perform statistical analysis to comprehend how project outcomes, costs, and time are affected; to identify important variables affecting project results and decision-making using decision trees; to use Monte Carlo simulation to create project scenarios and weigh the likelihood of each; and to supply project managers with knowledge and suggestions to help them make informed decisions and successfully manage risks. Methods. To evaluate risk and uncertainty in software project management, this paper analyzes the decision theory approaches currently used as well as Decision Trees and Monte Carlo Simulation techniques. Results. This study offers thorough insights into how project results, costs, and duration vary among various techniques. The critical factors that have a substantial influence on project success are shown through decision trees. According to the study’s findings, combining decision theory and statistical analysis equips project managers to make wise decisions despite uncertainty. Conclusions. Project managers may improve decision making, risk reduction, and overall project success by applying these cutting-edge approaches. To adapt these techniques to unique software project management contexts and real-world situations, further study and implementation in practice are necessary. With the use of such techniques, the software development sector would be better able to manage the complexity of projects and provide good results within set financial and time parameters.

Product Lifecycle Management (PLM) systems support industrial organisations in managing their product portfolios and related data across all phases of the product lifecycle. PLM seeks to enhance an organisation's ability to manage its product development activities and support collaboration across organisational functions and business units, and between organisations. Effective decision-making is vital for the successful management of products over their lifecycle. However, decision-making is an under-researched area in PLM. We argue that decision-making theory and group decision support concepts can be brought to bear to enhance PLM decision-making processes. We present and justify a set of six principles to support decision-making in a PLM context. The paper highlights the need to consider and capture decisions as distinct units of PLM knowledge to support product lifecycle management. We derive a generic information flow and a group decision support structure for PLM decision-making that encapsulates the six principles. Three industrial cases are analysed to illustrate the application and value of the principles in supporting decision-making. The principles enable PLM decisions to be codified, recorded, and reviewed. Decision-making processes can be reused where appropriate. The principles can support future innovations that may affect PLM, such as ontological and semantic reasoning and Artificial Intelligence. [ABSTRACT FROM AUTHOR]

The article reflects on the first 20 years of the journal Decision Analysis. It discusses the themes that have been explored in the field and how they have evolved over time. The early years of the journal focused on documenting applications of decision analysis and unifying the literature in the field. Foundational methodology papers, behavioral decision theory, security-related considerations, and group decisions have also been prominent topics. The article also highlights the increasing attention given to social media and environmental sustainability in decision analysis. [Extracted from the article]

Many non-profit organisations (NPOs) deliver their services and mission through volunteers. Brand has been shown to be a powerful influence on the decision to volunteer. What was not known was the role that brand plays in volunteer choice between NPOs. Understanding this enables NPOs to be more effective at attracting the volunteers they need, particularly given limited budgets. Using Framework Analysis with a large qualitative sample, this paper contributes to that gap in knowledge. The research identifies three constructs driving volunteer choice of NPOs, sources of Brand Knowledge, level of Brand Engagement, and the behavioural process of choice, labelled Brand Discovery. Through exploring the relationship between these constructs, the study points to significant implications for NPOs for volunteer recruitment, importance of brand presence, and competitive set. The study makes several contributions to theory and practice. It extends volunteer motivation theory to examine non-profit brand choice. It builds on Symbolic Consumption Theory and Decision-Making Theory to define patterns of volunteer decision-making behaviour. It describes automatic, explicit, or considered decision-making despite high-involvement behaviour. As a result, the research calls for a new perspective on non-profit brands when seen through a volunteer lens, described as Business to Volunteer (B2V). [ABSTRACT FROM AUTHOR]

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

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]

Past corruption research at the individual level has mainly focused on demographics, personality, attitudes, or morality related variables. Until now, only a few studies have focused on the intra-individual psychological mechanisms of corruption. Building on normative decision-making theory, the present study attempts to shed further light on the internal mechanisms that lead to the decision that corruption is a viable path. Following an informed grounded theory approach, we conducted semi-structured interviews with 38 Indonesian prisoners who have been convicted of corruption. Guided by a multi-step decision-making process, including problem recognition, information search, and evaluation of the information, our results revealed unique insights into individuals' considerations that led to corruption. We elaborate on interrelations between these stages and explore new forms of corrupt decision-making elements within this process. Theoretical implications for corruption research and the practical implications for anti-corruption programs of these findings are discussed. [ABSTRACT FROM AUTHOR]

Bridges and tunnels are the critical parts of the transport infrastructure. The efficient maintenance of their structural performance during service life requires strategies for scheduling inspections and possible structural and/or organization interventions over time. Infrastructure owners have developed such strategies that are characterized by a corrective (failure based) and predetermined preventive (e.g. time‐based or use‐based) maintenance approach. It is broadly recognized that the currently applied strategies do not provide the basis for the optimal allocation of resources into maintenance of bridges and tunnels of the traffic infrastructure. The complexity of defining efficient and proactive maintenance strategies of structures is also well recognized by the academia, and promising solutions for maintenance decision‐making based on structural reliability methods and Bayesian Decision Theory have been developed and broadly discussed in the last decades. The suggested methods have, in principle, the potential to become the basis for efficient decision making in bridge and tunnel maintenance. However, they are not yet well implemented in a practical context. In the current contribution we identify the gap between the technological developments in research and the corresponding applied knowledge in practice. Furthermore, we discuss how this gap might be closed. Special emphasis is directed to the corresponding role of standardization and education. [ABSTRACT FROM AUTHOR]

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

The question how and why the First World War broke out has filled entire learned libraries, but its no less salient and consequential counterpart—how it got to be so protracted when so many of the illusions that contributed to the outbreak were already shattered by Christmas 1914—has received much less attention, though recent scholarship has begun to close the gap. The aims and anxieties of the respective parties certainly played a prime part on both sides, as did their continued hopes of winning and the complications of coalition warfare. But something more insidious counted for just as much: namely how the horrendous manner in which the war began—grievously disappointing expectations on all sides and producing a staggering five million casualties within only a few months—did not induce, as the standards of textbook rationality might suggest, a readiness to recognise where things were headed and a consequent willingness to change course. Instead of prompting the belligerents to heed the sunk cost principle and cut losses by a timely settlement, the opening months had just the opposite effect, hardening positions that should have been discredited, and leaving the belligerents all the more determined to keep fighting to the bitter end at practically any cost not only to their enemies but also to themselves. How this was possible will be explained, in part, by making connections with prospect and decision theory. [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]

For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non‐outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub. [ABSTRACT FROM AUTHOR]

If the laws are deterministic, then standard theories of counterfactuals are forced to reject at least one of the following conditionals: 1) had you chosen differently, there would not have been a violation of the laws of nature; and 2) had you chosen differently, the initial conditions of the universe would not have been different. On the relevant readings—where we hold fixed factors causally independent of your choice—both of these conditionals appear true. And rejecting either one leads to trouble for philosophical theories which rely upon counterfactual conditionals—like, for instance, causal decision theory. Here, I outline a semantics for counterfactual conditionals which allows us to accept both (1) and (2). And I discuss how this semantics deals with objections to causal decision theory from Arif Ahmed. [ABSTRACT FROM AUTHOR]

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

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

Due to the uncertainty of daily reservoir inflow and market clearing power price, the market-oriented operation of cascade hydropower stations faces both transaction risk in the bidding stage and performance risk in the operation stage. It is difficult to obtain the fluctuation range of uncertain variables and establish a reasonable bidding strategy when the expected power sale profit is met. This work developed a medium-term scheduling and transaction decision method for cascade hydropower stations based on information gap decision theory (IGDT) and prospect theory. For performance risk in the operation stage, the IGDT-based robust optimization model solves the maximum fluctuation range of the uncertain variables under different expected profits from power sale. For transaction risk in the bidding stage, prospect theory analyzes the bidding strategy of cascade hydropower stations according to the fluctuation range of uncertain variables, and the corresponding bidding decision model and solution are provided. A cascaded hydropower system in Southwest China was used as a case study to verify the proposed method. The results showed that this method could provide an acceptable fluctuation range of the uncertain variables and a bidding strategy to ensure the expected profit. According to different risk preferences, decision makers can evaluate a range of strategies to optimize bidding and operation, to guarantee the expected profit of cascaded hydropower stations. [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]

Mathematics is a limited component of solutions to real‐world problems, as it expresses only what is expected to be true if all our assumptions are correct, including implicit assumptions that are omnipresent and often incorrect. Statistical methods are rife with implicit assumptions whose violation can be life‐threatening when results from them are used to set policy. Among them are that there is human equipoise or unbiasedness in data generation, management, analysis, and reporting. These assumptions correspond to levels of cooperation, competence, neutrality, and integrity that are absent more often than we would like to believe. Given this harsh reality, we should ask what meaning, if any, we can assign to the P‐values, "statistical significance" declarations, "confidence" intervals, and posterior probabilities that are used to decide what and how to present (or spin) discussions of analyzed data. By themselves, P‐values and CI do not test any hypothesis, nor do they measure the significance of results or the confidence we should have in them. The sense otherwise is an ongoing cultural error perpetuated by large segments of the statistical and research community via misleading terminology. So‐called inferential statistics can only become contextually interpretable when derived explicitly from causal stories about the real data generator (such as randomization), and can only become reliable when those stories are based on valid and public documentation of the physical mechanisms that generated the data. Absent these assurances, traditional interpretations of statistical results become pernicious fictions that need to be replaced by far more circumspect descriptions of data and model relations. [ABSTRACT FROM AUTHOR]

Though the benefits of mobile adoption in banking institutions are extensively recognized, there is a low pace of its adoption among customers. The establishment of behavioral factors that can motivate banking customers to adopt mobile banking has been a concern. Provided that no single framework can fit all sizes, this study builds on past research to invest in behavioral factors among customers toward MB adoption. Through a cross-sectional survey design, 325 questionnaires were distributed among university students in Tanzania to assess customer behavior that can motivate MB adoption. Using structural equation modeling (AMOS 21), the study found that when customers possess judgmental heuristic knowledge that allows them to understand how risks associated with the adoption of MB can be resolved, they are motivated to adopt the innovation. The study recommends the importance of providing knowledge to their customers through various mechanisms and the benefits associated with mobile banking adoption. [ABSTRACT FROM AUTHOR]

Fuzzy sets, which have a crucial role in the decision making theory, model uncertainty by means of membership and non-membership functions. q-rung orthopair fuzzy sets, which are the natural extension of fuzzy, intuitionistic fuzzy and Pythagorean fuzzy sets, are quite successful in modeling data thanks to their larger domains. However, in a q-rung orthopair fuzzy set the membership and non-membership degrees of an element to a set are given just by a pair of certain numbers from the closed interval [0, 1] that causes a strict modelling. Various types of interval valued fuzzy sets, multi fuzzy sets or circular fuzzy sets change these strict modelling with a sensitive one. In this paper, we introduce a new fuzzy set notion via continuous functions that take values on a closed interval to provide a more sensitive tool in decision making theory. In this new fuzzy set notion, the membership and non-membership degrees of an element to a fuzzy set are represented by continuous functions instead of numbers. Actually, we study not only with points, but also with functions by taking into account the sufficiently large and continuous neighborhoods of the points. Thus more sensitive and realistic models are made by relieving the precision of the fuzzy data or linguistic argument. The data carried to function space environment is processed with the function theoretic tools via aggregation functions, distance measures or score functions. This fact distinguishes the new fuzzy set notion from the other continuous extensions in the literature such as interval valued or circular structures. Moreover, we provide an extended Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in this new fuzzy environment and apply it to a multi criteria group decision making problem from the literature. Finally, we provide a comparison analysis and a complexity analysis. We also visualise the time complexity of the proposed extended TOPSIS for different numbers of decision makers. [ABSTRACT FROM AUTHOR]

Three-way decision models have received substantial interest grounded in decision-theoretic rough sets and Bayesian decision theory. Single-valued neutrosophic sets are extremely useful for handling uncertain and inconsistent information, making them a valuable tool that is commonly applied in decision-making. In a three-way decision problem involving a piece of single-value neutrosophic information, the losses of each equivalence class under different actions can usually be identified with some accuracy. A critical aspect of the three-way decision problem centers around appropriate handling of the loss function. This paper proposes a novel approach to rank loss functions in each equivalence class of three-way decisions, based on the TODIM method and operates within a singlevalued neutrosophic environment. Furthermore, a numerical experiment on the location of a breakfast restaurant is used to to assess the model compared to some existing related models, with the aim of demonstrating its validity and soundness. [ABSTRACT FROM AUTHOR]

Purpose: Commercial property development builds floor space in anticipation of potential, but unknown, future demand, making it particularly prone to risk and uncertainty. The research explores the degree to which property developer decision-making is objective and rational and the degree to which it relies on behavioural instincts and intuition. Decision-making theory, including heuristics, is considered and its prevalence in the field of commercial property development is examined. Design/methodology/approach: A "dual-processing" decision-making model, comprising intuitive System 1 and objective System 2 processing, is proposed and tested. Inductive research using template analysis of interviews, with "high status" commercial property developers, explored whether the model offers an accurate representation of developers' behaviour and effective lens through which to examine decisions made under conditions of risk and uncertainty. Findings: Participants believed they adopted objective and rational approaches to complex commercial property development decisions. Analysis of interviews reveals that System 1 heuristics and intuition play significant roles in decision-making behaviour, leading to potential bias and systematic error. The research concludes that the dual-processing model provides a useful lens through which to better understand the decision-making approach adopted by commercial property developers. Originality/value: The research represents the rare application of behavioural theory to the realm of commercial property development and provides new and original insight as to how important investment decisions are made under conditions of risk and uncertainty, with implications for professional practice. [ABSTRACT FROM AUTHOR]

The environmental concerns of the use of fossil fuels and the financial interests of governments have raised the necessity of installing renewable power plants in the distribution network. In order to make maximum use of these resources, it is necessary to calculate the hosting capacity of the network. The hosting capacity of the distribution network is the maximum allowed capacity for installing distributed generation in the network, according to operating restrictions. Wind farms are one of the renewable resources used in the power system. The presence of wind farms intensifies the uncertainties of network operation. In this article, the theory of Information Gap Decision has been used to model the uncertainties in the production of wind farms and the amount of hourly load to calculate the hosting capacity of the network. The strategies of energy management of the network have been taken into account in order to increase the capacity. The network energy management strategies, considered in this article, include static var compensators, network reconfiguration, and power factor control of wind turbines. The correctness and the accuracy of the proposed modeling has been studied on 33 bus IEEE networks. [ABSTRACT FROM AUTHOR]

Internet of drones (IoD) are commonly constructed with unmanned vehicles, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. These IoDs are interact with zone service providers (ZSPs) to achieve the goal of assisting drones in accessing controlled agriculture services. The utilization of drones in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area by analysing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. objectively and equitably assigns a weight to multiple service scheduling parameters based on maldistributed decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priori-ty accordingly Hence, this paper proposes reinforcement learning-based topology-aware routing protocol with priority scheduling (RLTARP) to provide reliable combinations between the source and destination. It also improves the routing decision by considering two-hop neighbour nodes, extending the local view of the network topology. The priority scheduling method adopts maldistributed decision making theory, to find the group of priorities based on service request. The proposed RLTARP is compared with three existing methods such as DroneCOCoNet, Markov decision process (MDP)and deep deterministic policy gradient (DDPG) and hence it produces 46.34% of packet delivery ratio, 67.49% of end to end delay, 16.45% of routing overhead, 13% of energy consumption and 97.6% of network lifetime. [ABSTRACT FROM AUTHOR]