Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample.

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.

A nature-positive future with biological invasions: theory, decision support and research needs.

In 2050, most areas of biodiversity significance will be heavily influenced by multiple drivers of environmental change. This includes overlap with the introduced ranges of many alien species that negatively impact biodiversity. With the decline in biodiversity and increase in all forms of global change, the need to envision the desired qualities of natural systems in the Anthropocene is growing, as is the need to actively maintain their natural values. Here, we draw on community ecology and invasion biology to (i) better understand trajectories of change in communities with a mix of native and alien populations, and (ii) to frame approaches to the stewardship of these mixed-species communities. We provide a set of premises and actions upon which a nature-positive future with biological invasions (NPF-BI) could be based, and a decision framework for dealing with uncertain species movements under climate change. A series of alternative management approaches become apparent when framed by scale-sensitive, spatially explicit, context relevant and risk-consequence considerations. Evidence of the properties of mixed-species communities together with predictive frameworks for the relative importance of the ecological processes at play provide actionable pathways to a NPF in which the reality of mixed-species communities are accommodated and managed. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Psychological frameworks augment even classical decision theories.

Johnson, Bilovich, and Tuckett set out a helpful framework for thinking about how humans make decisions under radical uncertainty and contrast this with classical decision theory. We show that classical theories assume so little about psychology that they are not necessarily in conflict with this approach, broadening its appeal.

The small world's problem is everyone's problem, not a reason to favor CNT over probabilistic decision theory.

The case for the superiority of Conviction Narrative Theory (CNT) over probabilistic approaches rests on selective employment of a double standard. The authors judge probabilistic approaches inadequate for failing to apply to "grand-world" decision problems, while they praise CNT for its treatment of "small-world" decision problems. When both approaches are held to the same standard, the comparative question is murkier.

Dynamic prospect theory: Two core decision theories coexist in the gambling behavior of monkeys and humans.

Research in the multidisciplinary field of neuroeconomics has mainly been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our model and revealed a systematic violation of prospect theory's assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between these species were uncovered by various econometric analyses of our dynamic prospect theory model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our model provides a unified theoretical framework for exploring a neurobiological model of economic choice in human and nonhuman primates.

Participação plurissocial para aumentar a inclusão e a assertividade nas tomadas de decisão em saúde relacionadas a tratamentos da osteoporose / Pluri-social participation to increase inclusion and assertiveness in health decision-making related to osteoporosis treatments

Embora as fraturas por fragilidade sejam importantes detratoras de qualidade de vida relacionada à saúde, aumentando a morbimortalidade e acarretando alto impacto clínico, psicossocial e econômico, elas são pouco valorizadas e negligenciadas por médicos e até mesmo por pacientes. Além disso, os critérios de priorização para avaliação de novas tecnologias, em geral, não consideram critérios além dos financeiros para uma tomada de decisão mais inclusiva e assertiva para o tratamento da população de mais alto risco de fratura. Assim, este artigo visa revisitar alguns diferentes pontos de vista e trazer uma reflexão sobre critérios e prioridades na osteoporose. Para isso, foi considerada a perspectiva de múltiplos atores no processo de tomada de decisão em saúde, bem como analisadas as falhas na atenção a uma doença de alta prevalência e que, além do grande impacto econômico gerado para a sociedade, causa repercussões emocionais, incapacidade gerada por fraturas e medo de novas quedas ou pequenos traumas.

Although fragility fractures are important detractors of health-related quality of life, increasing morbidity and mortality and causing a high clinical, psychosocial, and economic impact, they are undervalued and neglected by physicians and even patients. In addition, prioritization criteria for evaluating new technologies, in general, do not consider criteria other than financial ones for a more inclusive and assertive decision-making for the treatment of the population at higher risk of fracture. Thus, this article aims to revisit some different points of view and bring a reflection on criteria and priorities in osteoporosis. For this, the perspective of multiple stakeholders in the health decision-making process was considered, as well as the failures in the care of this highly prevalent disease that, in addition to the great economic impact generated for society, causes emotional repercussions, disability generated by fractures and fear of further falls or minor trauma.

Hospital profiling using Bayesian decision theory.

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.

Uma abordagem de Análise de Decisão Multicritérios (MCDA) para identificar e priorizar critérios de tomada de decisão para tratamentos da osteoporose pós-menopausa no Sistema Único de Saúde do Brasil / A Multicriteria Decision Analysis (MCDA) framework to identify and prioritize criteria for guiding decision making for postmenopausal osteoporosis treatment in the Brazilian Public Healthcare System

Objetivo: Identificar os principais critérios e preferências na tomada de decisão em saúde para osteoporose pós-menopausa, por três grupos de stakeholders (n=3, cada): médicos; representantes de pacientes; gestores de saúde. Métodos: Uma estrutura de Análise de Decisão Multicritério (MCDA) foi realizada para gerar priorização entre tecnologias: uma revisão da literatura formou conjuntos de critérios; um painel online validou os critérios selecionados; o método AHP (Analytic Hierarchy Process) atribuiu pesos de importância para cada critério, por consenso. Resultados: Os critérios avaliados foram: eficácia (fraturas clínicas, vertebrais, não vertebrais e de quadril, densidade mineral óssea), segurança (eventos adversos e tolerabilidade), conveniência (adesão e comodidade posológica) e economia (razão de custo-efetividade incremental ­ RCEI, custo por respondedor, impacto orçamentário e custos indiretos). Fraturas clínicas e de quadril apareceram nas primeiras posições para todos os grupos. Para os médicos, fratura de quadril (26,11%) e eventos adversos (14,64%) foram os principais critérios de priorização; para os representantes dos pacientes, fratura clínica (25,09%) e de quadril (22,84%), enquanto critérios econômicos receberam os menores pesos (1,2% a 0,98%), abaixo da comodidade posológica, por exemplo (4%). Gestores públicos priorizaram RCEI (19,44%) e fratura de quadril (16,21%). Conclusões: Os resultados apresentados têm potencial para auxiliar na tomada de decisão e priorização de tratamentos para osteoporose e estão em linha ao observado em estudos de preferência nesta área terapêutica. Embora os pesos finais tenham variado entre os grupos, os desfechos de eficácia que envolvem fraturas foram os critérios priorizados.

Objective: To identify the main criteria and preferences in healthcare decision-making for postmenopausal osteoporosis according to three stakeholder groups (n=3, each): physicians, patient representatives, and public healthcare managers. Methods: A multi-Criteria Decision Analysis framework was performed to generate prioritization rankings between technologies: a literature review formed sets of criteria; an online panel validated the pre-selected criteria; the Analytic Hierarchy Process (AHP) method assigned importance weights to each criterion by consensus. Results: The final weighted average included: efficacy (clinical fractures, new vertebral, non-vertebral, hip fractures, and bone mineral density), safety (clinically significant adverse events and tolerability), convenience (adherence and dosing convenience), and economics (incremental cost-effectiveness ratio ­ ICER, cost per responder, budget impact and indirect costs). New hip and clinical fractures appeared in the top-five positions for all stakeholder groups. For physicians the main criteria were new hip fracture (26.11%) and adverse events (14.64%); similarly, for patient representatives, clinical fracture (25.09%) and new hip fracture (22.84%) were the most important ones, while economic criteria received the lowest weights (1,2% to 0,98%), below dosing convenience, for example (4%). Public healthcare managers prioritized ICER (19.44%) and new hip fractures (16.21%). Conclusions: The presented results have the potential to assist decision-making and treatment prioritization in postmenopausal osteoporosis. Although final weightings varied among stakeholders, efficacy outcomes involving fractures were the priority criteria for all of them. It is possible to observe similar results in previously published studies of preferences in osteoporosis.

Evaluation of digital economy development level based on multi-attribute decision theory.

The maturity and commercialization of emerging digital technologies represented by artificial intelligence, cloud computing, block chain and virtual reality are giving birth to a new and higher economic form, that is, digital economy. Digital economy is different from the traditional industrial economy. It is clean, efficient, green and recyclable. It represents and promotes the future direction of global economic development, especially in the context of the sudden COVID-19 pandemic as a continuing disaster. Therefore, it is essential to establish the comprehensive evaluation model of digital economy development scientifically and reasonably. In this paper, first on the basis of literature analysis, the relevant indicators of digital economy development are collected manually and then screened by the grey dynamic clustering and rough set reduction theory. The evaluation index system of digital economy development is constructed from four dimensions: digital innovation impetus support, digital infrastructure construction support, national economic environment and digital policy guarantee, digital integration and application. Next the subjective weight and objective weight are calculated by the group FAHP method, entropy method and improved CRITIC method, and the combined weight is integrated with the thought of maximum variance. The grey correlation analysis and improved VIKOR model are combined to systematically evaluate the digital economy development level of 31 provinces and cities in China from 2013 to 2019. The results of empirical analysis show that the overall development of China's digital economy shows a trend of superposition and rise, and the development of digital economy in the four major economic zones is unbalanced. Finally, we put forward targeted opinions on the construction of China's provincial digital economy.

On the use of discrete-time quantum walks in decision theory.

We present a short review of discrete-time quantum walks (DTQW) as a potentially useful and rich formalism to model human decision-making. We present a pedagogical introduction of the underlying formalism and main structural properties. We suggest that DTQW are particularly suitable for combining the two strands of literature on evidence accumulator models and on the quantum formalism of cognition. Due to the additional spin degree of freedom, models based on DTQW allow for a natural modeling of model choice and confidence rating in separate bases. Levels of introspection and self-assessment during choice deliberations can be modeled by the introduction of a probability for measurement of either position and/or spin of the DTQW, where each measurement act leads to a partial decoherence (corresponding to a step towards rationalization) of the deliberation process. We show how quantum walks predict observed probabilistic misperception like S-shaped subjective probability and conjunction fallacy. Our framework emphasizes the close relationship between response times and type of preferences and of responses. In particular, decision theories based on DTQW do not need to invoke two systems ("fast" and "slow") as in dual process theories. Within our DTQW framework, the two fast and slow systems are replaced by a single system, but with two types of self-assessment or introspection. The "thinking fast" regime is obtained with no or little self-assessment, while the "thinking slow" regime corresponds to a strong rate of self-assessment. We predict a trade-off between speed and accuracy, as empirically reported.

Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays.

In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub-type principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.

Hierarchical multi-label classification based on LSTM network and Bayesian decision theory for LncRNA function prediction.

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

Bayesian decision theory and navigation.

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.

Better baboon break-ups: collective decision theory of complex social network fissions.

Many social groups are made up of complex social networks in which each individual associates with a distinct subset of its groupmates. If social groups become larger over time, competition often leads to a permanent group fission. During such fissions, complex social networks present a collective decision problem and a multidimensional optimization problem: it is advantageous for each individual to remain with their closest allies after a fission, but impossible for every individual to do so. Here, we develop computational algorithms designed to simulate group fissions in a network-theoretic framework. We focus on three fission algorithms (democracy, community and despotism) that fall on a spectrum from a democratic to a dictatorial collective decision. We parameterize our social networks with data from wild baboons (Papio cynocephalus) and compare our simulated fissions with actual baboon fission events. We find that the democracy and community algorithms (egalitarian decisions where each individual influences the outcome) better maintain social networks during simulated fissions than despotic decisions (driven primarily by a single individual). We also find that egalitarian decisions are better at predicting the observed individual-level outcomes of observed fissions, although the observed fissions often disturbed their social networks more than the simulated egalitarian fissions.

Razonamiento clínico en medicina II: hacia una definición integradora / Clinical reasoning in medicine II: Towards an integrating definition

RESUMEN El entendimiento del razonamiento clínico es una necesidad para la investigación, la docencia y la práctica clínica. Los modelos teóricos subyacentes podrían agruparse en tres grandes ejes no excluyentes. El primero es denominado bayesiano informal según su estructura semejante al análisis de probabilidades condicionales. El segundo propone (desde las ciencias cognitivas) un razonamiento dual que es la suma de dos tipos de pensamientos: el tipo 1, rápido e intuitivo y, el tipo 2, hipotético-deductivo. El tercero, el conocimiento intersubjetivo que involucra la interacción del saber del paciente sobre su condición con el del médico, además, de hacer explícito el papel de la emoción. En esta segunda entrega se presenta una revisión narrativa de estas teorías para poder proponer una definición integradora, en la que se presenta al razonamiento clínico como un constructo complejo, iterativo y adaptativo.

SUMMARY Understanding clinical reasoning is a crucial for research, teaching, and daily clinical practice. Theoretical models could be grouped into three main non-exclusive axes. The first describes probability-based thinking, called informal Bayesian, because of its similarity to the conditional probability analysis structure. The second, from the cognitive sciences, describes reasoning as the sum of two types of thinking: type 1 (fast and intuitive) and type 2 (hypothetical-deductive). Finally, the third, intersubjective knowledge, which involves the interaction of the patient's knowledge about his condition with the doctor's knowledge and also makes explicit the role of emotion. In this second part, a narrative review of current theories is presented in order to propose an integrative definition, in which clinical reasoning is presented as a complex, iterative and adaptive construct.

I want to believe: delusion, motivated reasoning, and Bayesian decision theory.

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.

Classification under uncertainty: data analysis for diagnostic antibody testing.

Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization.

Making policy decisions under plural uncertainty: responding to the COVID-19 pandemic.

In this paper, I contend that the uncertainty faced by policy-makers in the COVID-19 pandemic goes beyond the one modelled in standard decision theory. A philosophical analysis of the nature of this uncertainty could suggest some principles to guide policy-making.

Certainty-Based Marking on Multiple-Choice Items: Psychometrics Meets Decision Theory.

When a response to a multiple-choice item consists of selecting a single-best answer, it is not possible for examiners to differentiate between a response that is a product of knowledge and one that is largely a product of uncertainty. Certainty-based marking (CBM) is one testing format that requires examinees to express their degree of certainty on the response option they have selected, leading to an item score that depends both on the correctness of an answer and the certainty expressed. The expected score is maximized if examinees truthfully report their level of certainty. However, prospect theory states that people do not always make rational choices of the optimal outcome due to varying risk attitudes. By integrating a psychometric model and a decision-making perspective, the present study looks into the response behaviors of 334 first-year students of physiotherapy on six multiple-choice examinations with CBM in a case study. We used item response theory to model the objective probability of students giving a correct response to an item, and cumulative prospect theory to estimate their risk attitudes when students choose to report their certainty. The results showed that with the given CBM scoring matrix, students' choices of a certainty level were affected by their risk attitudes. Students were generally risk averse and loss averse when they had a high success probability on an item, leading to an under-reporting of their certainty. Meanwhile, they were risk seeking in case of small success probabilities on the items, resulting in the over-reporting of certainty.

Application of multi-trait Bayesian decision theory for parental genomic selection.

In all breeding programs, the decision about which individuals to select and intermate to form the next selection cycle is crucial. The improvement of genetic stocks requires considering multiple traits simultaneously, given that economic value and net genetic merits depend on many traits; therefore, with the advance of computational and statistical tools and genomic selection (GS), researchers are focusing on multi-trait selection. Selection of the best individuals is difficult, especially in traits that are antagonistically correlated, where improvement in one trait might imply a reduction in other(s). There are approaches that facilitate multi-trait selection, and recently a Bayesian decision theory (BDT) has been proposed. Parental selection using BDT has the potential to be effective in multi-trait selection given that it summarizes all relevant quantitative genetic concepts such as heritability, response to selection and the structure of dependence between traits (correlation). In this study, we applied BDT to provide a treatment for the complexity of multi-trait parental selection using three multivariate loss functions (LF), Kullback-Leibler (KL), Energy Score, and Multivariate Asymmetric Loss (MALF), to select the best-performing parents for the next breeding cycle in two extensive real wheat data sets. Results show that the high ranking lines in genomic estimated breeding value (GEBV) for certain traits did not always have low values for the posterior expected loss (PEL). For both data sets, the KL LF gave similar importance to all traits including grain yield. In contrast, the Energy Score and MALF gave a better performance in three of four traits that were different than grain yield. The BDT approach should help breeders to decide based not only on the GEBV per se of the parent to be selected, but also on the level of uncertainty according to the Bayesian paradigm.