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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 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]

The behavior intention estimation and interaction between Autonomous Vehicles (AV) and human traffic participants are the key problems in Automatic Driving System (ADS). When the classical decision theory studies implicitly assume that the behavior of human traffic participants is completely rational. However, according to the booming quantum decision theory in recent years and actual traffic cases, traffic behaviors and other human behaviors are often irrational and violate the assumptions of classical cognitive and decision theory. This paper explores the decision-making problem in the two-car game scene based on quantum decision theory and compares it with the current mainstream method of studying irrational behavior-Cumulative Prospect Theory (CPT) model. The comparative analysis proved that the Quantum Game Theory (QGT) model can explain the separation effect which the classical probability model can't reveal, and it has more advantages than CPT model in dealing with game scene decision-making. When two cars interact with each other, the QGT model can consider the interests of both sides from the perspective of the other car. Compared with the classical probability model and CPT model, the QGT is more realistic in the behavior decision-making of ADS. [ABSTRACT FROM AUTHOR]

This paper characterized the dietary characteristics of the catering consumer group from sales data of Chinese optional restaurants and predicted the dish sales of the restaurant. Due to the huge changes in the daily dishes served in Chinese optional restaurants, traditional time series forecasting methods can hardly solve the problem. This paper proposed a forecas ting model based on decision theory. It used the MACBETII method and the expected utility theory to transform the restaurant 's dishes into a unified utility value, which served as the basis for the dining group to choose dishes, used the decision weight to represent the dietary characteristics of the consumer group, and then designed a decision model for catering consumers based on the behavioral decision theory. This paper used the running data of Chinese optional restaurants for training. The crossentropy of the prediction model on the training set is 0. 082 and the cross -entropy on the test set is 0 . 086. Compared with the prediction model based on neural network, the model is more accurate and has good interpretability. Therefore, the model can accurately predict the sales volume of Chinese optional restaurants under a given dish supply, thereby supporting the efficient operation of Chinese optional restaurants . [ABSTRACT FROM AUTHOR]

In this paper, we discuss aggregate measures of marginal costs of public funds (MCF) in populations that are heterogeneous with respect to observed as well as unobserved characteristics. We first discuss how to compute MCF in selected examples of traditional (textbook) labour supply models. Next, we review two types of discrete labour supply models proposed in the literature. Subsequently, we discuss how to calculate aggregate measures of MCF for discrete labour supply models. Finally, we apply an estimated two-sector discrete labour supply model to compute MCF based on Norwegian data. [ABSTRACT FROM AUTHOR]

This paper investigates the robustness of the conventional mean-variance (MV) optimization model by making two adjustments within the MV formulation. First, the portfolio selection based on a behavioral decision-making theory that encapsulates the MV statistics and investors psychology. The second aspect involves capturing the portfolio asset dependence structure through copula. Using the behavioral MV (BMV) and the copula behavioral MV (CBMV), the results show that stocks with lower behavioral scores outperform counterpart portfolios with higher behavioral scores. On the other hand, in the Forex market, the reverse is observed for the BMV approach, while the CBMV remains consistent. [ABSTRACT FROM AUTHOR]

Decision theory can be used to test the logic of decision making—one may ask whether a given set of decisions can be justified by a decision‐theoretic model. Indeed, in principal–agent settings, such justifications may be required—a manager of an investment fund may be asked what beliefs she used when valuing assets and a government may be asked whether a portfolio of rules and regulations is coherent. In this paper we ask which collections of uncertain‐act evaluations can be simultaneously justified under the maxmin expected utility criterion by a single set of probabilities. We draw connections to the fundamental theorem of finance (for the special case of a Bayesian agent) and revealed‐preference results. [ABSTRACT FROM AUTHOR]

Purpose: This paper aimed to study the optimal way to implement and subsequently manage the new metropolitan lines in the Kuwait city as well as to justify them from a social and economic-financial viability standpoint and the most important aspects of transport infrastructure projects viability. The implementation of new modes of transport in an urban environment requires the study of several factors that allow their incorporation on the transport system in the most efficient method. These factors include reviewing the organization of the transport system. The concept of transport system organization, financing models and common trends was defined. Finally, the Public Transport Authority was created to define global objectives and establish specific policies to achieve them was suggested. Design/methodology/approach: After the analysis of the "status quo" of transport systems organization, the financing and management schemes, both infrastructure projects and the provision of rail service were analysed. The characteristics of the PPP scheme (Public–Private Participation) contract, advantages and disadvantages, its structure, and the definition and share of risk (matrix of risk, rights and obligations of the parties) as a key element of the PPP contracts were defined. From this point onwards, the legal framework in force in Kuwait was analysed, particularly the administrative and commercial regulations applying to this project and the authors verified that the economic-financial viability analysis suggested before can be established under this framework. Findings: For the viability of these alternatives, the authors developed a simulation economic-financial model that reflects Financial Statements for the "Society Vehicle Project" and are considered some minimum thresholds of profitability, both from the viewpoint of the partners sponsoring the project as from the standpoint of lenders, which make attractive participation of private initiative. Finally, there was a short socio-economic analysis to justify the project implementation from a social standpoint. It analyses and quantifies the reduction of operating expenses of other modes of transport, travelling times, accident rates, environmental pollution etc. All these factors affect the quality of life of the population of Kuwait and are the main reason to carry out this project. Several recommendations were raised that aimed at preparing the projects, bidding and selection of private partner and the articulation and implementation of projects. Originality/value: This research contributes to the existing body of knowledge through setting the framework for metro projects in arid regions with the hottest temperature on Earth such as Kuwait where such projects are missing completely. This work will be very helpful to governments and municipalities in taking investment decisions. It sets the strategy for utilizing the best of decision-making theory, identifying the reliability advantages and finding the larger economic effects. This work identified, through the analysis of alternative management options generally used in underground and rail projects in different parts of the world, the most convenient alternative in developing countries. It also clearly showed, through the analysis carried out on governmental contributions, how to obtain economic viability for such types of projects. Finally, it helped drawing a roadmap for preparing the projects, bidding and selection of private partner and the articulation and implementation of projects in Kuwait and Gulf Cooperation Countries (GCC). [ABSTRACT FROM AUTHOR]

Completeness is one of the basic assumptions about the rational preference relation in classical decision theory. Strongly and weakly consistent preferences are presented by abandoning the completeness of the rational preference relation. Some expansion and contraction conditions are proposed and the relationships between these conditions of rationality are discussed. The relationships between the conditions of rationality and boundedly rational choice behavior based on strongly and weakly consistent preferences are analyzed and discussed. Furthermore, an example about the choices of chocolates with interval ordinal numbers is given to explain some of the main conclusions in this paper. The results can be used as references for the study of boundedly rational decisions. [ABSTRACT FROM AUTHOR]

Growing evidence shows that long noncoding RNAs (lncRNAs) play an important role in cellular biological processes at multiple levels, such as gene imprinting, immune response, and genetic regulation, and are closely related to diseases because of their complex and precise control. However, most functions of lncRNAs remain undiscovered. Current computational methods for exploring lncRNA functions can avoid high-throughput experiments, but they usually focus on the construction of similarity networks and ignore the certain directed acyclic graph (DAG) formed by gene ontology annotations. In this paper, we view the function annotation work as a hierarchical multilabel classification problem and design a method HLSTMBD for classification with DAG-structured labels. With the help of a mathematical model based on Bayesian decision theory, the HLSTMBD algorithm is implemented with the long-short term memory network and a hierarchical constraint method DAGLabel. Compared with other state-of-the-art algorithms, the results on GOA-lncRNA datasets show that the proposed method can efficiently and accurately complete the label prediction work. [ABSTRACT FROM AUTHOR]

The point estimation problems that emerge in Bayesian predictive inference are concerned with random quantities which depend on both observable and non-observable variables. Intuition suggests splitting such problems into two phases, the former relying on estimation of the random parameter of the model, the latter concerning estimation of the original quantity from the distinguished element of the statistical model obtained by plug-in of the estimated parameter in the place of the random parameter. This paper discusses both phases within a decision theoretic framework. As a main result, a non-standard loss function on the space of parameters, given in terms of a Wasserstein distance, is proposed to carry out the first phase. Finally, the asymptotic efficiency of the entire procedure is discussed. [ABSTRACT FROM AUTHOR]

There are decision problems in which rational deliberation fails to result in choosing a pure act. This phenomenon is known as decision instability and has been discussed in the literature on causal decision theory. In this paper we investigate another type of instability, called structural instability in dynamical systems theory. Structural instability indicates that certain qualitative features of the process of rational deliberation are under-determined in a decision situation. We illustrate some of the issues arising from structural instability with a recent argument against causal decision theory proposed by Caspar Hare and Brian Hedden. We show that their argument is undermined by considerations arising from decision instability and structural instability. [ABSTRACT FROM AUTHOR]

Metanormativists hold that moral uncertainty can affect how we ought, in some morally authoritative sense, to act. Many metanormativists aim to generalize expected utility theory for normative uncertainty. Such accounts face the "easy problem of intertheoretic comparisons": the worry that distinct theories' assessments of choiceworthiness are incomparable. The easy problem may well be resolvable, but another problem looms: while some moral theories assign cardinal degrees of choiceworthiness, other theories' choiceworthiness assignments are merely ordinal. Expected choiceworthiness over such theories is undefined. Call this the "hard problem of intertheoretic comparisons." This paper argues that to solve the hard problem, we should model moral theories with imprecise choiceworthiness. Imprecise choiceworthiness assignments can model incomplete cardinal information about choiceworthiness, with precise cardinal choiceworthiness and merely ordinal choiceworthiness as limiting cases. Generalizing familiar decision theories for imprecise choiceworthiness to the case of moral uncertainty generates puzzles, however: natural generalizations seem to require reifying parts of the model that don't correspond to anything in normative reality. I discuss three ways of addressing this problem: by demystifying the reified elements by using them as promiscuously as possible; by constructing alternative decision theories that don't require the troublesome elements; and by employing an alternative model of metanormative decision problems, and of moral uncertainty generally. [ABSTRACT FROM AUTHOR]

The management of civil infrastructures in the aftermath of a seismic event is a concern for decision makers, which have to choose quickly among alternative actions with limited knowledge on the actual structural conditions. The availability of real time Structural Health Monitoring (SHM) data on the asset might be particularly useful. However, SHM data are not collected for free, and the cost of the SHM system should be compared with its associated benefit. A powerful tool to estimate such benefit is the Value of Information (VoI) from Bayesian decision theory. This paper provides a methodology to compute the VoI of SHM for seismic emergency management of roadway bridges. This methodology can be used by decision makers before the installation of a seismic SHM system to quantify the cost benefit of doing so and thus optimize the allocation of economic resources. Results show that the VoI is high when the expected costs of the decision alternatives (such as 'keep the bridge open' or 'close the bridge') evaluated without the SHM information are comparable. In this condition, which is highly dependent on the seismic hazard and on emergency management costs at stake, the SHM information provides the maximum support to decision making. [ABSTRACT FROM AUTHOR]

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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.

Late-onset toxicities often occur in phase I trials investigating novel immunotherapy and molecular targeted therapies. For trials with cohort based designs (such as modified toxicity probability interval, Bayesian optimal interval, and i3+3), patients are often turned away since the current cohort are still being followed without definite dose-limiting toxicities, which results in prolonged trial duration and waste of patient resources. In this paper, we incorporate a probability-of-decision framework into the i3+3 design and allow real-time dosing inference when the next patient becomes available. Both follow-up time for the pending patients and time to dose-limiting toxicities for the observed patients are used in calculating the posterior probability of each possible dosing decision. An intensive simulation study is conducted to evaluate the operating characteristics of the newly proposed probability-of-decision-i3+3 design under various dosing scenarios and patient accrual settings. Results show that the probability-of-decision-i3+3 design achieves comparable safety and reliability performances but much shorter trial duration compared to the complete designs. [ABSTRACT FROM AUTHOR]

Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, I diagnose the problems facing causal decision theory as a special case of the general phenomenon of decision‐dependence–the possibility that some input into our evaluation of options can be affected by the decision we end up making. I go on to develop a new decision theory, which treats each choice as a tournament between options competing in pairs, and show how it can capture attractive features of the causal view while avoiding the problems generated by decision‐dependence. [ABSTRACT FROM AUTHOR]

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. Multi-attribute decision-making (MADM) technique is a very effective and well-known tool to investigate fuzzy information more effectively. However, the selection of houses cannot be carried out by utilizing symmetry information, because enterprises does not have complete information, so asymmetric information should be used when selecting enterprises. Hamy mean (HM) operator is a feasible tool to handle strategic decision-making problems because it can capture the order between the finite input terms. Additionally, the complex interval-valued q-rung orthopair fuzzy (CIVq-ROF) setting is a broadly flexible and massively dominant technique to operate problematic and awkward data in actual life problems. The major contribution of this analysis is how to aggregate the collection of alternatives into a singleton set, for this we analyzed the technique of CIVq-ROF Hamy mean (CIVq-ROFHM) operator and CIVq-ROF weighted Hamy mean (Cq-ROFWHM) operator and some well-known results are deliberated. Keeping the advantages of the parameters in HM operators, we discussed the specific cases of the invented operators. To investigate the decision-making problems based on CIVq-ROF information, we suggested the following multi-attribute decision-making (MADM) technique to determine the beneficial term from the finite group of alternatives with the help of evaluating several examples. This manuscript showed how to make decisions when there is asymmetric information about enterprises. Finally, based on the evaluating examples, we try to discover the sensitive analysis and supremacy of the invented operators to find the flexibility and dominancy of the diagnosed approaches. [ABSTRACT FROM AUTHOR]

Pascal's wager is one of the most influential arguments in philosophy and decision theory. This paper reformulates Pascal's wager in terms of two-outcome finite payoffs (one before death and one after death). The proposed reformulation accentuates the lexicographic nature of decision maker's preferences. Lexicographic preferences violate the continuity axiom—a standard assumption of many theories of decision making under risk and uncertainty. Since Pascal's wager assumes lexicographic (i.e., discontinuous) preferences, it is appropriate to represent the latter with lexicographic utility and not conventional expected utility theory. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. A fuzzy decision-maker is used in this study to select the best solution from the optimally set goals by the Pareto Front beam method. In this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and MG clusters, with consideration for the uncertainty of renewable energy resources (RESs), is examined. An hourly robust energy management approach is presented for distribution systems under the penetration of renewable energy-based MGs. In addition to wind turbines and photovoltaics as RESs, the MGs are equipped with energy storage systems and micro-turbines. The uncertainty of renewable generation is demonstrated via the information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed model, it is tested on a 94 bus distribution test system using the general algebraic modeling system (GAMS) software. The results show the prominence of MGs clustering in improving the techno-economic characteristics of the distribution system and indicate the important consequences of clustered microgrids in optimal power transaction and distribution system operation. [ABSTRACT FROM AUTHOR]

Ant Colony Optimization (ACO) metaheuristic is a multi-agent system in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. Set Covering Problems (SCP), on the other hand, deal with maximizing the coverage of every subset while the weight nodes used must be minimized. In this paper, ACO was adapted and used to solve a case of Set Covering Problem. The adapted ACO for solving the SCP was implemented as a computer program using SciLab 5.4.1. The problem of determining the optimal location of Wi-Fi Access Points using the 802.11n protocol in the UP Los Banos Math Building (MB) was solved using this metaheuristic. Results show that in order to have 100% coverage of the MB, 7 access points are required. Methodology of the study can be adapted and results of the study can be used by decision makers on related optimization problems. [ABSTRACT FROM AUTHOR]

Purpose: Boundary decision is an important but underexplored theme in digital platform research. The boundary decision of digital platform enterprises (DPEs) differs from traditional organizations because of cross-side network effects (CNEs). This study intends to investigate whether transaction cost economics (TCE) and resource-based view (RBV), as classical organization boundary mechanisms, are still applicable for DPEs. Design/methodology/approach: To unfold the research problem, this study conducts a fuzzy-set qualitative comparative analysis (fsQCA) on the samples of 21 platform business units. Findings: The results show that the classical boundary decision theory still applies in the context of DPEs, but the cross-side network effects will affect boundary decision of DPEs. Originality/value: This study provides a new framework – integrates TCE, RBV and CNEs – to analyze boundary decision of DPEs. This paper also contributes to research on both organization boundary decision and platform governance. [ABSTRACT FROM AUTHOR]

Paper aims: the combination of the quality indices, a novel model called "Dynamic Growth Allocation Model (DGAM)", Fuzzy Decision Making Theory (FDM), Analytical Hierarchy Process (AHP) and the Evolutive Particle Swarm Optimization (EPSO) is proposed. Originality: the multi-objective optimization (with uncertainty) of the Argentine energy transition is not sufficiently studied. This combined methodology in this problem was not published and it had good, relatively easy and fast results. Research method: the optimization indices (EROI, CO2, IC and RP), the methodology used (DGAM, FDM, AHP and EPSO) and its results are analyzed. Main findings: (i) the nuclear energy allowed the renewable transition; (ii) the fossil dismantling and the investment in biomass and wind are needed; (iii) the EROI depends on the good load factor, useful life and performance. Implications for theory and practice: It is sought a minimum Renewable Participation (RP) of 20% of Argentina with a sustainable energy matrix. [ABSTRACT FROM AUTHOR]

This paper introduces two new paradoxes for standard deontic logic (SDL). They are importantly related to, but distinct from Ross' paradox. These two new paradoxes for SDL are the simple weakening paradox and the complex weakening paradox. Both of these paradoxes arise in virtue of the underlying logic of SDL and are consequences of the fact that SDL incorporates the principle known as weakening. These two paradoxes then show that SDL has counter-intuitive implications related to disjunctive obligations that arise in virtue of deontic weakening and in virtue of decisions concerning how to discharge such disjunctive obligations. The main result here is then that theorem T1 is a problematic component of SDL that needs to be addressed. [ABSTRACT FROM AUTHOR]

Frames and framing make one dimension of a decision problem particularly salient. In the simplest case, frames prime responses (as in, e.g., the Asian disease paradigm, where the gain frame primes risk-aversion and the loss frame primes risk-seeking). But in more complicated situations frames can function reflectively, by making salient particular reason-giving aspects of a thing, outcome, or action. For Shakespeare's Macbeth, for example, his feudal commitments are salient in one frame, while downplayed in another in favor of his personal ambition. The role of frames in reasoning can give rise to rational framing effects. Macbeth can prefer fulfilling his feudal duty to murdering the king, while also preferring bravely taking the throne to fulfilling his feudal duty, knowing full well that bravely taking the throne just is murdering the king. Such patterns of quasi-cyclical preferences can be correct and appropriate from the normative perspective of how one ought to reason. The paper explores three less dramatic types of rational framing effects: (1) Consciously framing and reframing long-term goals and short-term temptations can be important tools for self-control. (2) In the prototypical social interactions modeled by game theory, allowing for rational framing effects solves longstanding problems, such as the equilibrium selection problem and explaining the appeal of non-equilibrium solutions (e.g., the cooperative solution in the Prisoner's Dilemma). (3) Processes for resolving interpersonal conflicts and breaking discursive deadlock, because they involve internalizing multiple and incompatible ways of framing actions and outcomes, in effect create rational framing effects. [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]

Anti-exceptionalists about logic claim that logical methodology is not different from scientific methodology when it comes to theory choice. Two anti-exceptionalist accounts of theory choice in logic are abductivism (defended by Priest and Williamson) and predictivism (recently proposed by Martin and Hjortland). These accounts have in common reliance on pre-theoretical logical intuitions for the assessment of candidate logical theories. In this paper, I investigate whether intuitions can provide what abductivism and predictivism want from them and conclude that they do not. As an alternative to these approaches, I propose a Carnapian view on logical theorizing according to which logical theories do not simply account for pre-theoretical intuitions, but rather improve on them. In this account, logical theories are ameliorative, rather than representational. [ABSTRACT FROM AUTHOR]

The paper investigates what type of motivation can be given for adopting a knowledge-based decision theory (hereafter, KBDT). KBDT seems to have several advantages over competing theories of rationality. It is commonly argued that this theory would naturally fit with the intuitive idea that being rational is doing what we take to be best given what we know, an idea often supported by appeal to ordinary folk appraisals. Moreover, KBDT seems to strike a perfect balance between the problematic extremes of subjectivist and objectivist decision theory. We argue that these alleged advantages do not stand up to a closer scrutiny: KBDT inherits the same kinds of problems as alternative decision theoretic frameworks but doesn't retain any of the respective advantages. Moreover, differently from other knowledge-action principles advanced in the literature, KBDT cannot fully explain the intuitive connections between knowledge and rational action. We conclude that the most serious challenge for knowledge-based decision theorists is to provide a substantive rationale for the adoption of such a view. [ABSTRACT FROM AUTHOR]

L. A. Paul's Transformative Experience makes the claim that many important life decisions are epistemically and personally transformative in a way that does not allow us to assign subjective values to their outcomes. As a result, we cannot use normative decision theory to make such decisions rationally, or when we modify it to do so, decision theory leads us to choose in a way that is in tension with our authenticity. This paper examines Paul's version of decision theory, and whether this version in fact admits of the challenge she wants to raise. I focus on her psychological realist view of utilities and beliefs and her notion of rational, authentic preferences as informed by imaginative acquaintance. I argue that Paul fails to engage critically with traditional accounts of decision theory and, on closer inspection, it is not clear that her version of decision theory entails a tension between rational and authentic choice. More importantly, I argue that if her contribution is instead to bring to light the importance of authenticity alongside rational decision-making, the definition she provides of authenticity in fact undermines her argument. [ABSTRACT FROM AUTHOR]

This paper takes issue with an influential interpretationist argument for physicalism about intentionality based on the possibility of radical interpretation. The interpretationist defends the physicalist thesis that the intentional truths supervene on the physical truths by arguing that it is possible for a radical interpreter, who knows all of the physical truths, to work out the intentional truths about what an arbitrary agent believes, desires, and means without recourse to any further empirical information. One of the most compelling arguments for the possibility of radical interpretation, associated most closely with David Lewis and Donald Davidson, gives a central role to decision theoretic representation theorems, which demonstrate that if an agent's preferences satisfy certain constraints, it is possible to deduce probability and utility functions that represent her beliefs and desires. We argue that an interpretationist who wants to rely on existing representation theorems in defence of the possibility of radical interpretation faces a trilemma, each horn of which is incompatible with the possibility of radical interpretation. [ABSTRACT FROM AUTHOR]

In this paper, a new reliability‐based risk‐involved model is presented for multi‐period macrogrid expansion planning (MAGEP) by amalgamating the notion of community microgrids (CoMigs) within a market environment. The main targets of the proposed model are not only to develop a holistic framework for the MAGEP problem, but also to scrutinize the impacts of the macrogrid‐connected multi‐CoMigs on power supply reliability enhancement. To that end, the mathematical formulation of the proposed model falls into a nonconvex mixed‐integer nonlinear three‐level optimization problem. The lower‐level outlines a security‐constrained bid‐based‐pool electricity market aimed at providing an efficient platform for different CoMigs and market participants—generation companies (GenCos) and distribution companies (DisCos)—to freely trade electrical power across the macrogrid. The intermediate‐level represents an interactive decentralized multi‐period two‐stage microgrid expansion planning (MIGEP) problem in which optimal expansion plans of the CoMigs obtained in the first stage will be examined against the reliability restrictions in the second stage. The upper‐level, however, addresses a centralized multi‐period techno‐economic MAGEP problem, while handling the risks of planning arising from severe uncertainties in forecasted price, demand, and power generation of the CoMigs through a potent envelope‐bound information‐gap decision theory (IGDT). Due to its proper tackling of the non‐convex mixed‐integer nonlinear multi‐level optimization problems, the symphony orchestra search algorithm (SOSA) is widely employed for solving the proposed model. Effectiveness and capability of the newly developed model are numerically analyzed and verified using its implementation on the IEEE 30‐bus and IEEE 118‐bus test systems. [ABSTRACT FROM AUTHOR]

The Kies probability model was proposed as an alternative to the extended Weibull models as it provides a more efficient fit to some real-life data sets in comparison to the aforementioned models. The paper proposes classical and Bayesian inferences for the Kies distribution based on records. Maximum likelihood estimates are studied jointly with asymptotic and bootstrap confidence intervals. Moreover, Bayes estimates, along with credible intervals are discussed assuming squared and LINEX loss functions. The proposed estimation methods have been investigated and compared via simulation studies. A real data set has been analysed for illustrative purposes. [ABSTRACT FROM AUTHOR]

Motivation Probabilistic Identification of bacterial essential genes using transposon-directed insertion-site sequencing (TraDIS) data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We assess the effectiveness of our approach via analysis of both simulated data and three previously published Escherichia coli , Salmonella Typhimurium and Staphylococcus aureus datasets. These three bacteria have relatively well characterized essential genes which allows us to test our classification procedure using receiver operating characteristic curves and area under the curves. We compare the classification performance with that of Bio-Tradis, a standard tool for bacterial gene classification. Results Our method is able to classify genes in the three datasets with areas under the curves between 0.967 and 0.983. Our simulated synthetic datasets show that both the number of insertions and the extent to which insertions are tolerated in the distal regions of essential genes are both important in determining classification accuracy. Importantly our method gives the user the option of classifying essential genes based on the user-supplied costs of false discovery and false non-discovery. Availability and implementation An R package that implements the method presented in this paper is available for download from https://github.com/Kevin-walters/insdens. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Invasive species can lead to community-level damage to the invaded ecosystem and extinction of native species. Most surveillance systems for the detection of invasive species are developed based on expert assessment, inherently coming with a level of uncertainty. In this research, info-gap decision theory (IGDT) is applied to model and manage such uncertainty. Surveillance of the Asian House Gecko, Hemidactylus frenatus Duméril and Bibron, 1836 on Barrow Island, is used as a case study. Our research provides a novel method for applying IGDT to determine the population threshold (K ) so that the decision can be robust to the deep uncertainty present in model parameters. We further robust-optimize surveillance costs rather than minimize surveillance costs. We demonstrate that increasing the population threshold for detection increases both robustness to the errors in the model parameter estimates, and opportuneness to lower surveillance costs than the accepted maximum budget. This paper provides guidance for decision makers to balance robustness and required surveillance expenditure. IGDT offers a novel method to model and manage the uncertainty prevalent in biodiversity conservation practices and modelling. The method outlined here can be used to design robust surveillance systems for invasive species in a wider context, and to better tackle uncertainty in protection of biodiversity and native species in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Though common sense says that causes must temporally precede their effects, the hugely influential interventionist account of causation makes no reference to temporal precedence. Does common sense lead us astray? In this paper, I evaluate the power of the commonsense assumption from within the interventionist approach to causal modeling. I first argue that if causes temporally precede their effects, then one need not consider the outcomes of interventions in order to infer causal relevance, and that one can instead use temporal and probabilistic information to infer exactly when X is causally relevant to Y in each of the senses captured by Woodward's interventionist treatment. Then, I consider the upshot of these findings for causal decision theory, and argue that the commonsense assumption is especially powerful when an agent seeks to determine whether so-called "dominance reasoning" is applicable. [ABSTRACT FROM AUTHOR]

Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal? This question impacts how far RL can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we study when an RL agent has an instrumental goal to tamper with its reward process, and describe design principles that prevent instrumental goals for two different types of reward tampering (reward function tampering and RF-input tampering). Combined, the design principles can prevent reward tampering from being an instrumental goal. The analysis benefits from causal influence diagrams to provide intuitive yet precise formalizations. [ABSTRACT FROM AUTHOR]

In this paper, I present a puzzle about the connection between an agent's knowledge and her rationality and a way to solve it. The puzzle is that, intuitively, many of us want to accept both that it is rational for an agent to act on what she knows and that it is irrational for an agent to take what she knows for granting in her practical reasoning. These two claims about rationality present us with a puzzle because, holding fixed our interpretation of rationality, we cannot accept them both. According to my view, the most compelling way of solving this puzzle is to distinguish between our primary and dispositional evaluations of actions. By making this distinction, we not only gain a unique perspective on the relationship between knowledge and rationality, we also see how doing what we know is best might still manifest an undesirable habit. [ABSTRACT FROM AUTHOR]

Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as‐if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald. [ABSTRACT FROM AUTHOR]

Summary: Electrification of transportation is one of the new ways to deal with environmental issues. In order to reach sustainable development, efficient integration of the plug‐in electric vehicles (PEVs) into the distribution network (DN) plays a significant role. Establishment of PEV intelligent parking lots (IPLs) is one of the important solutions to develop charging stations. However, one of the problems with IPLs is restrictions created by municipalities and DNs while the common parking lots are not faced with these constraints. The aim of this paper is the optimal allocation of IPLs through a two‐stage mathematical model in order to minimize system costs. The system cost involves installation cost, cost of exchanging electrical power with the upstream grid, cost of the electrical power loss, network reliability cost, and emissions cost. In the first stage, the behavior of IPLs is optimized taking into account the market interactions for enhancing the IPL owner's profit. In the second stage, optimal allocation of the IPL is performed considering various network constraints. Demand response is considered as Time of Use program. Information gap decision theory is developed to hedge the DN operator and IPL owner against the risk imposed by the information gap between the forecasted and actual uncertain variables. The effectiveness and efficiency of the proposed model are evaluated on the IEEE 33‐bus test network. In the end, the presented model with simultaneously optimization can lessen the total cost by 7.74%, 15.48%, and 19.27% compared to cases, which only optimize active power loss, reliability, and emissions, respectively. [ABSTRACT FROM AUTHOR]

Interval numbers multiple attribute decision‐making (MADM) is an important branch of uncertainty decision theory, and the decision result largely depends on the selection of the aggregation operator. In this paper, we analyze the ordered weighted average (OWA) operator, which is an averaging aggregation operator. The OWA operator provides an aggregation method between the minimum and maximum operators. Moreover, we further analyze some of extensions about OWA operator, and pay special attention to the Bonferroni means and OWA (BON‐OWA) operator. Note that the BON‐OWA operator only aggregate the input arguments which are exact numbers. Under normal circumstances, decision makers is difficult to provide a clear evaluation value for attribute and most of them are described by vague information. When the decision information is an interval numbers, the BON‐OWA operator cannot describe decision result accurately. Under these environments, we proposed the interval numbers BON‐OWA (IBr,q‐OWA) operator to deal with the vague decision information in this paper. Then we consider their main properties, such as idempotence, monotonicity, and boundedness and prove them. Besides, a wide range of special aggregation operators are found in changing parameter values, such as the square mean and max operator, and so on. We also compare the ranking method of the interval numbers based on Boolean matrix. As a result, the combination of IBr,q‐OWA operator makes the decision result more scientific. Finally, a new approach for decision‐making problem is developed based on the IBr,q‐OWA operator, which shows the effectiveness in practical examples. [ABSTRACT FROM AUTHOR]

The paper presents an overview of the history and achievements of trans-border cooperation in the Lithuania-Germany-Poland triangle in planning instruments in Construction Management, decision-making theory, application of Operational Research, and Multiple Criteria Decision Making (MCDM) methods in Civil Engineering and sustainable development. The cooperation and results of the Colloquiums with 35 years of tradition, their multidimensional nature is underlined. The research instruments, methods, studied phenomena are reviewed and characteristic applications in engineering and economics are presented. The knowledge and combined efforts of three academic centers have created a synergy which set in motion many original methods and spectacular implementations. The Colloquium calendar and the evolution of organizational forms are presented along with the inclusion of the informal EURO Working Group on Operations Research in Sustainable Development and Civil Engineering. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

In China, the mature development of online retail channels provides consumers with multiple consumption choices, and the factors that affect whether consumers choose to search or purchase online are numerous and complex. In this context, this paper reports on experimental research regarding consumers' willingness to choose channels based on the two-stage decision-making theory. Using structural equation modeling, the factors influencing consumers' online search intention and purchase willingness and the relationship between them are studied. In particular, the perceived benefits, channel trust, and channel transfer costs are explored. Furthermore, a Bayesian network is used in order to analyze the degree of influence of each factor quantitatively. It is found that online trust is an important factor affecting consumers' online search intention, and the most important factor for consumers' online purchase intention is their perceived benefits of online shopping. At the same time, there is a positive relationship between online search intention and purchase intention. This study can provide management decision support for online retail enterprises and help to promote the healthy development of online shopping. [ABSTRACT FROM AUTHOR]

Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification. [ABSTRACT FROM AUTHOR]

Newcomb's problem has spawned a debate about which variant of expected utility maximisation (if any) should guide rational choice. In this paper, we provide a new argument against what is probably the most popular variant: causal decision theory (CDT). In particular, we provide two scenarios in which CDT voluntarily loses money. In the first, an agent faces a single choice and following CDT's recommendation yields a loss of money in expectation. The second scenario extends the first to a diachronic Dutch book against CDT. [ABSTRACT FROM AUTHOR]