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

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24 h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters. [ABSTRACT FROM AUTHOR]

The index gas analysis is an important method of predicting coal spontaneous combustion (CSC). The temperature programmed experiment was used to simulate the CSC process in goaf, and the characteristics of each index gas and coal temperature change were investigated. The corresponding relationship between the target center distance and the coal temperature was determined using the multi-index weighted gray target decision theory. The findings indicate that the selected gases (CO, C2H4, C2H6) and gas combination (format fire coefficient (R), φ(CO)/φ(CO2), φ(C2H4)/φ(CH4), φ(C2H4)/φ(C2H6)) can be used as index gases for CSC prediction. The critical temperature was 70°C and the dry cracking temperature was 110°C. The combined action of coal and oxygen is divided into three stages: slow oxidation (30–70°C), accelerated oxidation (70–110°C), and strong oxidation (after 110°C). The combination of the programmed temperature experiment and the weighted gray target decision theory model determines the relationship between the off-target distance and the coal temperature. The lower the degree of CSC, the better the decision scheme, and the smaller the off-target distance. The results of Xiaobaodang's experimental analyses are applied to the field, and the actual coal temperature in the goaf is predicted to be between 37–62°C. The risk level of CSC in the goaf is determined more intuitively and precisely, providing a theoretical foundation for improving the mining area's fire prevention and extinguishing scheme. [ABSTRACT FROM AUTHOR]

Lacking predictability of enforcement hinders the deterrent function of competition law. This article shows that academic analyses of optimal competition rules do not always treat this factor adequately, paying instead excessive attention to the problem of error. Sometimes, predictability is completely ignored as a relevant factor. At other times, it is taken into account but its effects are framed in a way that undermines their significance. This article further discusses three possible reasons why a part of competition law and economics scholarship engages in such mistreatment of predictability. First, it may be a result of writing convenience. Secondly, the role of predictability in selecting the optimal competition rule may simply be misunderstood. Thirdly, the role of predictability may be belittled intentionally in order to advocate rules benefiting the interests of competition practitioners and/or defendants. This article also briefly explores how problematic each reason is and what solutions might be available. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive model for cyber security risk assessment using the PIPRECIA-S method within decision theory, which enables organizations to systematically identify, assess and prioritize key cyber threats. The study focuses on the evaluation of malware, ransomware, phishing and DDoS attacks, using criteria such as severity of impact, financial losses, ease of detection and prevention, impact on reputation and system recovery. This approach facilitates decision making, as it enables the flexible adaptation of the risk assessment to the specific needs of an organization. The PIPRECIA-S model has proven to be useful for identifying the most critical threats, with a special emphasis on ransomware and DDoS attacks, which represent the most significant risks to businesses. This model provides a framework for making informed and strategic decisions to reduce risk and strengthen cyber security, which are critical in a digital environment where threats become more and more sophisticated. [ABSTRACT FROM AUTHOR]

This article develops a decision model which enables service firms to optimize their productivity. Companies must efficiently determine the necessary resource input to increase service productivity to meet customer demand. In so doing, managers face service-specific challenges: They must select the appropriate type and quantity of limited resources to deliver services efficiently, consider the volatility of demand to provide services effectively, and integrate the interaction effects of resources in terms of substitution to utilize constraint resources optimally. In addressing these challenges, we develop an interdisciplinary approach by combining insights from service research and operations research to create a decision model that helps managers select the optimal type and quantity of resources available to overcome the abovementioned challenges. We validate our model in several case studies and further generalize our findings by applying it to different data settings. Ultimately, we prove that productivity can be increased significantly if firms optimize resource selection by considering stochastic demand, the effects of substitution among resources, and resource constraints. [ABSTRACT FROM AUTHOR]

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms. [ABSTRACT FROM AUTHOR]

Measuring and analyzing both nonlinear-distortion and linear-reflection otoacoustic emissions (OAEs) combined creates what we have termed a "joint-OAE profile." Here, we test whether these two classes of emissions have different sensitivities to hearing loss and whether our joint-OAE profile can detect mild-moderate hearing loss better than conventional OAE protocols have. 2f1-f2 distortion-product OAEs and stimulus-frequency OAEs were evoked with rapidly sweeping tones in 300 normal and impaired ears. Metrics included OAE amplitude for fixed-level stimuli as well as slope and compression features derived from OAE input/output functions. Results show that mild-moderate hearing loss impacts distortion and reflection emissions differently. Clinical decision theory was applied using OAE metrics to classify all ears as either normal-hearing or hearing-impaired. Our best OAE classifiers achieved 90% or better hit rates (with false positive rates of 5%–10%) for mild hearing loss, across a nearly five-octave range. In summary, results suggest that distortion and reflection emissions have distinct sensitivities to hearing loss, which supports the use of a joint-OAE approach for diagnosis. Results also indicate that analyzing both reflection and distortion OAEs together to detect mild hearing loss produces outstanding accuracy across the frequency range, exceeding that achieved by conventional OAE protocols. [ABSTRACT FROM AUTHOR]

An action is agentially perfect if and only if, if a person tries to perform it, they succeed, and, if a person performs it, they try to. We argue that trying itself is agentially perfect: if a person tries to try to do something, they try to do it; and, if a person tries to do something, they try to try to do it. We show how this claim sheds new light on questions about basic action, the logical structure of intentional action, and the notion of "options" in decision theory. On the way to these central ideas, we argue that a person can try to do something even if they believe it is impossible that they will succeed, that a person can try to do something even if they do not want to succeed, and that a person can try to do something even if they do not intend to succeed. [ABSTRACT FROM AUTHOR]

Biosecurity activities primarily include pre‐border and border quarantine, post‐border surveillance and post‐border eradication. Budget allocated to quarantine and surveillance activities ultimately influence the expenditure and success rate of eradication campaigns. Optimal portfolio allocation examined in previous research is susceptible to potential severe uncertainties existing in ecology and in the behaviour of invasive species itself. These uncertainties, together with a limited budget, make it difficult for decision makers to allocate the total management budget to each biosecurity activity in a robust manner.Info‐gap decision theory is applied to model the severe uncertainty in invasive species management, and robust optimize the total management cost.This research shows that using a combination of pre‐border and border quarantine (to reduce the incursion probability) and post‐border surveillance (to enable early detection and rapid response), enables decision makers to be more robust to potential uncertainty. Further, it is reported that investment in quarantine that is more cost‐effective should outweigh that in surveillance, in line with precautionary principle.Increasing the estimated population threshold for surveillance detection also gains more robustness.Synthesis and applications: Portfolio allocation options developed in this research provide decision makers with a way to manage the invasive species spatially, cost‐effectively and confidently by allocating the total management budget in a robust manner. The methods outlined in this research can not only be applied to invasive species, but also the conservation of endangered species that are constrained by severe uncertainty in ecological modelling and limited resources. [ABSTRACT FROM AUTHOR]

Longtermists have recently argued that it is overwhelmingly important to do what we can to mitigate existential risks to humanity. I consider three mistakes that are often made in calculating the value of existential risk mitigation. I show how correcting these mistakes pushes the value of existential risk mitigation substantially below leading estimates, potentially low enough to threaten the normative case for existential risk mitigation. I use this discussion to draw four positive lessons for the study of existential risk. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of risk on time allocation decisions between occupations by modeling a hybrid entrepreneur who must decide how to allocate time between paid employment (labor) and working on a venture (entrepreneurship). We argue that hybrid entrepreneurs self-insure in response to income risk by managing the time they allocate between the two occupations. We provide the conditions under which an uninsurable risk (in paid employment or the entrepreneurial sector) has an unambiguous precautionary effect on the optimal time allocated to each occupation, and these conditions are based on the strengths of risk aversion and downside risk aversion. We focus on three cases: when risk affects only the entrepreneurial sector, which is the classical case studied in the occupational choice literature; when risk affects only the paid employment sector; and finally, when risk affects both sectors, as we experienced during the recent pandemic. Plain English Summary: Hybrid entrepreneurs allocate their time between paid work (labor) and working on a venture. Traditionally, it is thought that the paid job sector is stable over time, and so all risks are concentrated in the entrepreneurial sector. However, the disruption of COVID-19 affected a wide variety of productive activity, and the labor market in particular became extremely turbulent for many people. As a result, it is vital to understand how risk influences how hybrid entrepreneurs allocate their time between different occupations using an optimization technique that considers the risk connected with each activity as a way to understand precautionary behavior. The method utilized in this study has the primary advantage of permitting the investigation of simultaneous risks, which can be tested in the future through experimentation. This link brings up a plethora of possibilities for exploration in the study of entrepreneurship activities utilizing modern decision theory methods. [ABSTRACT FROM AUTHOR]

A standard practice in statistical hypothesis testing is to mention the P-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With P-values, it is not clear how to use an extreme observation (e.g. P << α) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post hoc, after observation of the data--thereby providing a handle on "roving α's." When Type-II risks are taken into consideration, the only admissible decision rules in the post hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty confidence interval is not fixed in advance, standard confidence intervals and distributions may fail, whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment. [ABSTRACT FROM AUTHOR]

As one of the most promising large-scale de-fossilization technologies currently available, Power-to-Gas (P2G) is expected to promote utilization of renewable sources and mitigate the effects of greenhouse gas. Through the coupling of P2G, Combined Heat and Power (CHP) as well as Carbon Capture and Storage (CCS), the P2G–CCS–CHP microgrid energy system could achieve hierarchical energy transfer and green-powered interconversion of gas, electricity and heat flows. However, the conversion of multiple energy shapes between different facilities is deeply intertwined with renewable energy sources, greatly increasing risk of unbalance operation and impacting the overall robustness and economic viability of system. To address these challenges, this study proposes a decision-making framework based on Information Gap Decision Theory (IGDT) method for a P2G–CCS–CHP system. The real-time balancing of heat & power loads and supply, the mechanism of gas-electricity-heat interaction, as well as recycling pattern of synthetic fuel within the system are carefully addressed to achieve optimal operation. Different scheduling schemes are obtained by setting up two separating decision principles, namely risk-seeking (RS) and risk-avoiding (RA), to solve these uncertainties and ensure robust decision making. Meanwhile, adaptability of decision-making methodology is analyzed based on several criteria including share of recycled green-powered fuel, ecological friendliness, carbon trading benefit, etc. Results show that adding of P2G module enables a 12.6% reduction in carbon emissions and generates 1252.2 kg of green powered gas, highlighting the eco-friendly nature of the multi energy system (MES). About 21.5% of the renewable energy flows participate the interconversion of electric-heat-gas and about 17.7% of total carbon flow is circulated as synthetic fuel within the system. In addition, conservative tendency is witnessed in the case of risk-seeking decision mode, leading to an approximate 13.22% increase in the operation costs, compared with risk-avoiding mode. The proposed IGDT decision strategy has shown significant compatibility in dealing with the impact of multiple uncertainties. • Employing IGDT strategy for decision-making considering energy uncertainty. • Optimization of power and heat balancing for reliable and low-carbon operation. • Recirculation of green-powered fuel within system is enabled via power-to-gas. • Interaction of electricity-heat-gas flows is analyzed under two risk principles. [ABSTRACT FROM AUTHOR]

Information about species' locations can influence what happens to them—from supporting habitat protection to exposing poaching targets. Debate about releasing locations when new species are found highlights the trade‐off between the risk of loss and the benefits of funding and public support. No research so far has collected data on how such decisions are made, and no decision tools easily compare a range of decision‐making scenarios. Here, we present a method to compare the costs and benefits of decisions about the disclosure of information about newly discovered species and populations. We implement our method for seven species where information is completely or partially secret. We ask decision‐makers to estimate the costs and benefits associated with these case studies and apply our method. Results show a range of implications from choices that are always better, to others that depend on risk attitude, and demonstrate that the process of decision‐making can be transparent and easily communicated. [ABSTRACT FROM AUTHOR]

The authors present the results of the research combining the methods of approximation theory and optimal decision theory. Namely, a solution to the optimization problem for the biharmonic Poisson integral in the upper half-plane is considered one of the most optimal solutions to the biharmonic equation in Cartesian coordinates. The approximate properties of the biharmonic Poisson operator in the upper half-plane on the classes of quasi-smooth functions are obtained in the form of an exact equality for the deviation of quasi-smooth functions from the positive operator under consideration. [ABSTRACT FROM AUTHOR]

Why do we remember the past, and plan the future? We introduce a toy model in which to investigate emergent time asymmetries: the causal multibaker maps. These are reversible discrete-time dynamical systems with configurable causal interactions. Imposing a suitable initial condition or "Past Hypothesis", and then coarse-graining, yields a Pearlean locally causal structure. While it is more common to speculate that the other arrows of time arise from the thermodynamic arrow, our model instead takes the causal arrow as fundamental. From it, we obtain the thermodynamic and epistemic arrows of time. The epistemic arrow concerns records, which we define to be systems that encode the state of another system at another time, regardless of the latter system's dynamics. Such records exist of the past, but not of the future. We close with informal discussions of the evolutionary and agential arrows of time, and their relevance to decision theory. [ABSTRACT FROM AUTHOR]

Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation. [ABSTRACT FROM AUTHOR]

Countenancing unbounded utility in ethics gives rise to deep puzzles in formal decision theory. Here, these puzzles are taken as an invitation to assess the most fundamental principles relating probability and value, with the aim of demonstrating that unbounded utility in ethics is compatible with a desirable decision theory. The resulting theory frames further discussion of Expected Utility Theory and of principles concerning symmetries of utility. [ABSTRACT FROM AUTHOR]

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

Consumer choice models used in optimal product design typically ignore potential context effects by assuming the utility of each product is independent of the attributes of other products in the choice set. We characterize implications of context effects for profit-maximizing designs by deriving the first-order conditions of the design problem under alternative utility formulations, and we propose a utility function that incorporates context effects and has well-defined optimal design solutions for all products in the choice set. We then conduct a discrete choice survey experiment of automobile options and find statistically significant context-effect parameters and superior out-of-sample prediction when context-effect parameters are used in both logit and mixed logit models. These results suggest that context effects can be important in engineering design contexts and have the potential to affect optimal design differentiation. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

Population game theory is a new direction of game theory, developed in recent thirty years, which originated from "Mass-Action" interpretation on mixed strategies and equilibria in 1950 by J. Nash in his PhD dissertation. It established rational decision making theory for individuals in population and society consisting of large number of individuals, and has been applied extensively and intensively in sociology, biology, economics, management science and information science, etc. In this paper, we give a review on recent advances of population game theory and investigate new developing directions. [ABSTRACT FROM AUTHOR]

In the high-proportion hydropower market, the fairness of the execution of traded electricity and clean energy consumption are two issues that need to be considered in medium- and long-term dispatching. Aiming at the fairness of medium- and long-term optimal dispatching of hydropower-dominated grids and the problem of water abandonment in the power market environment, this paper proposes a medium- and long-term optimal dispatching method for hydropower-dominated grids based on the information gap decision-making theory (IGDT). Firstly, IGDT is used to establish a two-layer model of medium- and long-term optimal dispatching that considers runoff uncertainty, in which the lower layer solves the maximum value of the maximum difference in the contract power completion rate of the power stations, and the upper layer solves the maximum fluctuation range of the interval inflow. Then, a mixed-integer linear programming (MILP)-based single-layer optimization model is obtained through a variety of linearization techniques, and the model is solved via the CPLEX solver (version 12.10.0). The medium- and long-term optimal dispatching of 10 thermal power stations and 22 hydropower stations in Yunnan Power Grid, China, is taken as an example to verify the proposed model. The results show that the maximum difference in the contracted electricity completion rate of each power station is 0.412, and the amount of abandoned hydropower is reduced by 81.33% compared to when the abandoned water penalty function is not considered. It is proved that the proposed model can effectively alleviate the problems of excessive power generation, insufficient power generation and large-scale hydropower abandonment, which are of great significance for realizing the fair dispatching of hydropower-dominated power grids and promoting clean energy consumption in the market environment. [ABSTRACT FROM AUTHOR]

The paper presents research results combining the methods of approximation theory and optimal decision theory. Namely, the optimization problem for the biharmonic Poisson integral in the upper half-plane is considered as one of the most optimal solutions to the biharmonic equation in Cartesian coordinates. The approximate properties of the biharmonic Poisson operator in the upper half-plane on the classes of quasi-smooth functions are obtained in the form of an exact equality for the deviation of quasi-smooth functions from the positive operator under consideration. [ABSTRACT FROM AUTHOR]

Summary: What are "deep uncertainties" and how should their presence influence prudent decisions? To address these questions, we bring ideas from robust control theory into statistical decision theory. Decision theory has its origins in axiomatic formulations by von Neumann and Morgenstern, Wald, and Savage. After Savage, decision theorists constructed axioms that formalize a notion of ambiguity aversion. Meanwhile, control theorists constructed decision rules that are robust to some model misspecifications. We reinterpret axiomatic foundations of decision theories to express ambiguity about a prior over a family of models along with concerns about misspecifications of the corresponding likelihood functions. [ABSTRACT FROM AUTHOR]

In this article, we survey the applications of Three-way decision theory (TWD) in machine learning (ML), focusing in particular on four tasks: weakly supervised learning and multi-source data management, missing data management, uncertainty quantification in classification, and uncertainty quantification in clustering. For each of these four tasks we present the results of a systematic review of the literature, by which we report on the main characteristics of the current state of the art, as well as on the quality of reporting and reproducibility level of the works found in the literature. To this aim, we discuss the main benefits, limitations and issues found in the reviewed articles, and we give clear indications and directions for quality improvement that are informed by validation, reporting, and reproducibility standards, guidelines and best practice that have recently emerged in the ML field. Finally, we discuss about the more promising and relevant directions for future research in regard to TWD. [ABSTRACT FROM AUTHOR]

Suppose that, for reasons of animal welfare, it would be better if everyone stopped eating chicken. Does it follow that you should stop eating chicken? Proponents of the 'inefficacy objection' argue that, due to the scale and complexity of markets, the expected effects of your chicken purchases are negligible. So the expected effects of eating chicken do not make it wrong. We argue that this objection does not succeed, in two steps. First, empirical data about chicken production tells us that the expected effects of consuming many chickens are not negligible. Second, this implies that the expected effect of consuming one chicken is ordinarily not negligible. Parity between your purchase and other counterfactual purchases, and uncertainty about others' consumption behaviour, each tend to pull the expected effect of a single purchase toward the average large scale effect. While some purchases do have negligible expected effects, many do not. [ABSTRACT FROM AUTHOR]

The Apaṇṇaka Sutta, one of the early recorded teachings of the Buddha, contains an argument for accepting the doctrines of karma and rebirth that Buddhist scholars claim anticipates Pascal's wager. I call this argument the Buddha's wager. Does it anticipate Pascal's wager and is it a good bet? Contemporary scholars identify at least four versions of Pascal's wager in his Pensées. This article demonstrates that the Buddha's wager anticipates two versions of Pascal's wager, but not its canonical form. Like Pascal's wager, the Buddha's wager presents a decision problem between two opposing theses in an epistemic context that lacks evidence of their truth or falsity. Like Pascal, the Buddha also tries to solve this problem using dominance, superdominance or 'superduperdominance' reasoning. The Apaṇṇaka Sutta likely provides the earliest textual example of such reasoning. While the Buddha's wager does not exhibit the expected utility reasoning of the best-known form of Pascal's wager, the article suggests a reformulation that parallels Alan Hájek's (2018) vector-value reformulation. Is it a good bet? This article argues that it is not if this means we are rationally required to accept its recommendation. This is because, while it avoids two of the major objections levelled against Pascal's wager, it succumbs to one and has at least two problems of its own. [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.

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

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

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

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

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

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

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

Private and public megaprojects—whether new plant facilities, IT systems, railways, or the Olympics—frequently entail dramatic cost and schedule overruns. Root causes are behavioral biases, such as optimism and deliberate deception, accompanied by principal–agent issues and a lack of project-related skills. Through a three-stage process—forecasting, organizing, and executing ("FOX")—we organize and offer solutions to mitigate the cognitive biases and agency issues planners and policy-makers face in large projects. Following the FOX process and building on behavioral decision theory, we review evidence for the accuracy of "reference-class forecasting," which considers comparable past projects to forecast a current, planned project. We provide evidence for reference-class forecasting performance and recent methodological extensions, such as similarity-based forecasting. Then, considering the relevant literature, we offer organizational solutions to reduce unfounded optimism and deception, including debiasing techniques and specific measures to curb principal–agent issues. Finally, we suggest combining a project modular design with speedy implementation for faster, better, cheaper, and lower-risk execution. Overall, we offer an original, holistic theoretical view that addresses both behavioral and strategic elements of how to debias large projects, along with direct practical implications and advice for those who manage megaprojects with increasingly high stakes and risks. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic generated uncertainty among consumers, a slowdown in consumption and an increase of the added saving at world level, the microeconomic evidence showed a tendency towards dissaving and growing consumption. These variations activate questioning about the consequences of confinement in intertemporal consumption, at the same time they allow to provide new empirical evidence about life-cycle models in their standard or neoclassical and behavioral versions. The purpose of this work was to experimentally evaluate the intertemporal consumption patterns from the postulates of both life cycle models. To this end, an experimental simulation exercise of online purchases of commodities was carried out with the participation of 210 consumers who were subjected to treatments that included a baseline, income increase scenarios and no-income withdrawal scenarios. The results verified the existence of consistent responses with the behavioral model in 85% of the cases and with the standard model for the remaining 15%; that is, the tendencies to smooth consumption and increase savings were in the minority in the group evaluated and the confinement context did not translate into more self-controlled intertemporal consumption behaviors.

Abstract In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work.

Nikk Effingham and Huw Price argue that in certain cases of Newcomb problems involving time travel and foreknowledge, being given information about the future makes it rational to choose as an evidential decision theorist would choose. Although the cases they consider have some intuitive pull, and so appear to aid in answering the question of what it is rational to do, we argue that their respective positions are not compelling. Newcomb problems are structured such that whichever way one chooses, one might be led by one's preferred decision theory to miss out on some riches (riches which others obtain whilst employing their preferred decision theory). According to the novel aesthetic diagnosis we shall offer of the Newcomb dialectic, missing out in this way does not render one irrational but, rather, subject to being seen as absurd. This is a different kind of cost but not one that undermines one's rationality. [ABSTRACT FROM AUTHOR]

Moral options are permissions to do less than best, impartially speaking. In this paper, we investigate the challenge of reconciling moral options with the ideal of justifiability to each individual. We examine ex-post and ex-ante views of moral options and show how they might conflict with this ideal in single-choice and sequential-choice cases, respectively. We consider some ways of avoiding this conflict in sequential-choice cases, showing that they face significant problems. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous–categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks. [ABSTRACT FROM AUTHOR]

How do ordinary people choose survival strategies during intense, surprising political violence? Why do some flee violence, while others fight back, adapt, or hide? Individual decision-making during violence has vast political consequences, but remains poorly understood. I develop a decision-making theory focused on individual appraisals of how controllable and predictable violent environments are. I apply my theory, situational appraisal theory, to explain the choices of Indian Sikhs during the 1980s–1990s Punjab crisis and 1984 anti-Sikh pogroms. In original interviews plus qualitative and machine learning analysis of 509 oral histories, I show that control and predictability appraisals influence strategy selection. People who perceive "low" control over threats often avoid threats rather than approach them. People who perceive "low" predictability in threat evolution prefer more-disruptive strategies over moderate, risk-monitoring options. Appraisals explain behavior variation even after accounting for individual demographics and conflict characteristics, and also account for survival strategy changes over time. [ABSTRACT FROM AUTHOR]

This article introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize–winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real-world data. The method is available as an open source Python package named geoshapley. [ABSTRACT FROM AUTHOR]

As agile processes are increasingly adopted for product design, and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this article are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data. [ABSTRACT FROM AUTHOR]

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Project development in river basins is beset with lots of challenges occasioned by human activities which spark off climate variability that hinder their effectiveness to planning and management with other extraneous factors in the area. The study aimed at the use of Game theory model to optimize the net benefits on multi-purpose projects development in Anambra-Imo River basin. The objective was to use the iterative algorithm of game decision theory model to optimize the multi-purpose/multi-objective projects development at the river basin. The methodology involves the use of Game decision theory based on data generated from Bill of Engineering Measurement and Evaluation (BEME), descriptive, experimental model size and simulation modeling solution techniques, correlation and regression analysis. The result shows that the optimal strategies for the game theory was N69.02billion. The value of the name was 5.52 calculated from the Simplex Method Linear Programming Techniques of Game theory which falls between the maximin of 5.77 and minimax of 3.36. The correlation between the probabilities for player A and player B shows a strongly positive correlation 0.9612 which is consistent with that calculated from the graphical activities of r = 0.9635. The work recommends that the implementation of the optimal strategies of Game theory will assist in mitigating the effect of climate variability for improved integrated planning and management of the river basin. The adaptation to climate change condition will deliver benefits in order to achieve global potential contribution to multiple sustainable challenges in the river basin. [ABSTRACT FROM AUTHOR]