Decision-theoretic Approach Research Articles

Surgical Management of Secondary Complex Microsurgical Reconstructions after Amputation and Severe Trauma Injuries: A Case Series.

Secondary complex microsurgical reconstructions after amputation and severe trauma injuries are often necessary to optimize functional outcomes. We reviewed eight patients who underwent extensive reconstruction after severe trauma. The details of secondary procedures are further described in the article. A literature search was performed using the National Center for Biotechnology Information (NCBI) database for studies evaluating secondary procedures after complex reconstructions. To date, the order and the need for performing secondary procedures have yet to be fully defined. The tissues encountered include skin, soft tissue, bone, nerve, joint, and tendon. We described the use of a decision-theoretic approach to the secondary reconstruction. Treatment of a complex trauma should be measured by functional outcome.

Bounded Rational Decision Networks with Belief Propagation.

Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit's information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.

On global robustness of an adversarial risk analysis solution

AbstractAdversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the nonstrategic play and level‐ thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research.

The Concept of Statistical Evidence, Historical Roots and Current Developments

One can argue that one of the main roles of the subject of statistics is to characterize what the evidence in the collected data says about questions of scientific interest. There are two broad questions that we will refer to as the estimation question and the hypothesis assessment question. For estimation, the evidence in the data should determine a particular value of an object of interest together with a measure of the accuracy of the estimate, while for the hypothesis assessment, the evidence in the data should provide evidence in favor of or against some hypothesized value of the object of interest together with a measure of the strength of the evidence. This will be referred to as the evidential approach to statistical reasoning, which can be contrasted with the behavioristic or decision-theoretic approach where the notion of loss is introduced, and the goal is to minimize expected losses. While the two approaches often lead to similar outcomes, this is not always the case, and it is commonly argued that the evidential approach is more suited to scientific applications. This paper traces the history of the evidential approach and summarizes current developments.

Potential outcomes and decision-theoretic foundations for statistical causality: Response to Richardson and Robins

Abstract I thank Thomas Richardson and James Robins for their discussion of my article, and discuss the similarities and differences between their approach to causal modelling, based on single world intervention graphs, and my own decision-theoretic approach.

Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

Factor analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relationship between model fit, factor dimension, and sparse loadings. This relation is done through a summary of the information contained in the multivariate posterior. A two-step strategy is used in our method. First, given the posterior samples from the Bayesian factor analysis model, a series of point estimates with a decreasing number of factors and different levels of sparsity are recovered by minimizing an expected penalized loss function. Second, the degradation in model fit between the posterior of the full model and the recovered estimates is displayed in a summary. In this step, a criterion is proposed for selecting the factor model with the best trade-off between fit, sparseness, and factor dimension. The findings are illustrated through a simulation study and an application to personality data. We used different prior choices to show the flexibility of the proposed method.

Mortality analysed by propensity score matching: an application to national neonatal audit

Mortality, a key outcome variable in many population studies and studies of healthcare and its interventions, is commonly analysed by regression of the survival status on a set of relevant background variables. We describe an alternative based on the potential outcomes framework, in which we ask how a particular group of subjects, or a population, whose outcomes were realised in one condition, would have fared had they been treated or cared for in different circumstances. The method is applied to neonatal mortality in the operational delivery networks in England and Wales. The performance of a network is assessed by the difference of the mortality rates of the network and of a matched set of babies drawn from the entire domain of the study. The outlier status of a network is established by a decision-theoretical approach.

Optimal nonparametric range-based volatility estimation

We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time-interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency gains compared to existing commonly used range-based procedures.

Joint species distribution modeling with competition for space

AbstractJoint species distribution models (JSDM) are among the most important statistical tools in community ecology. However, existing JSDMs cannot model mutual exclusion between species. We tackle this deficiency in the context of modeling plant percentage cover data, where mutual exclusion arises from limited growing space and competition for light. We propose a hierarchical JSDM where latent Gaussian variable models describe species' niche preferences and Dirichlet‐Multinomial distribution models the observation process and competition between species. We also propose a decision theoretic model comparison and validation approach to assess the goodness of JSDMs in four different types of predictive tasks. We apply our models and methods to a case study on modeling vegetation cover in a boreal peatland. Our results show that ignoring the interspecific interactions and competition reduces models' predictive performance and leads to biased estimates for total percentage cover. Models' relative predictive performance also depends on the predictive task highlighting that model comparison and assessment should resemble the true predictive task. Our results also demonstrate that the proposed JSDM can be used to simultaneously infer interspecific correlations in niche preference as well as mutual competition for space and through that provide novel insight into ecological research.

Improving Knowledge Learning Through Modelling Students’ Practice-Based Cognitive Processes

Practice is an essential means by which humans and animals engage in cognitive activities. Intelligent tutoring systems, with a crucial component of modelling learners’ cognitive processes during learning and optimizing their learning strategies, offer an excellent platform to investigate students’ practice-based cognitive processes. In related studies, modelling methods for cognitive processes have demonstrated commendable performance. Furthermore, researchers have extended their investigations using decision-theoretic approaches, such as a partially observable Markov decision process (POMDP), to induce learning strategies by modelling the students’ cognitive processes. However, the existing research has primarily centered around the modelling of macro-level instructional behaviors rather than the specific practice selection made by the students within the intricate realms of cognitive domains. In this paper, we adapt the POMDP model to represent relations between the student’s performance on cognitive tasks and his/her cognitive states. By doing so, we can predict his/her performance while inducing learning strategies. More specifically, we focus on question selection during the student’s real-time learning activities in an intelligent tutoring system. To address the challenges on modelling complex cognitive domains, we exploit the question types to automate parameter learning and subsequently employ information entropy techniques to refine learning strategies in the POMDP. We conduct the experiments in two real-world knowledge concept learning domains. The experimental results show that the performance of the learning strategies induced by our new model is superior to that of other learning strategies. Moreover, the new model has good reliability in predicting the student’s performance. Utilizing an intelligent tutoring system as the research platform, this article addresses the modelling and strategy induction challenges of practice-based cognitive processes with intricate structures, aiming to tutor students effectively. Our work provides a new approach of predicting the students’ performance as well as personalizing their learning strategies.

Application Of A Bayesian Adaptive Decision-theoretic Approach To A Multi-arm Exercise Oncology Trial

Application Of A Bayesian Adaptive Decision-theoretic Approach To A Multi-arm Exercise Oncology Trial

Optimal test procedures for multiple hypotheses controlling the familywise expected loss.

We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real-life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. Using a decision-theoretic approach, we define suitable loss functions for a given decision rule, where incorrect decisions can be treated unequally by assigning different loss values. Taking expectation with respect to the sampling distribution of the data allows us to control the familywise expected loss instead of the conventional familywise error rate. Different loss functions can be adopted, and we search for decision rules that satisfy certain optimality criteria within a broad class of decision rules for which the expected loss is bounded by a fixed threshold under any parameter configuration. We illustrate the methods with the problem of establishing efficacy of a new medicinal treatment in non-overlapping subgroups ofpatients.

A Decision-Theoretic Approach to Assisted Medical Decision-Making

A Decision-Theoretic Approach to Assisted Medical Decision-Making

Feature Attention as a Control Mechanism for the Balance of Speed and Accuracy in Visual Search

Finding an object amongst a cluttered visual scene is an everyday task for humans but presents a fundamental challenge to computational models performing this feat. Previous attempts to model efficient visual search have focused on locating targets as swiftly as possible, but so far have not considered balancing the costs of lengthy searches against the costs of making errors. Here, we propose a neuro-inspired model of visual search that offers an attention-based control mechanism for this speed-accuracy trade-off. The model combines a goal-based fixation policy, which captures human-like behaviour on a simple visual search task, with a deep neural network that carries out the target detection step. The neural network is patched with a target-based feature attention model previously applied to standalone classification tasks. In contrast to image classification, visual search introduces a time component, which places an additional demand on the model to minimise the time cost of the search whilst also maintaining acceptable accuracy. The proposed model balances these two costs by modulating the attentional strength given to characteristic features of the target class, thereby minimising an associated cost function. The model offers a method for optimising the costs of visual search and demonstrates the value of a decision theoretic approach to modelling more complex visual tasks involving attention.

A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials

ObjectivesAdaptive designs may reduce trial sample sizes and costs. This study illustrates a Bayesian-adaptive decision-theoretic design applied to a multiarm exercise oncology trial. Study Design and SettingIn the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial, 230 breast cancer patients receiving chemotherapy were randomized to supervised resistance and aerobic exercise (OnTrack), home-based physical activity (OncoMove) or usual care (UC). Data were reanalyzed as an adaptive trial using both Bayesian decision-theoretic and a frequentist group-sequential approach incorporating interim analyses after every 36 patients. Endpoint was chemotherapy treatment modifications (any vs. none). Bayesian analyses were performed for different continuation thresholds and settings with and without arm dropping and both in a ‘pick-the-winner’ and a ‘pick-all-treatments-superior-to-control’ setting. ResultsTreatment modifications occurred in 34% of patients in UC and OncoMove vs. 12% in OnTrack (P = 0.002). Using a Bayesian-adaptive decision-theoretic design, OnTrack was identified as most effective after 72 patients in the ‘pick-the-winner’ setting and after 72–180 patients in the ‘pick-all-treatments-superior-to-control’ setting. In a frequentist setting, the trial would have been stopped after 180 patients, and the proportion of patients with treatment modifications was significantly lower for OnTrack than UC. ConclusionA Bayesian-adaptive decision-theoretic approach substantially reduced the sample size required for this three-arm exercise trial, especially in the ‘pick-the-winner’ setting.

Choice structures in games

Following the decision-theoretic approach to game theory, we extend the analysis of Epstein and Wang (1996) and Di Tillio (2008) from hierarchies of preference relations to hierarchies of choice functions. We then construct the universal choice structure containing all these choice hierarchies, and show how the universal preference structure from Di Tillio (2008) is embedded in it.

Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM

Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM

Soft ordered double quantitative approximations based three-way decisions and their applications

The classical theory of rough set was established by Pawlak, which mainly focusses on the approximation of sets characterized by a single equivalence relation over the universe. However, most of the current single granulation structure models cannot meet the user demand or the target of solving problems. Multigranulation rough sets approach can better deal with the problems, where data might be spread over various locations. In this article, we present the idea of soft preference and soft dominance relation for the development of soft dominance rough set in an incomplete information system. Subsequently, several important structural properties and results of the proposed model are carefully analyzed. After employing soft dominance based rough set approach to it for any times, we can only get six different sets at most in an incomplete information system. That is to say, every rough set in a universe can be approximated by only six sets, where the lower and upper approximations of each set in the six sets are still lying among these six sets. The relationships among these six sets are established. Based on soft dominance relation, we introduce logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximations in an incomplete information. Meanwhile, to measure the uncertainty of soft dominance optimistic/pessimistic multigranulation decision theoretic rough approximation and some of their interesting properties are examined. Thereafter, a novel multi attribute with multi decision making problem approach based on logical disjunction/conjunction soft dominance optimistic/pessimistic multigranulation decision theoretic rough sets approach are developed to solve the selection of medicine to treat the coronavirus disease (COVID-19). The basic principle and the detailed steps of the decision making model (algorithms) are presented in detail. To demonstrate the applicability and potentiality of the proposed model, we present a practical example of a medical diagnosis is given to validate the practicality of the technique.

Targeted Double Scoring of Performance Tasks Using a Decision-Theoretic Approach.

Targeted double scoring, or, double scoring of only some (but not all) responses, is used to reduce the burden of scoring performance tasks for several mastery tests (Finkelman, Darby, & Nering, 2008). An approach based on statistical decision theory (e.g., Berger, 1989; Ferguson, 1967; Rudner, 2009) is suggested to evaluate and potentially improve upon the existing strategies in targeted double scoring for mastery tests. An application of the approach to data from an operational mastery test shows that a refinement of the currently used strategy would lead to substantial cost savings.

MEASURING PERFORMANCE FOR END-OF-LIFE CARE.

Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital's seemingly good performance for readmission may be an artifact of it having poor performance for mortality. in this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. in some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N = 17,685 patients diagnosed with pancreatic cancer between 2000-2012 at one of J = 264 hospitals in California.