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Bayesian decision theory is a framework for making decisions that are expected to have the best outcomes based on available data. Dynamo is a paradigm that applies Bayesian decision theory to Test & Evaluation (T&E). It provides capabilities to visualize how knowledge of a system under test is updated as test results arrive, to assess the ways in which this knowledge could be refined under various test plans, and to recommend the test decisions with the best potential for furthering stakeholder priorities. Dynamo stands for Dynamic knowledge + Moneyball. Dynamic knowledge refers to the management of the various forms of uncertainty associated with T&E. This knowledge is updated in real time and is leveraged to provide various analytic capabilities. Moneyball refers to optimization criteria that express stakeholder priorities and testing costs in a common currency to facilitate testing decisions that provide the most “bang for the buck.” This article discusses the principles behind the Dynamo paradigm and the progression of capabilities it provides for T&E as it is deployed in three phases. It ends with a simple example that illustrates how the impetus to test is generated at decision boundaries in knowledge space. [ABSTRACT FROM AUTHOR]

With this article we hope to achieve two goals. The first is to encourage consumer behavioral researchers to consider Bayesian methods for analyzing experimental and survey data. As such, we provide what we hope will be a persuasive set of arguments for trying Bayes. The second goal is to survey the different uses to which the Bayesian posterior distribution can be put. We organize this survey in terms of loss functions and propose that such loss functions can be chosen so as to simply describe a consumer behavioral phenomenon, to highlight a managerial implication, or to emphasize a theoretical contribution. [ABSTRACT FROM AUTHOR]

Abstract Background A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive. Methods We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making. Results We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that $$r^{*}=(1-p)/p$$ r ∗ = ( 1 - p ) / p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, $$r^{*}$$ r ∗ represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians. Conclusions Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify.

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]

Background: A central goal of modern evidence-based medicine is the development of simple and easy to use tools that help clinicians integrate quantitative information into medical decision-making. The Bayesian Pre-test/Post-test Probability (BPP) framework is arguably the most well known of such tools and provides a formal approach to quantify diagnostic uncertainty given the result of a medical test or the presence of a clinical sign. Yet, clinical decision-making goes beyond quantifying diagnostic uncertainty and requires that that uncertainty be balanced against the various costs and benefits associated with each possible decision. Despite increasing attention in recent years, simple and flexible approaches to quantitative clinical decision-making have remained elusive. Methods: We extend the BPP framework using concepts of Bayesian Decision Theory. By integrating cost, we can expand the BPP framework to allow for clinical decision-making. Results: We develop a simple quantitative framework for binary clinical decisions (e.g., action/inaction, treat/no-treat, test/no-test). Let p be the pre-test or post-test probability that a patient has disease. We show that r ∗ = (1 - p) / p represents a critical value called a decision boundary. In terms of the relative cost of under- to over-acting, r ∗ represents the critical value at which action and inaction are equally optimal. We demonstrate how this decision boundary can be used at the bedside through case studies and as a research tool through a reanalysis of a recent study which found widespread misestimation of pre-test and post-test probabilities among clinicians. Conclusions: Our approach is so simple that it should be thought of as a core, yet previously overlooked, part of the BPP framework. Unlike prior approaches to quantitative clinical decision-making, our approach requires little more than a hand-held calculator, is applicable in almost any setting where the BPP framework can be used, and excels in situations where the costs and benefits associated with a particular decision are patient-specific and difficult to quantify. [ABSTRACT FROM AUTHOR]

When P(E) > 0, conditional probabilities P (H | E) are given by the ratio formula. An agent engages in ratio conditionalization when she updates her credences using conditional probabilities dictated by the ratio formula. Ratio conditionalization cannot eradicate certainties, including certainties gained through prior exercises of ratio conditionalization. An agent who updates her credences only through ratio conditionalization risks permanent certainty in propositions against which she has overwhelming evidence. To avoid this undesirable consequence, I argue that we should supplement ratio conditionalization with Kolmogorov conditionalization, a strategy for updating credences based on propositions E such that P(E) = 0. Kolmogorov conditionalization can eradicate certainties, including certainties gained through prior exercises of conditionalization. Adducing general theorems and detailed examples, I show that Kolmogorov conditionalization helps us model epistemic defeat across a wide range of circumstances. [ABSTRACT FROM AUTHOR]

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

This technical note extends a recent discussion in this journal of the role of validation study data in rational decision making. One argument that has been made in this context, using elements of Bayesian decision theory, is that further aggregation of validation study data into error rates involves a loss of information that compromises rational inference and decision making and should therefore be discouraged. This technical note seeks to explain that this argument can be developed at different levels of detail, depending on the definition of the propositions of interest, the forensic findings to be evaluated (and hence the form of the likelihood ratio), and the characterization of the relative desirability of decision consequences. The analyses proposed here reveal the cascade of abstractions and assumptions into which discussions about the use of validation study results in forensic science have fallen. This reinforces the conclusion that further aggregation of validation study data into error rates is problematic. It also suggests that even if a definition of error rate(s) could be agreed upon and defensively quantified in a given application, we should rethink and possibly adjust our expectations about what exactly error rates can practically contribute to rational modes of reasoning and decision making in legal contexts.

With the continuous development of innovative education and the growth of the demand for personalized learning, this paper explores the evaluation of students’ learning ability and resource recommendation methods in Civic Education under the guidance of OBE (student-centered, outcome-oriented, continuous improvement) educational philosophy to better achieve the educational goals. By constructing a learning ability evaluation model for Civic Education, this paper establishes a learning ability evaluation and Civic Education resource recommendation model based on Bayesian decision making by using Bayesian Decision Theory’s a posteriori probability for uncertainty reasoning and combining the personalized recommendation function of collaborative filtering algorithm. By analyzing the data of a university’s ideological and political education course in the fall of 2020, this paper not only realizes the accurate evaluation of students’ learning ability, but also tests the model’s effectiveness in learning ability evaluation and resource recommendation. The findings show that students perform well in effective communication, collaboration and independent learning ability, whose evaluation mean values are 0.62, 0.58 and 0.55, respectively. In addition, some students’ learning ability significantly improves after receiving the recommended test questions, with the increase ranging from 34.36% to 36.67%. These findings demonstrate the model’s excellent learning ability evaluation and resource recommendation performance. The research in this paper provides valuable references for evaluating students’ learning ability and resource recommendation in Civics and Political Science education, which can help further improve students’ learning effectiveness and achieve educational goals.

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

Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.

Using one field and two online lab experiments, this article shows that congruency between shelf layout and a consumer's internal product categorization increases the perceived variety of the assortment and reduces the perceived complexity of the shelf layout. These assortment perceptions, in turn, heighten purchase intention and satisfaction toward the chosen item. Results are robust across internal categorization measurements (planogram design vs. sorting tasks), congruency measures (distance- vs. matching-based), and products (biscuits vs. yogurt snacks). In the field study, familiarity—operationalized as either consumption frequency or subjective product knowledge—increased the overall effect of categorization congruency and strengthened its pathway through perceived variety (vs. the one through complexity). The authors show how their research can be exploited to improve shelf layouts by optimizing the external categorization. They demonstrate the value of a unifying Bayesian framework for research on behavioral decision making that uses the same set of posterior parameter draws for parameter inference, moderated mediation analysis, and optimization under uncertainty. [ABSTRACT FROM AUTHOR]

Selective use of new information is crucial for adaptive decision-making. Combining a gamble bidding task with assessing cortical responses using functional near-infrared spectroscopy (fNIRS), we investigated potential effects of information valence on behavioral and neural processes of belief and value updating during uncertainty reduction in young adults. By modeling changes in the participants’ expressed subjective values using a Bayesian model, we dissociated processes of (i) updating beliefs about statistical properties of the gamble, (ii) updating values of a gamble based on new information about its winning probabilities, as well as (iii) expectancy violation. The results showed that participants used new information to update their beliefs and values about the gambles in a quasi-optimal manner, as reflected in the selective updating only in situations with reducible uncertainty. Furthermore, their updating was valence-dependent: information indicating an increase in winning probability was underweighted, whereas information about a decrease in winning probability was updated in good agreement with predictions of the Bayesian decision theory. Results of model-based and moderation analyses showed that this valence-dependent asymmetry was associated with a distinct contribution of expectancy violation, besides belief updating, to value updating after experiencing new positive information regarding winning probabilities. In line with the behavioral results, we replicated previous findings showing involvements of frontoparietal brain regions in the different components of updating. Furthermore, this study provided novel results suggesting a valence-dependent recruitment of brain regions. Individuals with stronger oxyhemoglobin responses during value updating was more in line with predictions of the Bayesian model while integrating new information that indicates an increase in winning probability. Taken together, this study provides first results showing expectancy violation as a contributing factor to sub-optimal valence-dependent updating during uncertainty reduction and suggests limitations of normative Bayesian decision theory.

To reconcile the standard possible worlds model of knowledge with the intuition that ordinary agents fall far short of logical omniscience, a Stalnakerian strategy appeals to two components. The first is the idea that mathematical and logical knowledge is at bottom metalinguistic knowledge. The second is the idea that non-ideal minds are often fragmented. In this paper, we investigate this Stalnakerian reconciliation strategy and argue, ultimately, that it fails. We are not the first to complain about the Stalnakerian strategy. But in contrast to existing complaints, we want to cause trouble for the strategy directly on its home turf. That is, we will advance our objection while granting both the plausibility of the fragmentation component—save for an extreme version of it—and that of the metalinguistic component. Once our central objection to the Stalnakerian strategy is in place, we will show how it negatively affects Adam Elga and Augustín Rayo's recent attempt to apply the Stalnakerian strategy in the context of Bayesian decision theory. [ABSTRACT FROM AUTHOR]

In this paper, we study an independent Bernoulli two-armed bandit with unknown parameters ρ and λ , where ρ and λ have a pair of priori distributions such that d R (ρ) = C R ρ r 0 (1 − ρ) r 0 ′ d μ (ρ) , d L (λ) = C L λ l 0 (1 − λ) l 0 ′ d μ (λ) and μ is an arbitrary positive measure on [ 0 , 1 ] . Berry proposed the conjecture that, given a pair of priori distributions (R , L) of parameters ρ and λ , the arm with R is the current optimal choice if r 0 + r 0 ′ < l 0 + l 0 ′ and the expectation of ρ is not less than that of λ. We give an easily verifiable equivalent form of Berry's conjecture and use it to prove that Berry's conjecture holds when R and L are two-point distributions as well as when R and L are beta distributions and the number of trials N ≤ r 0 r 0 ′ + 1 . [ABSTRACT FROM AUTHOR]

Machine learning has been widely used for intelligent forwarding strategy in Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious security and privacy issues. BRFD is a smart Receiver Forwarding Decision solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks (NDN-VANET). In BRFD, every vehicle that received an interest packet is required to make a forwarding decision according to the collected network status information. And then decides whether it will forward the received interest packet or not. Therefore, the privacy information of a vehicle can be revealed to other vehicles during information exchange of the network status. In this paper, a Privacy-Aware intelligent forwarding solution PABRFD is proposed by integrating Homomorphic Encryption (HE) into the improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve the security and privacy issues of information exchanged among vehicle nodes. We informally prove that this new scheme can satisfy security requirements and we implement our solution based on HE standard libraries CKKS and BFV. The experimental results show that PABRFD can satisfy our expected performance requirements. [ABSTRACT FROM AUTHOR]

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions. [ABSTRACT FROM AUTHOR]

Introduction In complex sensorimotor behavior, uncertainty arises from ambiguity in the sensory inputs of the environment (e.g. Kersten et al., 2004) as well as from noise in sensory detection and motor execution (e.g. Todorov & Jordan, 2002). Moreover, the observable sensory inputs are delayed and there are typically multiple solutions to solve a motor task (e.g. Franklin & Wolpert, 2011). In sports, where human capacity reaches its limit with these challenges, dealing with uncertainty is crucial. Thus, the question arises how humans are able to deal with uncertainty in sensorimotor control in naturalistic situations such as sports. Methods A systematic search for original articles was conducted in six scientific databases with the following terms: (uncertainty OR noise) AND (sensor* OR motor) AND control AND sport AND movement. After independently screening 4,309 articles and additional reference lists by two raters, 70 articles remained for the review. Results There is clear evidence that prior knowledge (or similar contextual information) and multiple sensory information affect perception, action and performance. Moreover, there is considerable evidence that prior knowledge and multiple sensory information get integrated according to their reliability in order to reduce uncertainty. Furthermore, it has been reported that gaze behavior is close to the optimal compared to an optimal Bayesian observer in an expected and unexpected uncertain environment. There is also considerable evidence that not all variables have to be controlled and rather uncertainty is accepted in variables that do not impair overall performance. Some findings show that inherent noise is taken into account when planning motor execution so that the potential costs and rewards of the movement outcome are optimized. Finally, some findings show that unforeseen perturbation can be absorbed by means of higher muscle stiffness resulting from muscular co-contraction. Discussion The strategies to deal with uncertainty mentioned in the results section can be summarized under the umbrella of Bayesian decision theory (Franklin & Wolpert, 2011). Only a few alternative models were referenced that, however, were conceptually quite similar to Bayesian models. Therefore, Bayesian decision theory seems to offer a promising framework for future research on uncertainty in sensorimotor control in sport science. References Franklin, D. W., & Wolpert, D. M. (2011). Computational mechanisms of sensorimotor control. Neuron, 72(3), 425-442. https://doi.org/10.1016/j.neuron.2011.10.006 Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271-304. https://doi.org/10.1146/annurev.psych.55.090902.142005 Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11), 1226-1235. https://doi.org/10.1038/nn963

Background: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model. Methods: We developed a nested Markov Chain Monte Carlo procedure and a nonparametric regression-based method. We compared the methods by computing single-model and model-averaged EVSI for collecting additional follow-up data in 2 synthetic case studies. Results: There was good agreement between the 2 methods. The regression-based method was fast and straightforward to implement, and scales easily to include any number of candidate survival models in the model uncertainty case. The nested Monte Carlo procedure, on the other hand, was extremely computationally demanding when we included model uncertainty. Conclusions: We present a straightforward regression-based method for computing the EVSI of extending an existing trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. EVSI for ongoing trials can help decision makers determine whether early patient access to a new technology can be justified on the basis of the current evidence or whether more mature evidence is needed. Highlights: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life-expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we have developed new methods for computing the EVSI of extending a trial's follow-up, both where a single known survival model is assumed and where we are uncertain about the true survival model. We extend a previously described nonparametric regression-based method for computing EVSI, which we demonstrate in synthetic case studies is fast, straightforward to implement, and scales easily to include any number of candidate survival models in the EVSI calculations. The EVSI methods that we present in this article can quantify the need for collecting additional follow-up data before making an adoption decision given any decision-making context. [ABSTRACT FROM AUTHOR]

Background: Although several statistical methods have been developed to inform decision making on reimbursement under uncertainty (e.g., expected net benefit, cost-effectiveness acceptability curves, and expected value of perfect information [EVPI]), those for value-based pricing are limited. This research develops methods for estimating the value-based price and quantifying the uncertainty around it in health technology assessment. Methods: We defined the value-based price of a medical product under assessment as the price at which the incremental cost-effectiveness ratio is just equal to a cost-effectiveness threshold. According to this definition, we derived an explicit form of the value-based price. Using this explicit form, we developed frequentist and Bayesian approaches to value-based pricing under uncertainty. Our proposed methods were illustrated via 2 hypothetical case studies. Results: The value-based price can be expressed explicitly using cost, effectiveness, and a cost-effectiveness threshold and is a linear function of a cost-effectiveness threshold. In the frequentist framework, point estimation, interval estimation, and hypothesis testing for the value-based price are available. In the Bayesian framework, the best estimate of the value-based price under uncertainty is the weighted median value-based price with the weight of the expected consumption volume of a medical product under assessment. This is based on the opportunity loss incurred by a decision error in value-based pricing. This opportunity loss also provides a basis for the calculation of EVPI associated with value-based pricing. These methods provided estimates of the value-based prices of medical products and the uncertainty around them in 2 hypothetical case studies. Conclusions: Our developed methods can improve decision making on value-based pricing in health technology assessment. [ABSTRACT FROM AUTHOR]

Spatial navigation is a complex cognitive activity that depends on perception, action, memory, reasoning, and problem-solving. Effective navigation depends on the ability to combine information from multiple spatial cues to estimate one's position and the locations of goals. Spatial cues include landmarks, and other visible features of the environment, and body-based cues generated by self-motion (vestibular, proprioceptive, and efferent information). A number of projects have investigated the extent to which visual cues and body-based cues are combined optimally according to statistical principles. Possible limitations of these investigations are that they have not accounted for navigators' prior experiences with or assumptions about the task environment and have not tested complete decision models. We examine cue combination in spatial navigation from a Bayesian perspective and present the fundamental principles of Bayesian decision theory. We show that a complete Bayesian decision model with an explicit loss function can explain a discrepancy between optimal cue weights and empirical cues weights observed by (Chen et al. Cognitive Psychology, 95, 105–144, 2017) and that the use of informative priors to represent cue bias can explain the incongruity between heading variability and heading direction observed by (Zhao and Warren 2015b, Psychological Science, 26[6], 915–924). We also discuss (Petzschner and Glasauer's , Journal of Neuroscience, 31(47), 17220–17229, 2011) use of priors to explain biases in estimates of linear displacements during visual path integration. We conclude that Bayesian decision theory offers a productive theoretical framework for investigating human spatial navigation and believe that it will lead to a deeper understanding of navigational behaviors. [ABSTRACT FROM AUTHOR]

Abstract The concept of Value of Information (VoI) has attracted significant attentions within the civil engineering community over especially the last decade. Triggered by the increasing focus on structural health monitoring, availability of data and emerging techniques of Big Data analysis and Artificial Intelligence, important insights on how to take benefit from VoI in structural integrity management have been gained. This literature review starts out with a summary of the historical developments and contains (1) a summary of two different VoI analysis origins, (2) a compilation of existing VoI analyses research and (3) current engineering interpretations and applications of VoI in the field of civil and infrastructure engineering. VoI analysis has roots in communication theory and Bayesian decision analysis in conjunction with utility theory. Starting point is thus taken in brief introduction of these theoretical foundations, followed by a discussion on the relevant modelling aspects such as information, probability and utility modelling. A detailed review of relevant existing research is presented, divided into the following main areas: computational methods, optimal sensor placement and engineering risk management. Finally, by way of conclusion and outlook, challenges and some promising directions for VoI analysis in the field of civil and infrastructure engineering are identified.

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of continuous-time stochastic systems subject to non-convex input constraints. The input is designed to minimize the probability of misdiagnosis. Since the misdiagnosis probability cannot be expressed in the closed form, its Chernoff upper bound likely tighter than its widely used Bhattacharyya upper bound is exploited as an alternative. The closed-form expression of the Chernoff distance under Gaussian distributions is correctly deduced. For calculation simplification and diagnosis performance enhancement purposes, a novel input design criterion is constructed for AFD based on the Chernoff distance. The optimization problem for AFD is formulated as a bilevel optimization, where the outer and inner layers optimize the Chernoff distance parameter and the input respectively. A convex relaxation is proposed to relax non-convex input constraints into convex ones, and the optimal solution to the relaxed inner input design problem is proven to be also optimal for the original inner problem whose objective function and input constraints are both non-convex under certain conditions, which is referred to as lossless relaxation. The relaxed inner input design problem under the constructed design criterion can be solved to global optimums, and the relaxed outer optimization is solved by a grid search method. Given the optimal input, faults are diagnosed based on the minimum misdiagnosis probability decision rule. The effectiveness of the proposed AFD approach is demonstrated by utilizing a planetary landing example. [ABSTRACT FROM AUTHOR]

Background: Uncertain results of the status assessment performed as required by the Water Framework Directive can be responsible for misclassification of a water body's status and may lead either to risk due to undertaking unnecessary remediation actions or risk of penalties for refraining from any action and not reaching environmental goals. Based on Bayesian decision theory, optimal decision tables are shown for two examples of water quality indicators, for a river catchment in central Poland. To overcome the problem of scarcity of publicly available monitoring data, the existing SWAT model for the studied catchment was used to generate nutrient concentration time series for the baseline conditions and under different scenarios. The status classes assessed based on annual mean concentrations of daily values for total phosphorus and total nitrogen were adopted as the 'true' status classes of the water bodies based on each indicator. SWAT simulation results enabled calculation of probability distributions of concentrations for the stochastic states of the water body, both for the period before and after the performance of corrective actions. Results: Bayesian decision tables consisted of alternative management decisions including modernization of the existing wastewater treatment plants in the case of phosphorous and also of fallowing agricultural areas in the case of nitrogen. An example of a penalty calculation procedure is presented in the event that the subject of the case before the EU Court of Justice would be failure to achieve the environmental objectives by all water bodies belonging to the selected catchment. Conclusions: Detailed discussion of this analysis indicates the potential benefits in terms of minimization of costs/losses that the proposed methodology may bring to the protection of surface waters. The presented method of risk analysis for making decisions on remedial actions when uncertainty exists about the water status assessment, can be considered as a prototype of a general methodology prepared for implementation in water protection. Unfortunately paying fines instead of taking remediation measures might be optimal for uncertain status of water bodies. [ABSTRACT FROM AUTHOR]

We examine the issue of macroeconomic uncertainty in the Eurozone Area using forecasts from the European Central Bank's Survey of Professional Forecasters from the inception of the Euro in 1999Q1 to 2020Q2. We provide new insights concerning the optimism or pessimism of the distribution of forecasts by examining the 25 th and 75 th quartiles of the forecast distribution for each three key macro economic variables, GDP growth, inflation and unemployment. In addition, we examine the over- or under-confidence of forecasters in the survey by deriving the term structure of ex-ante uncertainty for up to 2 years ahead and compare it to ex-post uncertainty, enabling us to make some comparisons with existing US studies. Our results suggest that GDP growth forecasts tend towards optimism, while those for inflation and unemployment tend towards pessimism. In addition, ex-ante uncertainty in forecasts for the Eurozone Area is less than ex-post uncertainty at both the short and longer-term forecasting horizons, for all three variables. This suggests a tendency towards over-confidence on the part of Eurozone forecasters. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis computerized adaptive testing (CD-CAT) aims to identify each examinee's strengths and weaknesses on latent attributes for appropriate classification into an attribute profile. As the cost of a CD-CAT misclassification differs across user needs (e.g., remedial program vs. scholarship eligibilities), item selection can incorporate such costs to improve measurement efficiency. This study proposes such a method, minimum expected risk (MER), based on Bayesian decision theory. According to simulations, using MER to identify examinees with no mastery (MER-U0) or full mastery (MER-U1) showed greater classification accuracy and efficiency than other methods for these attribute profiles, especially for shorter tests or low quality item banks. For other attribute profiles, regardless of item quality or termination criterion, MER methods, modified posterior-weighted Kullback–Leibler information (MPWKL), posterior-weighted CDM discrimination index (PWCDI), and Shannon entropy (SHE) performed similarly and outperformed posterior-weighted attribute-level CDM discrimination index (PWACDI) in classification accuracy and test efficiency, especially on short tests. MER with a zero-one loss function, MER-U0, MER-U1, and PWACDI utilized item banks more effectively than the other methods. Overall, these results show the feasibility of using MER in CD-CAT to increase the accuracy for specific attribute profiles to address different user needs. [ABSTRACT FROM AUTHOR]

As an important part of electronic warfare, radar countermeasure determines the trend of war to a large extent. Modern radar jamming technology, especially deception jamming technology, plays an increasingly important role. Therefore, how to identify radar deception jamming is very necessary. In this paper, a feature fusion algorithm based on Bayesian decision theory is used to recognize radar deception jamming signals. Firstly, the real echo signal, deception jamming signal (contains range gate pull-off jamming, velocity gate pull-off jamming and range-velocity gate pull-off jamming) and noise signal received by radar are acquired as signal sources. Then bispectrum transformation is used to extract features in several aspects. Finally, kernel density estimation is used to improve the fusion algorithm, and the feature fusion algorithm based on Bayesian decision theory is used to recognize the received signals from radar. Results of the experiment indicate that the algorithm not only can recognize the radar deception jamming, but also has high accuracy.

Bayesian clinical trials allow taking advantage of relevant external information through the elicitation of prior distributions, which influence Bayesian posterior parameter estimates and test decisions. However, incorporation of historical information can have harmful consequences on the trial's frequentist (conditional) operating characteristics in case of inconsistency between prior information and the newly collected data. A compromise between meaningful incorporation of historical information and strict control of frequentist error rates is therefore often sought. Our aim is thus to review and investigate the rationale and consequences of different approaches to relaxing strict frequentist control of error rates from a Bayesian decision-theoretic viewpoint. In particular, we define an integrated risk which incorporates losses arising from testing, estimation, and sampling. A weighted combination of the integrated risk addends arising from testing and estimation allows moving smoothly between these two targets. Furthermore, we explore different possible elicitations of the test error costs, leading to test decisions based either on posterior probabilities, or solely on Bayes factors. Sensitivity analyses are performed following the convention which makes a distinction between the prior of the data-generating process, and the analysis prior adopted to fit the data. Simulation in the case of normal and binomial outcomes and an application to a one-arm proof-of-concept trial, exemplify how such analysis can be conducted to explore sensitivity of the integrated risk, the operating characteristics, and the optimal sample size, to prior-data conflict. Robust analysis prior specifications, which gradually discount potentially conflicting prior information, are also included for comparison. Guidance with respect to cost elicitation, particularly in the context of a Phase II proof-of-concept trial, is provided. [ABSTRACT FROM AUTHOR]

In this paper, we study an independent Bernoulli two-armed bandit with unknown parameters ρ and λ, where ρ and λ have a pair of priori distributions such that dR(ρ)=CRρr0(1−ρ)r0′dμ(ρ),dL(λ)=CLλl0(1−λ)l0′dμ(λ) and μ is an arbitrary positive measure on [0,1]. Berry proposed the conjecture that, given a pair of priori distributions (R,L) of parameters ρ and λ, the arm with R is the current optimal choice if r0+r0′

This technical note extends a recent discussion in this journal of the role of validation study data in rational decision making. One argument that has been made in this context, using elements of Bayesian decision theory, is that further aggregation of validation study data into error rates involves a loss of information that compromises rational inference and decision making and should therefore be discouraged. This technical note seeks to explain that this argument can be developed at different levels of detail, depending on the definition of the propositions of interest, the forensic findings to be evaluated (and hence the form of the likelihood ratio), and the characterization of the relative desirability of decision consequences. The analyses proposed here reveal the cascade of abstractions and assumptions into which discussions about the use of validation study results in forensic science have fallen. This reinforces the conclusion that further aggregation of validation study data into error rates is problematic. It also suggests that even if a definition of error rate(s) could be agreed upon and defensively quantified in a given application, we should rethink and possibly adjust our expectations about what exactly error rates can practically contribute to rational modes of reasoning and decision making in legal contexts., Competing Interests: The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions.

Introduction: Several arguments suggest that motivated reasoning (occurring when beliefs are not solely shaped by accuracy, but also by other motives such as promoting self-esteem or self-protection) is important in delusions. However, classical theories of delusion disregard the role of motivated reasoning. Thus, this role remains poorly understood. Methods: To explore the role of motivated reasoning in delusions, here we propose a computational model of delusion based on a Bayesian decision framework. This proposes that beliefs are not only evaluated based on their accuracy (as in classical theories), but also based on the cost (in terms of reward and punishment) of rejecting them. Results: The model proposes that, when the values at stake are high (as often it is the case in the context of delusion), a belief might be endorsed because rejecting it is evaluated as too costly, even if the belief is less accurate. This process might contribute to the genesis of delusions. Conclusions: Our account offers an interpretation of how motivated reasoning might shape delusions. This can inspire research on the affective and motivational processes supporting delusions in clinical conditions such as in psychosis, neurological disorders, and delusional disorder. [ABSTRACT FROM AUTHOR]

Objective With the advancement of Internet hospitals,the “scalpers” network has replaced the traditional “scalpers” to seize high-quality medical resources and seek profiteering.To curb malicious outpatient registration behavior,scientific countermeasures were taken.Methods Based on the statistical analysis of large hospitals,and according to Bayesian decision theory,six countermeasures were designed to against the “network scalpers” in this study.Within two months,12 experts number sources were selected to carry out the test of returning registration and seeking medical treatment,involving more than 2 000 person-times.,and 1200 questionnaires were collected for medical satisfaction.The Bayesian countermeasure “scalper” decision formula is proposed and calculated.According to the satisfaction score and the countermeasures,the formula of system availability is proposed.Results Quantitative assessment of malicious users in the appointment registration system of a large tertiary hospital,and provides some reference for outpatient management of malicious registration behavior of “scalper”.Conclusion The Bayesian theory-based hospital appointment registration system will help hospital managers to understand the causes of scalpers' countermeasures,reduce scalpers' ticket-grabbing behavior to a certain extent,and maintain normal employment order. [ABSTRACT FROM AUTHOR]

Does natural selection favor veridical percepts—those that accurately (if not exhaustively) depict objective reality? Perceptual and cognitive scientists standardly claim that it does. Here we formalize this claim using the tools of evolutionary game theory and Bayesian decision theory. We state and prove the "Fitness-Beats-Truth (FBT) Theorem" which shows that the claim is false: If one starts with the assumption that perception involves inference to states of the objective world, then the FBT Theorem shows that a strategy that simply seeks to maximize expected-fitness payoff, with no attempt to estimate the "true" world state, does consistently better. More precisely, the FBT Theorem provides a quantitative measure of the extent to which the fitness-only strategy dominates the truth strategy, and of how this dominance increases with the size of the perceptual space. The FBT Theorem supports the InterfaceTheoryofPerception (e.g. Hoffman et al. in Psychon Bull Rev https://doi.org/10.3758/s13423-015-0890-8, 2015), which proposes that our perceptual systems have evolved to provide a species-specific interface to guide adaptive behavior, and not to provide a veridical representation of objective reality. [ABSTRACT FROM AUTHOR]

Abstract.This work considers Bayesian reliability acceptance sampling plan (RASP) under interval censoring. It is considered that the consumer and manufacturer agree on a common lifetime distribution of the product. However, they differ in their assessment of the prior distributions and utility functions because of the adversarial nature of the consumer and manufacturer, but the manufacturer knows the consumer’s utility and prior. The consumer accepts or rejects a lot of products based on his/her own priors about the quality of the product and a utility function. If the consumer rejects the lot, the manufacturer gives additional information on lifetime based on a life test to update the consumer’s belief. The consumer decides on accepting or rejecting the lot based on updated information. The life test is conducted under an interval censoring scheme, where the decision on acceptance or rejection of the lot can be taken sequentially at pre-specified inspection times. The manufacturer’s task is to determine the optimal interval-censoring life testing plan which maximizes his/her utility. Last, we discuss a methodology where the manufacturer does not require knowledge of the consumer’s utility function and prior distribution for planning the life test. [ABSTRACT FROM AUTHOR]

Partial data is a common challenge in many applied problems, where we can measure only the part of the solution. This paper considers the poroelasticity problem in fractured media, where only partial measurements of fracture pressures are available. The poroelasticity models have applications in many engineering and scientific fields, such as geomechanics, hydrology, and reservoir simulation. This paper aims to develop a Bayesian decision making approach using partial data and consider its application to the poroelasticity problem in fractured media. The methodology of the proposed approach combines Proper Orthogonal Decomposition, Discrete Empirical Interpolation Method, Bayesian Inversion Approach, and Bayesian Decision Theory. It allows us to compute posterior probabilities and decide according to the optimal decision rule based on the partial data. Moreover, we apply partially explicit temporal discretization to solve forward problems efficiently. For testing the proposed approach, we consider a two-dimensional model problem. We simulate the cases when we need to make a single decision about the reservoir or decisions about the elements of the set of reservoirs. The results show that the proposed approach can efficiently compute the posterior probabilities and make a decision in the presence of only partial data for both cases. [ABSTRACT FROM AUTHOR]

Fault diagnosis(FD) is vital for monitoring of industrial processes. Actually, both the multiple fault variables and minor faults are inclined to bring wrong diagnosis results. In this paper, a fault variable identification method is proposed for multivariate/minor fault diagnosis. The deviation factor is adopted as the characteristic of the sample, and Bayesian decision theory is adopted to calculate the possibility of the variable being faulty, then multi-dimensional reconstruction-based contribution (MRBC) is used to determine the fault source variables. This method not only improves the diagnosis rate for multiple/minor faults with less computation time, but also can indicate severity level of the different fault variables according to the fault occurrence probability. A Numerical example and Tennessee Eastman process are given to show the efficiency of the proposed method. • A fault variable identification method is proposed for multivariate/minor fault. • The deviation factor, the Bayesian decision theory and MRBC is used for FD. • The proposed method can indicate severity level of the different fault variables. • The proposed method will improve the diagnosis rate with less computation time. [ABSTRACT FROM AUTHOR]

Consider an optimal procurement strategy for fresh produce, which is a type of perishable product. Due to the different quality provided by each manufacturer, the fresh produce qualification rates are dissimilar. Simultaneously, consumers demand is influenced by the freshness and price of products, as a result, the demand in the market is not fixed. In this scenario, how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management. In order to address the above questions, the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand. Finally, simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]