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Background: To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods: We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results: Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions: The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size. [ABSTRACT FROM AUTHOR]

A fuzzy set extension known as the hesitant fuzzy set (HFS) has increased in popularity for decision making in recent years, especially when experts have had trouble evaluating several alternatives by employing a single value for assessment when working in a fuzzy environment. However, it has a significant problem in its uses, i.e., considerable data loss. The probabilistic hesitant fuzzy set (PHFS) has been proposed to improve the HFS. It provides probability values to the HFS and has the ability to retain more information than the HFS. Previously, fuzzy regression models such as the fuzzy linear regression model (FLRM) and hesitant fuzzy linear regression model were used for decision making; however, these models do not provide information about the distribution. To address this issue, we proposed a probabilistic hesitant fuzzy linear regression model (PHFLRM) that incorporates distribution information to account for multi-criteria decision-making (MCDM) problems. The PHFLRM observes the input–output (IPOP) variables as probabilistic hesitant fuzzy elements (PHFEs) and uses a linear programming model (LPM) to estimate the parameters. A case study is used to illustrate the proposed methodology. Additionally, an MCDM technique called the technique for order preference by similarity to ideal solution (TOPSIS) is employed to compare the PHFLRM findings with those obtained using TOPSIS. Lastly, Spearman's rank correlation test assesses the statistical significance of two rankings sets. [ABSTRACT FROM AUTHOR]

Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this article, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomized, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under consideration. We propose using distributional discrepancy to characterize the commensurability between subtrials for appropriate borrowing of information through a spike-and-slab prior, which is placed on the prior precision factor. When the basket trial has at least three subtrials, commensurate priors for point-to-point borrowing are combined into a marginal predictive prior, according to the weights transformed from the pairwise discrepancy measures. In this way, only information from subtrial(s) with the most commensurate treatment effect is leveraged. The marginal predictive prior is updated to a robust posterior by the contemporary subtrial data to inform decision making. Operating characteristics of the proposed methodology are evaluated through simulations motivated by a real basket trial in chronic diseases. The proposed methodology has advantages compared to other selected Bayesian analysis models, for (i) identifying the most commensurate source of information and (ii) gauging the degree of borrowing from specific subtrials. Numerical results also suggest that our methodology can improve the precision of estimates and, potentially, the statistical power for hypothesis testing. [ABSTRACT FROM AUTHOR]

Biomedical research has come to rely on p-values as a deterministic measure for data-driven decision-making. In the largely extended null hypothesis significance testing for identifying statistically significant differences among groups of observations, a single p-value is computed from sample data. Then, it is routinely compared with a threshold, commonly set to 0.05, to assess the evidence against the hypothesis of having non-significant differences among groups, or the null hypothesis. Because the estimated p-value tends to decrease when the sample size is increased, applying this methodology to datasets with large sample sizes results in the rejection of the null hypothesis, making it not meaningful in this specific situation. We propose a new approach to detect differences based on the dependence of the p-value on the sample size. We introduce new descriptive parameters that overcome the effect of the size in the p-value interpretation in the framework of datasets with large sample sizes, reducing the uncertainty in the decision about the existence of biological differences between the compared experiments. The methodology enables the graphical and quantitative characterization of the differences between the compared experiments guiding the researchers in the decision process. An in-depth study of the methodology is carried out on simulated and experimental data. Code availability at https://github.com/BIIG-UC3M/pMoSS. [ABSTRACT FROM AUTHOR]

If artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal 'Goldilocks' zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI's influence, raising important implications and new directions for research on human–AI interaction. [ABSTRACT FROM AUTHOR]

The discussion on the use and misuse of p-values in 2016 by the American Statistician Association was a timely assertion that statistical concept should be properly used in science. Some researchers, especially the economists, who adopt significance testing and p-values to report their results, may felt confused by the statement, leading to misinterpretations of the statement. In this study, we aim to re-examine the accuracy of the p-value and introduce an alternative way for testing the hypothesis. We conduct a simulation study to investigate the reliability of the p-value. Apart from investigating the performance of p-value, we also introduce some existing approaches, Minimum Bayes Factors and Belief functions, for replacing p-value. Results from the simulation study confirm unreliable p-value in some cases and that our proposed approaches seem to be useful as the substituted tool in the statistical inference. Moreover, our results show that the plausibility approach is more accurate for making decisions about the null hypothesis than the traditionally used p-values when the null hypothesis is true. However, the MBFs of Edwards et al. [Bayesian statistical inference for psychological research. Psychol. Rev. 70(3) (1963), pp. 193–242]; Vovk [A logic of probability, with application to the foundations of statistics. J. Royal Statistical Soc. Series B (Methodological) 55 (1993), pp. 317–351] and Sellke et al. [Calibration of p values for testing precise null hypotheses. Am. Stat. 55(1) (2001), pp. 62–71] provide more reliable results compared to all other methods when the null hypothesis is false. [ABSTRACT FROM AUTHOR]

Although choosing between several alternatives is the basic and most common process of decision-making, it has been well established that when having to decide, people are prone to various biases and seldom elect an alternative rationally. One of the most common and well-researched biases is the 'choice-bias' and many researches demonstrated its effect on decision-makers (both high-level and daily decisions) of the previous generations. This research examined the hypothesis that since the psychological development of 'generation Y' differs significantly from the researched generations, youth today are more resistant to this specific bias. A lifetime of constant exposure to the overabundance of suggestions and opportunities may have influenced decision-making capabilities. A study conducted on two separate groups of students (256 participants) revealed that no choice bias was observed, unlike similar studies conducted with previous generation populations. [ABSTRACT FROM AUTHOR]

Statistical methods are indispensable to the practice of science. But statistical hypothesis testing can seem daunting, with P-values, null hypotheses, and the concept of statistical significance. This article explains the concepts associated with statistical hypothesis testing using the story of "the lady tasting tea," then walks the reader through an application of the independent-samples t-test using data from Peter and Rosemary Grant's investigations of Darwin's finches. Understanding how scientists use statistics is an important component of scientific literacy, and students should have opportunities to use statistical methods like this in their science classes. [ABSTRACT FROM AUTHOR]

This study compares husbands' and wives' views on the person in a couple who should be responsible for preparing for hurricane hazards; it also examines whether the varying levels of agreement reached by husbands and wives regarding this responsibility are associated with actual preparedness behaviors. An online survey targeting married, heterosexual couples living in Sarasota County, Florida, USA was sent out between March and May, 2015. Both the husbands and the wives were asked to fill out the survey. A total of 170 surveys were used for analysis. Results suggested that husbands and wives felt that they had shared responsibility for most of the 19 preparedness behaviors considered. However, a few stereotypically masculine preparedness behaviors were found to typically fall to husbands. Husbands' and wives' views of perceived responsibility were not statistically different, but husbands tended to favor individual responsibility, while wives tended to favor joint responsibility. Higher levels of agreement were significantly associated with greater engagement in planning-related preparedness behaviors. Policy implications are discussed. [ABSTRACT FROM AUTHOR]

Predicting and analyzing behaviors of investors is of great value to financial institutions. This paper uses survey data from about 9,000 individual investors across China to explore the predictability of decision behaviors by studying demographic characteristics that are relatively easy to obtain. After applying Pearson’s chi-squared test, Spearman rank correlation test, and several data mining methods, we verified that demographic characteristics are closely linked to decision behaviors, and it would be an economical and feasible solution for financial organizations to build initial behavioral prediction models especially when investors’ behavioral data are insufficient. [ABSTRACT FROM AUTHOR]

An important theoretical question in decision making concerns the nature of cue-generation: What mechanism drives the generation of cues used to make inferences? Most models of decision making assume that the properties of cues, often cue validity, initiate a set of dynamic pre-decision processes. In two studies, we test how memory accessibility affects cue use by manipulating both ecological cue validity and cue accessibility in a stock-forecasting task. Cue accessibility was manipulated by the pattern of accurate cue discriminations within experiment blocks of the learning phase of the experiments. Specifically, we manipulated the serial positions in which the cues accurately discriminated while holding overall cue validity constant. At test, participants preferred cues that discriminated early in the learning phase—a kind of primacy effect. The findings suggest that cue use is influenced by memory retrieval mechanisms and that cue use is not solely determined by cue validity. The results have implications for the development of computational models of heuristic decision-making. [ABSTRACT FROM AUTHOR]

Aims: To clarify the hypothesis tests associated with the full covariate modelling (FCM) approach in population pharmacokinetic analysis, investigate the potential impact of multiplicity in population pharmacokinetic analysis, and evaluate simultaneous confidence intervals (SCI) as an approach to control multiplicity. Methods: Clinical trial simulations were performed using a simple one‐compartment pharmacokinetic model. Different numbers of covariates, sample sizes, effect sizes of covariates, and correlations among covariates were explored. The false positive rate (FPR) and power were evaluated. Results: The FPR for the FCM approach dramatically increases with number of covariates. The chance of incorrectly selecting ≥1 seemingly clinically relevant covariates can be increased from 5% to a 40–70% range for 10–20 covariates. The SCI approach may provide appropriate control of the family‐wise FPR, allowing more appropriate decision making. As a result, the power detecting real effects without incorrectly identifying non‐existing effects can be greatly improved by the SCI approach compared to the approach in current practice. The performance of the SCI approach is driven by the ratio of sample size to number of covariates. The FPR can be controlled at 5% and 10% using the SCI approach when the ratio was ≥20 and 10, respectively. Conclusion: The FCM approach still lies within the framework of statistical testing, and therefore multiplicity is an issue for this approach. It is imperative to consider multiplicity reporting and adjustments in FCM modelling practice to ensure more appropriate decision making. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainty in hydrological forecasting is valuable for water resources management and decision-making processes. The hydrological uncertainty processor (HUP) can quantify hydrological uncertainty and produce probabilistic forecasts under the hypothesis that there is no input uncertainty. This study proposes a HUP based on a copula function, in which the prior density and likelihood function are explicitly expressed, and the posterior density and distribution obtained using Monte Carlo sampling. The copula-based HUP was applied to the Three Gorges Reservoir, and compared with the meta-Gaussian HUP. The Nash-Sutcliffe efficiency and relative error were used as evaluation criteria for deterministic forecasts, while predictive QQ plot, reliability, resolution and continuous rank probability score (CRPS) were used for probabilistic forecasts. The results show that the proposed copula-based HUP is comparable to the meta-Gaussian HUP in terms of the posterior median forecasts, and that its probabilistic forecasts have slightly higher reliability and lower resolution compared to the meta-Gaussian HUP. Based on the CRPS, both HUPs were found superior to deterministic forecasts, highlighting the effectiveness of probabilistic forecasts, with the copula-based HUP marginally better than the meta-Gaussian HUP. [ABSTRACT FROM PUBLISHER]

Maternal capabilities-qualities of mothers that enable them to leverage skills and resources into child health-hold potential influence over mother's adoption of child caring practices, including infant and young child feeding. We developed a survey ( n = 195) that assessed the associations of 4 dimensions of maternal capabilities (social support, psychological health, decision making, and empowerment) with mothers' infant and young child feeding practices and children's nutritional status in Uganda. Maternal responses were converted to categorical subscales and an overall index. Scale reliability coefficients were moderate to strong (α range = 0.49 to 0.80). Mothers with higher social support scores were more likely to feed children according to the minimum meal frequency (odds ratio [OR] [95% confidence interval (CI)] = 1.38 [1.10, 1.73]), dietary diversity (OR [95% CI] = 1.56 [1.15, 2.11]), iron rich foods, (OR [95% CI] = 1.47 [1.14, 1.89]), and minimally acceptable diet (OR [95% CI] = 1.55 [1.10, 2.21]) indicators. Empowerment was associated with a greater likelihood of feeding a minimally diverse and acceptable diet. The maternal capabilities index was significantly associated with feeding the minimum number of times per day (OR [95% CI] = 1.29 [1.03, 1.63]), dietary diversity (OR [95% CI] = 1.44 [1.06, 1.94]), and minimally acceptable diet (OR [95% CI] = 1.43 [1.01, 2.01]). Mothers with higher psychological satisfaction were more likely to have a stunted child (OR [95% CI] = 1.31 [1.06, 1.63]). No other associations between the capabilities scales and child growth were significant. Strengthening social support for mothers and expanding overall maternal capabilities hold potential for addressing important underlying determinants of child feeding in the Ugandan context. [ABSTRACT FROM AUTHOR]

A typical rule that has been used for the endorsement of new medications by the Food and Drug Administration is to have two trials, each convincing on its own, demonstrating effectiveness. “Convincing” may be subjectively interpreted, but the use of p-values and the focus on statistical significance (in particular with p < .05 being coined significant) is pervasive in clinical research. Therefore, in this paper, we calculate with simulations what it means to have exactly two trials, each with p < .05, in terms of the actual strength of evidence quantified by Bayes factors. Our results show that different cases where two trials have a p-value below .05 have wildly differing Bayes factors. Bayes factors of at least 20 in favor of the alternative hypothesis are not necessarily achieved and they fail to be reached in a large proportion of cases, in particular when the true effect size is small (0.2 standard deviations) or zero. In a non-trivial number of cases, evidence actually points to the null hypothesis, in particular when the true effect size is zero, when the number of trials is large, and when the number of participants in both groups is low. We recommend use of Bayes factors as a routine tool to assess endorsement of new medications, because Bayes factors consistently quantify strength of evidence. Use of p-values may lead to paradoxical and spurious decision-making regarding the use of new medications. [ABSTRACT FROM AUTHOR]

Many people exhibit inconsistent preferenceswhen theymake choices based on descriptive summaries as compared to choices based on prior experiences. Theoretically, factors that promote more deliberative and skilled decision making (e.g., statistical numeracy) should also tend to promote more consistent choices and preferences regardless of information presentation formats. To test this hypothesis, in two studies I investigated individual differences in information search strategies (e.g., the amount and direction of information search) while estimating the degree to which numeracy predicted risky choice consistency across (a) decisions-from-description and (b) the decisions-from-samples. Results from Study 1 revealed that numeracy was generally associated with more extensive sampling and greater choice consistency across paradigms. Instead of reflecting differences in logical maximizing strategies (e.g., calculating expected values), analyses indicated that highly-numerate participants largely generated consistent choices by relying on heuristic-type processes (e.g., alternated between options less and exploring options with higher outcome variability more). In Study 2, the relationship between numeracy and choice consistency was eliminated when all participants were forced to make choices after experiencing the same fixed amount of sampling (i.e., provided the same amount of information). Taken together, the results of both studies converge with and extend previous findings (e.g., Skilled Decision Theory), indicating that numerate participants may generally exhibit more consistent preferences and choices via deliberatative heuristic search during decision making. [ABSTRACT FROM AUTHOR]

The current investigation extended past efforts on cultural differences in sport fandom by investigating sport fandom in Qatar, a previously unexplored culture. A sample of 259 sport fans completed a questionnaire packet assessing demographic variables, sport fandom, team identification, impacts of various socialization agents, and six typical sport fan behaviors. The results revealed a number of interesting findings including gender differences (males exhibited both greater levels of identification with a team and sport fandom than females), the influence of team-related factors for selecting a sport team, and a particularly large percentage of foreign teams listed as one's favorite team. To further our understanding of the behavior of sport fans globally and provide insights to sport managers, the results of this research are compared and contrasted with similar studies conducted in Australia, Greece, Norway, U.K., and the U.S. [ABSTRACT FROM AUTHOR]

We implemented a Choice Blindness Paradigm containing political statements in Argentina to reveal the existence of categorical ranges of introspective reports, identified by confidence and agreement levels, separating easy from very hard to manipulate decisions. CBP was implemented in both live and web-based forms. Importantly, and contrary to what was observed in Sweden, we did not observe changes in voting intentions. Also, confidence levels in the manipulated replies where significantly lower than in non-manipulated cases even in undetected manipulations. We name this phenomenon unconscious detection of self-deception. Results also show that females are more difficult to manipulate than men. [ABSTRACT FROM AUTHOR]

The presence of institutional investors may change the behaviors of companies. The impact of institutional investors on management decisions on financial affairs have been proven in other studies; yet, the present study aims to investigate the relationship between lack of information symmetry an concentration of proprietorship with profit management in the companies listed in Tehran Stock Exchange. The period under study included years 2004 to 2009. Rahavard Novin Computer application was utilized to collect the data; then they were calculated using Microsoft Excel. SPSS computer application was used to test research hypothesis and conduct other statistical analysis using statistical methods like descriptive statistics, correlation (correlation coefficient, coefficient of determination), regression analysis and testing its coefficients, correlation analysis and test of coefficients and significance of the difference between two correlation coefficients. A total of 109 active companies in the stock exchange were selected as the sample of the study. Results on the first hypothesis revealed that the relationship between concentration of proprietorship and lack of information symmetry. In case of the second hypothesis, it is predicted that there is a significant relationship between profit management with lack of information symmetry and concentration of proprietorship. According to research results, investors and analysts ought to pay attention to factors like proprietorship composition, the level of information symmetry along with the figures published by the companies when making short and long-term decisions. [ABSTRACT FROM AUTHOR]

Previous researches support that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Moreover, literature shows that different types of graphical information can help or harm the accuracy on decision making of accountants and financial analysts. We conducted a 4×2 mixed-design experiment to examine the effects of numerical information disclosure on financial analysts’ accuracy, and investigated the role of overconfidence in decision making. Results show that compared to text, column graph enhanced accuracy on decision making, followed by line graphs. No difference was found between table and textual disclosure. Overconfidence harmed accuracy, and both genders behaved overconfidently. Additionally, the type of disclosure (text, table, line graph and column graph) did not affect the overconfidence of individuals, providing evidence that overconfidence is a personal trait. This study makes three contributions. First, it provides evidence from a larger sample size (295) of financial analysts instead of a smaller sample size of students that graphs are relevant decision aids to tasks related to the interpretation of numerical information. Second, it uses the text as a baseline comparison to test how different ways of information disclosure (line and column graphs, and tables) can enhance understandability of information. Third, it brings an internal factor to this process: overconfidence, a personal trait that harms the decision-making process of individuals. At the end of this paper several research paths are highlighted to further study the effect of internal factors (personal traits) on financial analysts’ accuracy on decision making regarding numerical information presented in a graphical form. In addition, we offer suggestions concerning some practical implications for professional accountants, auditors, financial analysts and standard setters. [ABSTRACT FROM AUTHOR]

In this study, we conducted a laboratory experiment to assess preemptive striking by and towards individuals or groups. In the framework of a preemptive strike game, we set the following four conditions: one person faced another person, one person faced a three-person group, a three-person group faced an individual, and a three-person group faced another three-person group. Previous studies have revealed that greed is activated when participants belong to a group, while fear is activated when participants interact with a group, and further, that attacking behaviors in the preemptive strike game are driven by fear. These observations led to a hypothesis that high attack rates would be realized when participants interact with a group, regardless of whether the participants make decisions as individuals or a group. The results of our experiment, however, rejected this hypothesis. Among the four conditions, the attack rate was highest when a three-person group faced an individual. As possible reasons for our observation, we discuss the potential threat stemming from the imbalance in the effectiveness of attack between individuals and groups, and the (incorrect) belief by groups that single individuals would be more likely to attack out of fear. [ABSTRACT FROM AUTHOR]

This paper presents a Multi-Hypotheses Tracking (MHT) approach that allows solving ambiguities that arise with previous methods of associating targets and tracks within a highly volatile vehicular environment. The previous approach based on the Dempster–Shafer Theory assumes that associations between tracks and targets are unique; this was shown to allow the formation of ghost tracks when there was too much ambiguity or conflict for the system to take a meaningful decision. The MHT algorithm described in this paper removes this uniqueness condition, allowing the system to include ambiguity and even to prevent making any decision if available data are poor. We provide a general introduction to the Dempster–Shafer Theory and present the previously used approach. Then, we explain our MHT mechanism and provide evidence of its increased performance in reducing the amount of ghost tracks and false positive processed by the tracking system. [ABSTRACT FROM AUTHOR]

Cluster interpretation is an important step for a proper understanding of a set of classes, independently of whether they have been automatically discovered or expert-based. An understanding of classes is crucial for the further use of classes as the basis of a decision-making process. The abundant work on cluster validity found in the literature is mainly focused on the validation of clusters from the structural point of view. However, structural validation does not ensure that the clustering is useful, since meaningfulness is the key to guaranteeing that classes can support further decisions. In previous works, special significance tests taken from the field of multivariate analysis were introduced in an interpretation methodology for automatically assessing relevant variables in particular classes. In this paper, we present the interpretation of nested partitions and the relationships between both interpretations are studied. In particular, the inconsistencies produced in interpretation when a second partition refines the first one with a higher level of granularity are studied, diagnosed, and a modification of the original methodology is provided to guarantee consistency in these cases. The relevant characteristics detected in a parent class must also be inherited in subclasses, or at least in some of them. The proposal is evaluated using a real data set on baseline health conditions and dietary habits of a sample of the general population. [ABSTRACT FROM AUTHOR]

The article presents the study that explores on the use of haematopoietic stem cell transplantation (SCT) in the treatment of Fanconi anemia (FA) patients in Canada and the U.S. The topics discussed include the attributes of FA, the factors that influenced the decision making of FA patients and family members on SCT, and the benefits and probable risks of SCT.

More than 500,000 children sustain a traumatic brain injury (TBI) each year. Previous studies have described significant variation in inhospital mortality after pediatric TBI. The aim of this study was to identify facility-level characteristics independently associated with 30-day inhospital mortality after pediatric severe TBI. We hypothesized that, even after accounting for patient-level characteristics associated with mortality, the characteristics of facilities where patients received care would be associated with inhospital mortality. Using data from the National Trauma Data Bank from 2009-2012, we identified a cohort of 6707 pediatric patients hospitalized with severe TBI in 391 facilities and investigated their risk of 30-day inhospital mortality. Pre-specified facility-level characteristics (trauma certification level, teaching status, census region, facility size, nonprofit status, and responsibility for pediatric trauma care) were added to a Poisson regression model that accounted for patient-level characteristics associated with mortality. In multivariable analyses, patients treated in facilities located in the Midwest (risk ratio [RR]=1.42; 95% confidence interval [CI] 1.12-1.81) and South (RR=1.39; 95% CI: 1.12-1.72) regions had higher likelihoods of 30-day inhospital mortality compared with patients treated in the Northeast. Other facility-level characteristics were not found to be significant. To our knowledge, this is one of the largest investigations to identify regional variation in inhospital mortality after pediatric severe TBI in a national sample after accounting for individual and other facility-level characteristics. Further investigations to help explain this variation are needed to inform evidence-based decision-making for pediatric severe TBI care across different settings. [ABSTRACT FROM AUTHOR]

Highlights: [•] Apply semantic analysis on internet data to identify weak signals. [•] Project further data into semantic subspace to identify weak signal time series. [•] Compare time series to given hypotheses. [•] Provide relevant weak signal developments to decision makers. [Copyright &y& Elsevier]

Jumping to conclusions (JTC) during probabilistic reasoning is a cognitive bias repeatedly demonstrated in people with schizophrenia and shown to be associated with delusions. Little is known about the neurochemical basis of probabilistic reasoning. We tested the hypothesis that catecholamines influence data gathering and probabilistic reasoning by administering intravenous methamphetamine, which is known to cause synaptic release of the catecholamines noradrenaline and dopamine, to healthy humans whilst they undertook a probabilistic inference task. Our study used a randomised, double-blind, cross-over design. Seventeen healthy volunteers on three visits were administered either placebo or methamphetamine or methamphetamine preceded by amisulpride. In all three conditions participants performed the “beads” task in which participants decide how much information to gather before making a probabilistic inference, and which measures the cognitive bias towards jumping to conclusions. Psychotic symptoms triggered by methamphetamine were assessed using Comprehensive Assessment of At-Risk Mental States (CAARMS). Methamphetamine induced mild psychotic symptoms, but there was no effect of drug administration on the number of draws to decision (DTD) on the beads task. DTD was a stable trait that was highly correlated within subjects across visits (intra-class correlation coefficients of 0.86 and 0.91 on two versions of the task). The less information was sampled in the placebo condition, the more psychotic-like symptoms the person had after the methamphetamine plus amisulpride condition (p = 0.028). Our results suggest that information gathering during probabilistic reasoning is a stable trait, not easily modified by dopaminergic or noradrenergic modulation. [ABSTRACT FROM AUTHOR]

Abstract: Environmental policy and decision-making are characterized by complex interactions between different actors and sectors. As a rule, a stakeholder analysis is performed to understand those involved, but it has been criticized for lacking quality and consistency. This lack is remedied here by a formal social network analysis that investigates collaborative and multi-level governance settings in a rigorous way. We examine the added value of combining both elements. Our case study examines infrastructure planning in the Swiss water sector. Water supply and wastewater infrastructures are planned far into the future, usually on the basis of projections of past boundary conditions. They affect many actors, including the population, and are expensive. In view of increasing future dynamics and climate change, a more participatory and long-term planning approach is required. Our specific aims are to investigate fragmentation in water infrastructure planning, to understand how actors from different decision levels and sectors are represented, and which interests they follow. We conducted 27 semi-structured interviews with local stakeholders, but also cantonal and national actors. The network analysis confirmed our hypothesis of strong fragmentation: we found little collaboration between the water supply and wastewater sector (confirming horizontal fragmentation), and few ties between local, cantonal, and national actors (confirming vertical fragmentation). Infrastructure planning is clearly dominated by engineers and local authorities. Little importance is placed on longer-term strategic objectives and integrated catchment planning, but this was perceived as more important in a second analysis going beyond typical questions of stakeholder analysis. We conclude that linking a stakeholder analysis, comprising rarely asked questions, with a rigorous social network analysis is very fruitful and generates complementary results. This combination gave us deeper insight into the socio-political-engineering world of water infrastructure planning that is of vital importance to our well-being. [Copyright &y& Elsevier]

This paper considers a collaborative human decision making framework in which local decisions made at the individual agents are combined at a moderator to make the final decision. More specifically, we consider a binary hypothesis testing problem in which a group of n people makes individual decisions on which hypothesis is true based on a threshold based scheme and the thresholds are modeled as random variables. We assume that, in general, the decisions are not received by the moderator perfectly and the communication errors are modeled via a binary asymmetric channel. Assuming that the moderator does not have the knowledge of exact values of thresholds used by the individual decision makers but has probabilistic information, the performance in terms of the probability of error of the likelihood ratio based decision fusion scheme is derived when there are two agents in the decision making system. We show that the statistical parameters of the threshold distributions have optimal set of values which result in the minimum probability of error and we analytically derive these optimal values under certain conditions. We further provide detailed performance comparison to the case where the likelihood ratio based decision fusion is performed at the moderator with exact knowledge of the thresholds used by individual agents. For an arbitrary number of human agents n(> 2), we derive the performance of decision fusion with majority rule using certain approximations when the individual thresholds are modeled as random variables. [ABSTRACT FROM PUBLISHER]

Perceptual decision making is believed to be driven by the accumulation of sensory evidence following stimulus encoding. More controversially, some studies report that neural activity preceding the stimulus also affects the decision process. We used a multivariate pattern classification approach for the analysis of the human electroencephalogram (EEG) to decode choice outcomes in a perceptual decision task from spatially and temporally distributed patterns of brain signals. When stimuli provided discriminative information, choice outcomes were predicted by neural activity following stimulus encoding; when stimuli provided no discriminative information, choice outcomes were predicted by neural activity preceding the stimulus. Moreover, in the absence of discriminative information, the recent choice history primed the choices on subsequent trials. A diffusion model fitted to the choice probabilities and response time distributions showed that the starting point of the evidence accumulation process was shifted toward the previous choice, consistent with the hypothesis that choice priming biases the accumulation process toward a decision boundary. This bias is reflected in prestimulus brain activity, which, in turn, becomes predictive of future decisions. Our results provide a model of how non-stimulus-driven decision making in humans could be accomplished on a neural level. [ABSTRACT FROM AUTHOR]

This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of N agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With this collaboration, optimal diverse regular quantization with K cells per quantizer performs as well as optimal identical quantization with N(K-1)+1 cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion. [ABSTRACT FROM PUBLISHER]

Differential growth rate between males and females, owing to a sexual size dimorphism, has been proposed as a mechanism driving sex-biased survival. How parents respond to this selection pressure through sex ratio manipulation and sex-biased parental investment can have a dramatic influence on fitness. We determined how differential growth rates during early life resulting from sexual size dimorphism affected survival of young and how parents may respond in a precocial bird, the black brant Branta bernicla nigricans. We hypothesized that more rapidly growing male goslings would suffer greater mortality than females during brood rearing and that parents would respond to this by manipulating their primary sex ratio and parental investment. Male brant goslings suffered a 19.5% reduction in survival relative to female goslings and, based on simulation, we determined that a female biased population sex ratio at fledging was never overcome even though previous work demonstrated a slight male-biased post-fledging survival rate. Contrary to the Fisherian sex ratio adjustment hypothesis we found that individual adult female brant did not manipulate their primary sex ratio (50.39% male, n = 645), in response to the sex-biased population level sex ratio. However, female condition at the start of the parental care period was a good predictor of their primary sex ratio. Finally, we examined how females changed their behavior in response to primary sex ratio of their broods. We hypothesized that parents would take male biased broods to areas with increased growth rates. Parents with male biased primary sex ratios took broods to areas with higher growth rates. These factors together suggest that sex-biased growth rates during early life can dramatically affect population dynamics through sex-biased survival and recruitment which in turn affects decisions parents make about sex allocation and sex-biased parental investment in offspring to maximize fitness. [ABSTRACT FROM AUTHOR]

We study the detection error probability associated with a balanced binary relay tree, where the leaves of the tree correspond to N identical and independent sensors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree is a relay node that combines two binary messages to form a single output binary message. Only the leaves are sensors. In this way, the information from the sensors is aggregated into the fusion center via the relay nodes. In this context, we describe the evolution of the Type I and Type II error probabilities of the binary data as it propagates from the leaves toward the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of N are derived. These characterize how fast the total error probability converges to 0 with respect to N, even if the individual sensors have error probabilities that converge to 1/2. [ABSTRACT FROM PUBLISHER]

Background Socioeconomically deprived women are at greater risk of adverse pregnancy outcomes. Research tends to focus on access of services. Yet access may not equate with the equity of services for women from different socioeconomic backgrounds. Objectives To determine whether pregnant women's perceptions of antenatal provision differed in relation to their socioeconomic deprivation ranking (determined by the Scottish Index of Multiple Deprivation 2006). Design A longitudinal, qualitative study with comparative antenatal case studies between January 2007 and April 2009. Setting/Participants Cases were primigravida women from 'least deprived' ( n = 9) and 'most deprived' ( n = 12) geographical areas within one local authority in Scotland. Analysis Data were analysed using case study replication analysis. Results There was little difference in access to antenatal services between the 'least' and 'most' deprived groups. Perception of care differed in relation to the level of 'engagement' (defined using constructs of: language and personalization of care; power and relationships; and health literacy). Engagement was evidenced in most of the 'least deprived' cases and almost none of the 'most deprived' cases. Specifically, socioeconomically deprived women described less evidence of personal connection to their own care, effective communication and the opportunity for shared decision making. Conclusion In women from socioeconomically deprived areas, access may be a less useful indicator than engagement when assessing antenatal service quality. As engagement levels may be one method by which to predict and improve health outcomes, a more equitable antenatal service may need to be developed through the early identification of those women at risk of non-engagement. [ABSTRACT FROM AUTHOR]

Although the temporal scalability coding supported by both H.264/AVC and H.264/SVC provides a good means of adapting bitstream scalability in temporal dimension, it entails heavy computational complexity due to bidirectional motion estimation and compensation for B-pictures in temporal scalability levels. To make it more applicable, this paper introduces a fast inter-prediction mode decision method which is efficiently performed using statistical hypothesis testing. The hypothesis testing is performed on mean and variance of pixels in 16 \times 16 and 8 \times 8 blocks to decide early termination against further processing for their sub-blocks. Fallback check is employed to compromise the tradeoff between the compression efficiency and encoding time saving. The proposed method exhibits effective performance for early termination in the inter-prediction mode decision, thus leading to a significant reduction of the total encoding time up to average 64.8% with small increments in bit amounts and small degradation in visual quality. [ABSTRACT FROM PUBLISHER]

Abstract: The documentation of software architectural design decisions is important to help people understand the system and the rationale behind architectural solutions. In practice, the documentation of such decisions is regularly done after the fact, or skipped completely. To support software maintenance and evolution, the decisions have to be recovered and described. This is often hindered by the fact that the original architects are not available any more, or they do not completely remember the reasons for making the decisions. Additionally, the whole process is very expensive. In this paper, we hypothesize that architecture decision recovery can be more efficient by focusing on recovering decisions related to applying architecture patterns. To test this hypothesis, we designed a controlled experiment that was conducted to analyze the impact of architecture patterns on the quality and quantity of architecture decisions recovered after the fact. We are able to provide statistical evidence that a focus on patterns significantly increases the quality of decisions, while no conclusive evidence concerning the quantity of decisions was found. [Copyright &y& Elsevier]

There has been a recent increase in interest among evolutionary researchers in the hypothesis that humans evolved as cooperative breeders, using extended family support to help decrease offspring mortality and increase the number of children that can be successfully reared. In this study, data drawn from the 1970 longitudinal British cohort study were analysed to determine whether extended family support encourages fertility in contemporary Britain. The results showed that at age 30, reported frequency that participants saw their own parents (but not in-laws) and the closeness of the bond between the participant and their own parents were associated with an increased likelihood of having a child between ages 30 and 34. Financial help and reported grandparental childcare were not significantly positively associated with births from age 30 to 34. Men's income was positively associated with likelihood of birth, whereas women's income increased likelihood of birth only for working women with at least one child. While it was predicted that grandparental financial and childcare help would increase the likelihood of reproduction by lowering the cost to the parent of having a child, it appears that the mere physical presence of supportive parents rather than their financial or childcare help encouraged reproduction in the 1970 British birth cohort sample. [ABSTRACT FROM AUTHOR]

Context and objective Shared decision making is especially advocated for preference-sensitive decisions. We investigated whether physicians' recommendations pull patients away from their preferred treatment option when making a preference-sensitive decision. Design, participants and methods Inpatients ( N = 102 with schizophrenia, N = 101 with multiple sclerosis) were presented with a hypothetical scenario (the choice between two drugs). They were first asked about their preferences concerning the two drugs and then they received a (fictitious) clinician's recommendation that was contrary to their preferences. Subsequently they made a final choice between the two drugs. Main outcome measures The main outcome measure was whether the patient followed the physician's advice in the hypothetical scenario. Thereby patient's (pre-recommendation) preferences served as a baseline. Results In the decision scenario, about 48% of the patients with schizophrenia and 26% of the patients with multiple sclerosis followed the advice of their physician and thus chose the treatment option that went against their initial preferences. Patients who followed their physician's advice were less satisfied with their decision than patients not following their physician's advice (schizophrenia: t = 2.61, P = 0.01; multiple sclerosis: t = 2.67, P = 0.009). Discussion and conclusions When sharing decisions with patients, physicians should be aware that their advice might influence patients' decisions away from their preferred treatment option. They should encourage their patients to identify their own preferences and help to find the treatment option most consistent with them. [ABSTRACT FROM AUTHOR]

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules. [ABSTRACT FROM AUTHOR]

The aim of this article is to evaluate the contribution of process tracing data to the development and testing of models of judgment and decision making (JDM). We draw on our experience of editing the "Handbook of process tracing methods for decision research" recently published in the SJDM series. After a brief introduction we first describe classic process tracing methods (thinking aloud, Mouselab, eye-tracking). Then we present a series of examples of how each of these techniques has made important contributions to the development and testing of process models of JDM. We discuss the issue of large data volumes resulting from process tracing and remedies for handling those. Finally, we argue for the importance of formulating process hypotheses and opt for a multi-method approach that focuses on the cross-validation of findings. [ABSTRACT FROM AUTHOR]

Judgment and decision making research overwhelmingly uses null hypothesis significance testing as the basis for statistical inference. This article examines an alternative, Bayesian approach which emphasizes the choice between two competing hypotheses and quantifies the balance of evidence provided by the data--one consequence of which is that experimental results may be taken to strongly favour the null hypothesis. We apply a recently-developed "Bayesian t-test" to existing studies of the anchoring effect in judgment, and examine how the change in approach affects both the tone of hypothesis testing and the substantive conclusions that one draws. We compare the Bayesian approach with Fisherian and Neyman-Pearson testing, examining its relationship to conventional p-values, the influence of effect size, and the importance of prior beliefs about the likely state of nature. The results give a sense of how Bayesian hypothesis testing might be applied to judgment and decision making research, and of both the advantages and challenges that a shift to this approach would entail. [ABSTRACT FROM AUTHOR]

Numerous psychophysical studies suggest that the sensorimotor system chooses actions that optimize the average cost associated with a movement. Recently, however, violations of this hypothesis have been reported in line with economic theories of decision-making that not only consider the mean payoff, but are also sensitive to risk, that is the variability of the payoff. Here, we examine the hypothesis that risk-sensitivity in sensorimotor control arises as a mean-variance trade-off in movement costs. We designed a motor task in which participants could choose between a sure motor action that resulted in a fixed amount of effort and a risky motor action that resulted in a variable amount of effort that could be either lower or higher than the fixed effort. By changing the mean effort of the risky action while experimentally fixing its variance, we determined indifference points at which participants chose equiprobably between the sure, fixed amount of effort option and the risky, variable effort option. Depending on whether participants accepted a variable effort with a mean that was higher, lower or equal to the fixed effort, they could be classified as risk-seeking, risk-averse or risk-neutral. Most subjects were risk-sensitive in our task consistent with a mean-variance trade-off in effort, thereby, underlining the importance of risk-sensitivity in computational models of sensorimotor control. [ABSTRACT FROM AUTHOR]

We consider the problem of sensor selection for time-optimal detection of a hypothesis. We consider a group of sensors transmitting their observations to a fusion center. The fusion center considers the output of only one randomly chosen sensor at the time, and performs a sequential hypothesis test. We study a sequential multiple hypothesis test with randomized sensor selection strategy. We incorporate the random processing times of the sensors to determine the asymptotic performance characteristics of this test. For three distinct performance metrics, we show that, for a generic set of sensors and binary hypothesis, the time-optimal policy requires the fusion center to consider at most two sensors. We also show that for the case of multiple hypothesis, the time-optimal policy needs at most as many sensors to be observed as the number of underlying hypotheses. [ABSTRACT FROM AUTHOR]

Most existing stopping criteria for turbo decoding have their root in hypothesis test, requiring a subjective threshold for decision making. A consequence is that the turbo decoding receiver so-constructed can converge at high signal-to-noise ratios (SNRs) but fails at low SNRs, thereby calling for a new design philosophy for stopping criteria. In this correspondence, the problem is tackled in the framework of information theoretic criterion, which enables the turbo decoding to properly work in a changing SNR environment. Numerical results are presented for illustrating the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Helping behaviour in cooperative breeding systems has been attributed to kin selection, but the relative roles of direct and indirect fitness benefits in the evolution of such systems remain a matter of debate. In theory, helpers could maximize the indirect fitness benefits of cooperation by investing more in broods with whom they are more closely related, but there is little evidence for such fine-scale adjustment in helper effort among cooperative vertebrates. In this study, we used the unusual cooperative breeding system of the long-tailed tit Aegithalos caudatus to test the hypothesis that the provisioning effort of helpers was positively correlated with their kinship to broods. We first use pedigrees and microsatellite genotypes to characterize the relatedness between helpers and breeders from a 14 year field study. We used both pedigree and genetic approaches because long-tailed tits have access to pedigree information acquired through social relationships, but any fitness consequences will be determined by genetic relatedness. We then show using both pedigrees and genetic relatedness estimates that alloparental investment by helpers increases as their relatedness to the recipients of their care increases. We conclude that kin selection has played a critical role in moulding the investment decisions of helpers in this cooperatively breeding species. [ABSTRACT FROM AUTHOR]

Abstract: Process capability indices are widely used in industry to measure the ability of firms or their suppliers to meet quality specifications. The index , which is easy to use and analytically tractable, has been successfully developed and applied by competitive firms to dominate highly-profitable markets by improving quality and productivity. Hypothesis testing is very essential for practical decision-making. Generally, the underlying data are assumed to be precise numbers, but in general it is much more realistic to consider fuzzy values, which are imprecise numbers. In this case, the test statistic also yields an imprecise number, and decision rules based on the crisp-based approach are inappropriate. This study investigates the situation of uncertain or imprecise product quality measurements. A set of confidence intervals for sample mean and variance is used to produce triangular fuzzy numbers for estimating the index. Based on the -cuts of the fuzzy estimators, a decision testing rule and procedure are developed to evaluate process performance based on critical values and fuzzy p-values. An efficient computer program is also designed for calculating fuzzy p-values. Finally, an example is examined for demonstrating the application of the proposed approach. [Copyright &y& Elsevier]

Consider a standard statistical hypothesis test, leading to a binary decision made by exploiting a certain dataset. Suppose that, later, part of the data is lost, and we want to refine the test by exploiting both the surviving data and the previous decision. What is the best one can do? Such a question, here referred to as the unlucky broker problem, can be addressed by very standard tools from detection theory, but the solution gives intriguing insights and is by no means obvious. We provide the general form of the optimal detectors and discuss in depth their modus operandi, ranging from simple likelihood ratio tests to more complex behaviors. Limiting cases, where either the surviving data or the initial decision is almost useless, are also discussed. [ABSTRACT FROM AUTHOR]

Abstract: Predictive population models designed to assist managers and policy makers require an explicit treatment of inherent uncertainty and variability. These are particular concerns when modelling non-native and reintroduced species, when data have been collected within one geographical or ecological context but predictions are required for another, or when extending models to predict the consequences of environmental change (e.g., climate or land-use). We present an aspatial, probabilistic framework of hierarchical process models for predicting population growth even when data are sparse or of poor quality. Insight into the factors affecting population dynamics in real landscapes can be provided and Kullback–Leibler distances are used to compare the relative output of models. This flexible yet robust framework gives easily interpretable results, allowing managers as well as modellers to invalidate anomalous models and apply others to real-life scenarios. We illustrate the framework’s power with a meta-analysis of European wild boar (Sus scrofa) data. We test hypotheses about the effect of geographic region, hunting and mast years on wild boar population growth, to build models of wild boar dynamics for the UK. The framework quantifies the importance of hunting pressure as a driver of population growth, and confirms that reproductive success is greatly decreased in poor mast years, suggesting that the key to predicting wild boar dynamics is to ascertain local hunting pressure and to better understand changing food availability. Geography had no significant effect, indicating that it is not a good proxy for modelling the impact of change in climate or land-use on wild boar populations at the European scale. We use the framework to predict population abundance 9 years after an isolated population of wild boar established in the UK; in a comparison with the only field data and two independent modelling exercises, our framework provides the most robust and informative results. [Copyright &y& Elsevier]

We propose that when predicting future behavior, consumers selectively (but unwittingly) test the hypothesis that they will behave ideally. This selective hypothesis testing perspective on unrealistic optimism suggests that estimates of future behavior should be similar to those made by individuals who assume that conditions will be ideal. Moreover, consumers who initially provide estimates assuming that conditions will be ideal should recognize that the world is not ideal and so should test a more realistic hypothesis. In line with these predictions, we find that ideal- world estimates (e.g., In an ideal world, how often will you exercise next week?) do not differ from standard estimates (e.g., How often will you exercise next week?). We also find that individuals who initially estimate their behavior in an ideal world subsequently make more realistic predictions. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian hypothesis testing is investigated when the prior probabilities of the hypotheses, taken as a random vector, are quantized. Nearest neighbor and centroid conditions are derived using mean Bayes risk error (MBRE) as a distortion measure for quantization. A high-resolution approximation to the distortion-rate function is also obtained. Human decision making in segregated populations is studied assuming Bayesian hypothesis testing with quantized priors. [ABSTRACT FROM AUTHOR]