This paper aims to extend in two directions the probabilistic dynamic epistemic logic provided in Kooi’s paper (J Logic Lang Inform 12(4):381–408, 2003) and to relate these extensions to ones made in van Benthem et al. (Proceedings of LOFT’06. Liverpool, 2006). Kooi’s probabilistic dynamic epistemic logic adds to probabilistic epistemic logic sentences that express consequences of public announcements. The paper (van Benthem et al., Proceedings of LOFT’06. Liverpool, 2006) extends (Kooi, J Logic Lang Inform 12(4):381–408, 2003) to (...) using action models, but in both papers, the probabilities are discrete, and are defined on trivial σ -algebras over finite sample spaces. The first extension offered in this paper is to add a previous-time operator to a probabilistic dynamic epistemic logic similar to Kooi’s in (J Logic Lang Inform 12(4):381–408, 2003). The other is to involve non-trivial σ -algebras and continuous probabilities in probabilistic dynamic epistemic logic. (shrink)

In an earlier paper (Dupré 1984), I criticized a thesis sometimes defended by theorists of probabilistic causality, namely, that a probabilistic cause must raise the probability of its effect in every possible set of causally relevant background conditions (the "contextual unanimity thesis"). I also suggested that a more promising analysis of probabilistic causality might be sought in terms of statistical relevance in a fair sample. Ellery Eells (1987) has defended the contextual unanimity thesis against my objections, and (...) also raised objections of his own to my positive claims. In this paper I defend and amplify both my objections to the contextual unanimity thesis and my constructive suggestion. (shrink)

Intentional sampling methods are non-probabilistic procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. Intentional sampling methods are intended for exploratory research or pilot studies where tight budget constraints preclude the use of traditional randomized representative sampling. The possibility of subsequently generalize statistically from such deterministic samples to the general population has been the issue of long standing arguments and debates. Nevertheless, the intentional sampling techniques developed (...) in this paper explore pragmatic strategies for overcoming some of the real or perceived shortcomings and limitations of intentional sampling in practical applications. (shrink)

Scoring rules measure the accuracy or epistemic utility of a credence assignment. A significant literature uses plausible conditions on scoring rules on finite sample spaces to argue for both probabilism—the doctrine that credences ought to satisfy the axioms of probabilism—and for the optimality of Bayesian update as a response to evidence. I prove a number of formal results regarding scoring rules on infinite sample spaces that impact the extension of these arguments to infinite sample spaces. A common condition in the (...) arguments for probabilism and Bayesian update is strict propriety: that according to each probabilistic credence, the expected accuracy of any other credence is worse. Much of the discussion needs to divide depending on whether we require finite or countable additivity of our probabilities. I show that in a number of natural infinite finitely additive cases, there simply do not exist strictly proper scoring rules, and the prospects for arguments for probabilism and Bayesian update are limited. In many natural infinite countably additive cases, on the other hand, there do exist strictly proper scoring rules that are continuous on the probabilities, and which support arguments for Bayesian update, but which do not support arguments for probabilism. There may be more hope for accuracy-based arguments if we drop the assumption that scores are extended-real-valued. I sketch a framework for scoring rules whose values are nets of extended reals, and show the existence of a strictly proper net-valued scoring rules in all infinite cases, both for f.a. and c.a. probabilities. These can be used in an argument for Bayesian update, but it is not at present known what is to be said about probabilism in this case. (shrink)

We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty and represent the agent’s beliefs about probability, we consider a plausibility map, associating to each possible distribution a plausibility ranking. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds. We consider two forms of conditioning or belief update, corresponding to the acquisition of two types of information: learning observable evidence obtained by (...) repeated sampling from the unknown distribution; and learning higher-order information about the distribution. The first changes only the plausibility map, but leaves the given set of possible distributions essentially unchanged; the second rules out some distributions, thus shrinking the set of possibilities, without changing their plausibility ordering.. We look at stability of beliefs under either of these types of learning, defining two related notions, as well as a measure of the verisimilitude of a given plausibility model. We prove a number of convergence results, showing how our agent’s beliefs track the true probability after repeated sampling, and how she eventually gains in a sense knowledge of that true probability. Finally, we sketch the contours of a dynamic doxastic logic for statistical learning. (shrink)

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behaviour change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behaviour change scale. Structural equation modelling examined three (...) progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behaviour change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behaviour change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behaviour change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behaviour change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behaviour change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behaviour change. The discussion critically examines these findings in relation to dual-processing frameworks. This includes considering the limitations of current operationalisations and recommendations for future research that align outcomes and subsequent work more closely to specific dual-process models. (shrink)

Participants were asked to draw inferences about correlation from single x,y observations. In Experiment 1 statistically sophisticated participants were given the univariate characteristics of distributions of x and y and asked to infer whether a single x, y observation came from a correlated or an uncorrelated population. In Experiment 2, students with a variety of statistical backgrounds assigned posterior probabilities to five possible populations based on single x, y observations, again given knowledge of the univariate statistics. In Experiment 3, statistically (...) naïve participants were given a problem analogous to that given in Experiment 1, framed verbally. Experiment 4 replicated Experiment 3 but added an “impossible to determine” response option. Models that rely on computing sample correlations make no predictions about these investigations. From a Bayesian perspective, participants’ inferences in all four experiments tended to make probabilistically valid inferences as long as the single datum was directional. The results are discussed in light of the Brunswikian notion of vicarious functioning. (shrink)

Objective: Compassion has been associated with eudaimonia and prosocial behavior, and has been regarded as a virtue, both historically and cross-culturally. However, the psychological study of compassion has been limited to laboratory settings and/or standard survey assessments. Here, we use an experience sampling method (ESM) to compare naturalistic assessments of compassion with standard assessments, and to examine compassion, its variability, and associations with eudaimonia and prosocial behavior. -/- Methods: Participants took a survey which included standard assessments of compassion and (...) eudaimonia. Then, over four days, they were repeatedly asked about their level of compassion, eudaimonia, and situational factors within the moments of daily life. Finally, prosocial behavior was tested using the Dual Gamble Task and an opportunity to donate task winnings. -/- Results: Analyses revealed within-person associations between ESM compassion and eudaimonia. ESM compassion also predicted eudaimonia at the next ESM time point. While not impervious to situational factors, considerable consistency was observed in ESM compassion in comparison with eudaimonia. Further, ESM compassion along with eudaimonia predicted donating behavior. Standard assessments did not. -/- Conclusion: Consistent with virtue theory, some individual’s reports displayed a probabilistic tendency toward compassion, and ESM compassion predicted ESM eudaimonia and prosocial behavior toward those in need. (shrink)

This study assessed the extent to which within-individual variation in schizotypy and paranormal belief influenced performance on probabilistic reasoning tasks. A convenience sample of 725 non-clinical adults completed measures assessing schizotypy (Oxford-Liverpool Inventory of Feelings and Experiences; O-Life brief), belief in the paranormal (Revised Paranormal Belief Scale; RPBS) and probabilistic reasoning (perception of randomness, conjunction fallacy, paranormal perception of randomness, and paranormal conjunction fallacy). Latent profile analysis (LPA) identified four distinct groups: class 1, low schizotypy and low paranormal (...) belief (43.9% of sample); class 2, moderate schizotypy and moderate paranormal belief (18.2%); class 3, moderate schizotypy (high cognitive disorganization) and low paranormal belief (29%); and class 4, moderate schizotypy and high paranormal belief (8.9%). Identification of homogeneous classes provided a nuanced understanding of the relative contribution of schizotypy and paranormal belief to differences in probabilistic reasoning performance. Multivariate analysis of covariance revealed that groups with lower levels of paranormal belief (classes 1 and 3) performed significantly better on perception of randomness, but not conjunction problems. Schizotypy had only a negligible effect on performance. Further analysis indicated that framing perception of randomness and conjunction problems in a paranormal context facilitated performance for all groups but class 4. (shrink)

The need for a ‘many-valued logic’ in linguistics has been evident since the 1970s, but there was lack of clarity as to whether it should come from the family of fuzzy logics or from the family of probabilistic logics. In this regard, Fine [14] and Kamp [26] pointed out undesirable effects of fuzzy logic (the failure of idempotency and coherence) which kept two generations of linguists and philosophers at arm’s length. (Another unwanted feature of fuzzy logic is the property (...) of truth functionality.) While probabilistic logic is not fraught by the same problems, its lack of constructiveness, i.e. its inability to compose complex truth degrees from atomic truth degrees, did not make it more attractive to linguists either. In the absence of a clear perspective in ‘many-valued logic’, scholars chose to proliferate ontologies grafted atop the classical bivalent logic: ontologies for truth, individuals, events, situations, possible worlds and degrees. The result has been a collection of incompatible classical logics. In this paper, I present sample logic, in particular its semantics (not its axiomatization). Sample logics is a member of the family of probabilistic logics, which is constructive without being truth functional. More specifically, I integrate all the important linguistic data on which the classical logics are predicated. The concepts of (in)dependency and conditional (in)dependency are the cornerstones of sample logic. (shrink)

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ (...) rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency. (shrink)

Clatterbuck et al. (Biol Philos 28: 577–592, 2013) argue that there is no fact of the matter whether selection dominates drift or vice versa in any particular case of evolution. Their reasons are not empirically based; rather, they are purely conceptual. We show that their conceptual presuppositions are unmotivated, unnecessary and overly complex. We also show that their conclusion runs contrary to current biological practice. The solution is to recognize that evolution involves a probabilistic sampling process, and that (...) drift is a deviation from probabilistic expectation. We conclude that conceptually, there are no problems with distinguishing drift from selection, and empirically—as modern science illustrates—when drift does occur, there is a quantifiable fact of the matter to be discovered. (shrink)

How should we combine disagreeing expert judgments on the likelihood of an event? A common solution is simple averaging, which allows independent individual errors to cancel out. However, judgments can be correlated due to an overlap in their information, resulting in a miscalibration in the simple average. Optimal weights for weighted averaging are typically unknown and require past data to estimate reliably. This paper proposes an algorithm to aggregate probabilistic judgments under shared information. Experts are asked to report a (...) prediction and a meta-prediction. The latter is an estimate of the average of other individuals’ predictions. In a Bayesian setup, I show that if average prediction is a consistent estimator, the percentage of predictions and meta-predictions that exceed the average prediction should be the same. An “overshoot surprise” occurs when the two measures differ. The Surprising Overshoot algorithm uses the information revealed in an overshoot surprise to correct for miscalibration in the average prediction. Experimental evidence suggests that the algorithm performs well in moderate to large samples and in aggregation problems where individuals disagree in their predictions. (shrink)

I I set out my view that all inference is essentially deductive and pinpoint what I take to be the major shortcomings of the induction rule.II The import of data depends on the probability model of the experiment, a dependence ignored by the induction rule. Inductivists admit background knowledge must be taken into account but never spell out how this is to be done. As I see it, that is the problem of induction.III The induction rule, far from providing a (...) method of discovery, does not even serve to detect pattern. Knowing that there is uniformity in the universe is no help to discovering laws. A critique of Reichenbach's justification of the straight rule is constructed along these lines.IV The induction rule, by itself, cannot account for the varying rates at which confidence in an hypothesis mounts with data. The mathematical analysis of this salient feature of inductive reasoning requires prior probabilities. We also argue, against orthodox statisticians, that prior probabilities make a substantive contribution to the objectivity of inductive methods, viz. to the design of experiments and the selection of decision rules.V Carnap's general criticisms of various estimation rules, like the straight rule and the ‘impervious rule’, are seen to be misguided when the prior densities to which they correspond are taken into account.VI Analysis of Hempel's definition of confirmation qua formalization of the enumerative (naive) conception of instancehood. We show that from the standpoint of the quantitative measure P(H/E):P(H) for the degree to which E confirms H, Hempel's classificatory concept yields correct results only for sampling at large from a finite population with a two-way classification all of whose compositions are equally probable. We extend the analysis to Goodman's paradox, finding cases in which grue-like hypotheses do receive as much confirmation as their opposite numbers. We argue, moreover, the irrelevancy of entrenchment, and maintain that Goodman's paradox is no more than a straightforward counter-example to the enumerative conception of instancehood embodied in Hempel's definition.VII We rebutt the objection that prior probabilities, qua inputs of Bayesian analysis, can only be obtained by enumerative induction (insofar as they are objective). The divergence in the prior densities of two rational agents is less a function of subjectivity, we maintain, than of vagueness.VIII Our concluding remarks stress that, for Bayesians, there is no problem of induction in the usual sense. (shrink)

BackgroundDistorted gambling-related cognitions are tightly related to gambling problems, and are one of the main targets of treatment for disordered gambling, but their etiology remains uncertain. Although folk wisdom and some theoretical approaches have linked them to lower domain-general reasoning abilities, evidence regarding that relationship remains unconvincing.MethodIn the present cross-sectional study, the relationship between probabilistic/abstract reasoning, as measured by the Berlin Numeracy Test, and the Matrices Test, respectively, and the five dimensions of the Gambling-Related Cognitions Scale, was tested in (...) a sample of 77 patients with gambling disorder and 58 individuals without gambling problems.Results and interpretationNeither BNT nor matrices scores were significantly related to gambling-related cognitions, according to frequentist analyses, performed both considering and disregarding group in the models. Correlation Bayesian analyses largely supported the null hypothesis, i.e., the absence of relationships between the measures of interest. This pattern or results reinforces the idea that distorted cognitions do not originate in a general lack of understanding of probability or low fluid intelligence, but probably result from motivated reasoning. (shrink)

The Discounting Inventory, originally developed in polish language, allows the measurement of individual differences in the delay, probabilistic, effort, and social discounting rates. The present study attempted to validate the DI’s psychometric properties using German university students and to compare the results to those from a sample of Polish university students. Over four hundred participants completed the DI and traditional discounting measures. A confirmatory factor analyses indicated that the original four-factor model of the DI provided an excellent fit for (...) the German data and internal consistency was high. These outcomes were similar, if not superior, to those from the Polish sample. DI scores strongly correlated with traditional discounting measures scores in both samples, replicating previous results. These findings indicate that the DI is a valid measure for use in a sample from another cultural setting, which is potentially useful to both researchers and practitioners. (shrink)

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experienced-based choice is the “sampling paradigm”, which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of (...) explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical manuscript, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for decisions from experience. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. (shrink)

Climate scientists often apply statistical tools to a set of different estimates generated by an “ensemble” of models. In this paper, I argue that the resulting inferences are justified in the same way as any other statistical inference: what must be demonstrated is that the statistical model that licenses the inferences accurately represents the probabilistic relationship between data and target. This view of statistical practice is appropriately termed “model-based,” and I examine the use of statistics in climate fingerprinting to (...) show how the difficulties that climate scientists encounter in applying statistics to ensemble-generated data are the practical difficulties of normal statistical practice. The upshot is that whether the application of statistics to ensemble-generated data yields trustworthy results should be expected to vary from case to case. (shrink)

Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sam- pling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and (...) CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks. (shrink)

Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in (...) existing studies. More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation. (shrink)

Six recently discussed problems in discrete probabilistic sample space, which have been found puzzling and even paradoxical, are reexamined. The importance is stressed of a sharp distinction between the formalization of mathematical problems and their formal solution that, applied to probability theory, must lead through the explicit partitioning of a sample space. If this approach is consistently followed, such problems reveal themselves to be either inherently ambiguous, and therefore without solution, or quite straightforward. In both cases nothing remains of (...) any sense of paradox. (shrink)

In this article, an attempt was made to identify the level of community social participation according to age, gender and the structural characteristics of the personal support networks in a population displaced by floods in the Colombian Caribbean. The research was based in a non-experimental methodology with an associative-relational strategy. An intentional non-probabilistic sample of 151 people affected by the winter wave in the south of the Department of Atlántico (Colombia) was selected. In total, the study included 42 males (...) (27.8 %) and 109 females (72.2 %) participants, with an average age of 37.48 (±14, Range 18 - 80) and average relocation time of 21.79 months (± 8.22, Range 5-36). The Arizona Social Support Interview Schedule (ASSIS) and Community Empowerment instruments were responded to with an instrument adapted from the leadership competence factor. The results show lower rates of intermediation in the older population, and the relationship between social participation and gender shows equally cohesive social support networks in men and women. This evidence is discussed to promote psychosocial interventions aimed to increase community engagement and empowerment of people that have experienced non-voluntary mobility processes. (shrink)

The main objective of this article is to analyze the cognitive level of the activities in History textbooks in Spain, England, and Portugal in the transition stage from Primary to Secondary Education (11–13 years), according to the country of origin, typology, and the concepts and disciplinary contents included. The design of this research is quantitative, descriptive, and cross-sectional. The non-probabilistic sample consists of 6,561 activities contained in 27 school textbooks from Spain, England, and Portugal. Descriptive and contrast analyses have (...) been carried out using parametric tests. The results indicate that textbooks from Spain and Portugal mainly include activities situated between a basic and intermediate cognitive level while in England, the cognitive level of activities is medium or high. The ANOVA and TukeyBtests show significant differences between the cognitive level required in the activities and the typology of exercises, the concepts, and historical contents worked on. The activities with higher cognitive level correspond to those of creation and essays, the exercises that work on empathy and historical relevance, and that contain activities of social and economic history. In contrast, the activities with the lowest cognitive level are short questions and objective tests, those that work on first-order concepts (data and concrete facts), and those on the History of Art. The conclusion is that there is a need for a balanced presence of first-order content and historical thinking skills, the application in the classroom of a more active student-centered methodology, and the teachers’ conception of history teaching that prioritizes historical skills. (shrink)

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government-owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, the quality of open government data (...) needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision-making and management. In fact, we are dealing with decision-making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high-degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision-making technique including the DEMATEL technique have been used to express the solutions and identify the cause-and-effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up-to-datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision-making and proper management in the complex system of citizens and government organizations. (shrink)

The objective of the study was to translate and validate the COVID Stress Scales into Spanish in Peru. Around 1,424 people, selected through a non-probabilistic sampling, participated in the study. Factor analysis confirmed an initial six-dimensional factorial structure of the CSS-36. Reliability by internal consistency was good for the dimensions of fear of danger, socioeconomic consequences, xenophobia, fear of contamination, traumatic stress, and compulsive control. In addition, the factorial structure of scale has been shown be strictly invariant for (...) both males and females. The Spanish version of the CSS-36 has evidence of validity, reliability, and invariance to measure COVID-19 stress in a Peruvian sample. (shrink)

AimThe current study attempted to assess the effect of positive emotion on post-graduate students’ psychological capital as well as on their academic engagement behavior. Also, the direct relationship between PsyCap and academic engagement behavior was assessed alongside the presence of Stress as a moderating variable between PsyCap and academic engagement behavior amongst post-graduate students in Malaysia.Materials and MethodsA self-administered questionnaire was used for data collection from 373 post-graduate Ph.D. students registered in various universities throughout Malaysia with a non-probabilistic (...) class='Hi'>sampling technique. Research respondents belonged to management, humanities, engineering, computer science, and health sciences domains, and they responded through a questionnaire copy. Statistically, structural equation modeling was applied to evaluate confirmatory factor analysis, reliability analysis, validity analysis, measurement model, structural model, and path analysis. Furthermore, the bootstrapping approach was utilized to test the final model.ResultsFor the hypothesized model, our results confirmed that positive emotions had a positive and significant effect on students’ psychological capita as well as on their academic engagement behavior. Further, PsyCap also had a positive and significant effect on academic engagement behavior. Our results also reported that stress as a moderating variable has a negative and deteriorating effect on the relationship between PsyCap and the academic engagement of students.ConclusionThe study’s findings support the theoretical assumption that positive emotions help individuals generate cognitive resources, which in turn help them manage their engagement behavioral requirements. However, the stress caused by their study needs may deplete their psychological resources, consequently influencing their academic engagement behavior. Interventions like personal coaching/counseling, appropriate follow-up, and flexible goal settings with other measures may help post-graduate students in achieving their daunting tasks. (shrink)

The COVID-19 pandemic has driven organizations to implement various flexible work arrangements. Due to a lack of longitudinal studies, there is currently no consensus in specialized literature regarding the consequences of flexible work arrangements on employee mental health, as well any long term potential impacts. Using the Job Demand-Resource Model, this study documents consequences of the implementation of two types of flexible work arrangement: work schedule flexibility and teleworking on employee mental health over time, and the mediating role played by (...) work overload during the accelerated implementation of flexible work arrangements in the course of the COVID-19 pandemic. Using a longitudinal design and probabilistic sampling, 209 workers participated in this study, twice answering a flexible work arrangement and mental health questionnaire during the pandemic. Findings of this moderated-mediation suggest that work schedule flexibility generates positive effects on mental health over time due to decreased work overload, but only for employees not working from home. These results offer theoretical and practical implications applicable to organizations considering implementation of flexible work arrangements, particularly with regard to how these flexible practices could support a balance between demand and resources, their impact on work overload, and employee mental health over time. (shrink)

It is shown how all the major conceptual difficulties of standard (textbook) quantum mechanics, including the two measurement problems and the (supposed) nonlocality that conflicts with special relativity, are resolved in the consistent or decoherent histories interpretation of quantum mechanics by using a modified form of quantum logic to discuss quantum properties (subspaces of the quantum Hilbert space), and treating quantum time development as a stochastic process. The histories approach in turn gives rise to some conceptual difficulties, in particular the (...) correct choice of a framework (probabilistic sample space) or family of histories, and these are discussed. The central issue is that the principle of unicity, the idea that there is a unique single true description of the world, is incompatible with our current understanding of quantum mechanics. (shrink)

The rise of digital networks consolidates a new type of religious activism that increases the so-called conservative wave in the Brazilian context. These are new dynamics that support to saturate the boundary between the spheres of religious and politics, a key characteristic of the orthodox ideal of secularization. In this paper, we examine the role of conservative evangelical digital activism in the 2018 presidential election, characterized by the open engagement of churches and religious in the electoral dispute, mobilizing their networks (...) of believers, especially around a moral agenda. The research is based on the combination of the use of available data extracted from secondary sources and the exploratory study on the networks based on a non-probabilistic sample obtained through intentional sampling procedures. It was observed that evangelical electoral engagement in networks is the result of the consolidation of conservative counter-secularization movements. In a context of pluralism, digital networks allow, paradoxically, in addition to giving voice and visibility to many minorities, a reaction to these progressive advances, with a direct impact on the victory of Jair Bolsonaro. (shrink)

In the context of reproductive health, policies focused on decriminalising abortion that resulted in religious beliefs, attitudes and behaviours being affected. The main purpose of this article was to identify the religious beliefs of abortion in the emergency situations such as COVID-19. Although there is no general consensus regarding abortion, there is almost ‘general opposition to causing harm to life’ in most religions. In the current study, 28 indicators and four factors (seven for each factor) related to pregnancy termination were (...) explored through an exploratory factor structure. The study was therefore non-experimental, cross-sectional and exploratory, with 100 students selected in a non-probabilistic way. Contribution: The main contribution of this research is to find the variable effects on abortion in emergency conditions regarding religious beliefs. Given the results, behavioural intentions determine a structural model, but religious beliefs explain the solution. Furthermore, data generalisation is not possible due to the context, sample selection and type of analysis. In the local population, a confirmatory factor analysis should be performed with a probabilistic sample selection. (shrink)

Evaluating the Knowledge and Practice of Limitation of Therapeutic Effort in Health Workers at a Health Care Institution for Cancer Patients in Medellín, 2018Avaliação do conhecimento e da prática da limitação de esforço terapêutico em equipe de atendimento de uma instituição prestadora de serviços de saúde para pacientes oncológicos de Medellín, 2018Limitation of therapeutic effort is any action that involves suspending or not initiating medical treatment or therapeutic measures in patients who will not receive any clinical benefit. In Latin America, (...) some research has proven a lack of familiarization and disagreement with the subject and its practices. The present study tried to objectify the knowledge of LTE of healthcare personnel at an institute in Medellín using a self-administered survey that allowed conducting a cross-sectional descriptive study with non-probabilistic sampling. Within the results, 56.5 % of respondents understood the concept of LTE, and 90 % applied it actively. However, there were discrepancies regarding the core concepts of the definition and practice.Para citar este artículo / To reference this article / Para citar este artigoVallejo LI, Palacio AM, Marulanda V, Restrepo A, Yepes LA, Valencia NL, Cruz MA. Evaluación del conocimiento y práctica de la limitación del esfuerzo terapéutico en personal asistencial de una institución prestadora de servicios de salud para pacientes oncológicos de Medellín, 2018. 2020;24:177-87. DOI: https://doi.org/10.5294/pebi.2020.24.2.5Recibido: 19/07/2019Aceptado: 08/08/2020Publicado: 27/11/2020. (shrink)

The increasing challenges faced by organizations have led to numerous studies examining human resource management (HRM) practices, organizational ethical climates and sustainability. Despite this, little has been done to explore the possible relationships between these three topics. This study, based on a probabilistic sample of 6,000 employees from six European countries, analyses how HRM practices with the aim of developing organizational ethics influence the benevolent, principled and egoistic ethical climates that exist within organizations, while also investigating the possible moderating (...) role played by their employees’ perception of corporate sustainability. Findings demonstrate that ability-enhancing practices (i.e. recruiting, selection and training) and opportunity-enhancing practices (i.e. job design, industrial relationships and employee involvement) improve benevolent and principled organizational ethical climates, while motivation-enhancing practices (i.e. performance management, compensation and incentives) rather than being related to these organizational ethical climates, are linked to the egoistic climate. In addition, the perceptions of the company’s employees in terms of corporate sustainability moderate these relationships, by reinforcing the positive relationships of ability-enhancing and motivation-enhancing HRM practices in terms of benevolent and principled ethical climates and by reducing the positive relationships between motivation-enhancing practices and egoistic climate. Specific implications for HRM research, teaching and practice are then advanced and discussed. (shrink)

A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process employed (...) a value function that assumed a linear relationship between reward and sensory stimuli. Linearity is common in reinforcement learning in neuroscience. However, linearity can be problematic because neural mechanisms and the dependence of reward on sensory stimuli were typically nonlinear. Here, we analyze the learning performance with models including optimal nonlinear value functions. We also compare updating the free parameters of the value functions with the delta rule, which neuroscience models use frequently, vs. updating with a new Phi rule that considers the structure of the nonlinearities. Our computer simulations showed that optimal nonlinear value functions resulted in improvements of learning errors when the reward models were nonlinear. Similarly, the new Phi rule led to improvements in these errors. These improvements were accompanied by the straightening of the trajectories of the vector of free parameters in its phase space. This straightening meant that the process became more efficient in learning the prediction of reward. Surprisingly, however, this improved efficiency had a complex relationship with the rate of learning. Finally, the stochasticity arising from the probabilistic sampling of sensory stimuli, rewards, and motivations helped the learning process narrow the range of free parameters to nearly optimal outcomes. Therefore, we suggest that value functions and update rules optimized for social and ecological constraints are ideal for learning aesthetic biases. (shrink)

The pandemic motivated nutrition and healthy living to maintain a healthy lifestyle. The objective is to analyze and describe the perspectives of post-pandemic lifestyles of university students, using the quantitative cross-sectional method with non-probabilistic sampling and survey that measures the nutritional level and level of physical activities, 1033 university students participated. The results showed that the measurement instrument in the Peruvian context is acceptable and that the level of lifestyle is moderate. In conclusion, nutritional policies and physical activities (...) should be considered in universities so that university students practice a healthy lifestyle and take care of their health. (shrink)

Se realizó un estudio descriptivo para evaluar la influencia de la rehabilitación protésica en la calidad de vida de los pacientes desdentados totales tratados en las clínicas estomatológicas docentes "Ismael Clark Mascaró" y "La Vigía" en Camagüey, desde septiembre del 2009 a septiembre del 2013. El universo estuvo comprendido por 254 pacientes desdentados totales rehabilitados y una muestra de 43, a través de un muestreo probabilístico aleatorio. Se obtuvo un predominio del grupo de 50-69 años y el sexo femenino. Al (...) determinar los problemas mecánicos se apreció que los flancos sobreextendidos, seguido de las inserciones frénicas sin liberar y la presencia de puntos prematuros de contacto, fueron las principales dificultades encontradas en los pacientes portadores de prótesis totales. La rehabilitación influyó en la calidad de vida de estos pacientes. It was carried out a descriptive study with the objective to evaluate the influence of prosthetic rehabilitation in total toothless patients life quality treated in the teaching stomatological clinics ¨Ismael Clark Mascaró" and "La Vigía" in Camagüey, from September 2009 to September 2013, with a universe of study composed by 254 total toothless patients who were rehabilitated. The sample was of 43, through a random probabilistic sampling. There was a predominance of the female group aged from 50-69 years old, followed by the female group aged 70. While determining the mechanical problems it was appreciated that the flanks overextended, followed of phrenic insertions without being sustained and the presence of premature points of contact, and were the main difficulties found in the patient's carrier of total prostheses. Prosthetic rehabilitation influenced in the patients´ quality life. (shrink)

Much research on cognitive development focuses either on early-emerging domain-specific knowledge or domain-general learning mechanisms. However, little research examines how these sources of knowledge interact. Previous research suggests that young infants can make inferences from samples to populations (Xu & Garcia, 2008) and 11- to 12.5-month-old infants can integrate psychological and physical knowledge in probabilistic reasoning (Teglas, Girotto, Gonzalez, & Bonatti, 2007; Xu & Denison, 2009). Here, we ask whether infants can integrate a physical constraint of immobility into a (...) statistical inference mechanism. Results from three experiments suggest that, first, infants were able to use domain-specific knowledge to override statistical information, reasoning that sometimes a physical constraint is more informative than probabilistic information. Second, we provide the first evidence that infants are capable of applying domain-specific knowledge in probabilistic reasoning by using a physical constraint to exclude one set of objects while computing probabilities over the remaining sets. (shrink)

Genetic Programming is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces a number of possible symbols at (...) each node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. Computational experiments on two subjects demonstrate that our new PMBGP is much superior to prior probabilistic models. (shrink)

Scoring rules measure the deviation between a forecast, which assigns degrees of confidence to various events, and reality. Strictly proper scoring rules have the property that for any forecast, the mathematical expectation of the score of a forecast p by the lights of p is strictly better than the mathematical expectation of any other forecast q by the lights of p. Forecasts need not satisfy the axioms of the probability calculus, but Predd et al. [9] have shown that given a (...) finite sample space and any strictly proper additive and continuous scoring rule, the score for any forecast that does not satisfy the axioms of probability is strictly dominated by the score for some probabilistically consistent forecast. Recently, this result has been extended to non-additive continuous scoring rules. In this paper, a condition weaker than continuity is given that suffices for the result, and the condition is proved to be optimal. (shrink)

J. Richard Gott III (1993) has used the “Copernican principle” to derive a probability density function for the total longevity of any phenomenon, based solely on the phenomenon’s past longevity. John Leslie (1996) and others have used an apparently similar probabilistic argument, the “Doomsday Argument,” to claim that conventional predictions of longevity must be adjusted, based on Bayes’ Theorem, in favor of shorter longevities. Here I show that Gott’s arguments are flawed and contradictory, but that one of his conclusions—his (...) delta t formula—is mathematically equivalent to Laplace’s famous (and notorious) ‘rule of succession’; moreover, Gott’s delta t formula is a plausible worst-case (if one favors greater longevity) bound in some contexts. On the other hand, the Doomsday Argument is fallacious: the argument’s Bayesian formalism is stated in terms of total duration, but all attempted real-life applications of the argument—with one exception, an application by Gott 1994—actually plug in prior probabilities for future duration; moreover, the Self-Sampling Assumption, an essential premise of the Doomsday Argument, is contradicted by the prior information in all known real-life cases. But rejecting the Doomsday Argument does not entail rejecting the possibility of learning about the future from the past. Applying the work of Bruce M. Hill (1968, 1988, 1993) and Frank P.A. Coolen (1998, 2006) in the field of non-parametric predictive inference, I propose and defend an alternative methodology for quantifying how past longevity of any phenomenon does provide evidence for future longevity. In so doing, I identify an objective standard by which to choose among counting time intervals, counting population, or counting any other measure of past longevity in predicting future longevity. This methodology forms the basis of a calculus of induction. (shrink)

A probabilistic logic of induction is unable to separate cleanly neutral support from disfavoring evidence (or ignorance from disbelief). Thus, the use of probabilistic representations may introduce spurious results stemming from its expressive inadequacy. That such spurious results arise in the Bayesian “doomsday argument” is shown by a reanalysis that employs fragments of an inductive logic able to represent evidential neutrality. Further, the improper introduction of inductive probabilities is illustrated with the “self-sampling assumption.”.

If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

The present paper examined relationships between schizotypy (measured by the Oxford-Liverpool Inventory of Feelings and Experience; O-LIFE scale brief), belief in the paranormal (assessed via the Revised Paranormal Belief Scale; RPBS) and proneness to statistical bias (i.e., perception of randomness and susceptibility to conjunction fallacy). Participants were 254 volunteers recruited via convenience sampling. Probabilistic reasoning problems appeared framed within both standard and paranormal contexts. Analysis revealed positive correlations between the Unusual Experience (UnExp) subscale of O-LIFE and paranormal belief (...) measures [RPBS full scale, traditional paranormal beliefs (TPB) and new age philosophy]. Performance on standard problems correlated negatively with UnExp and belief in the paranormal (particularly the TPB dimension of the RPBS). Consideration of specific problem types revealed that perception of randomness associated more strongly with belief in the paranormal than conjunction; both problem types related similarly to UnExp. Structural equation modeling specified that belief in the paranormal mediated the indirect relationship between UnExp and statistical bias. For problems presented in a paranormal context a framing effect occurred. Whilst UnExp correlated positively with conjunction proneness (controlling for perception of randomness), there was no association between UnExp and perception of randomness (controlling for conjunction). (shrink)

Noise in behavior is often viewed as a nuisance: while the mind aims to take the best possible action, it is let down by unreliability in the sensory and response systems. How researchers study cognition reflects this viewpoint – averaging over trials and participants to discover the deterministic relationships between experimental manipulations and their behavioral consequences, with noise represented as additive, often Gaussian, and independent. Yet a careful look at behavioral noise reveals rich structure that defies easy explanation. First, both (...) perceptual and preferential judgments show that sensory and response noise may potentially only play minor roles, with most noise arising in the cognitive computations. Second, the functional form of the noise is both non-Gaussian and non-independent, with the distribution of noise being better characterized as heavy-tailed and as having substantial long-range autocorrelations. It is possible that this structure results from brains that are, for some reason, bedeviled by a fundamental design flaw, albeit one with intriguingly distinctive characteristics. Alternatively, noise might not be a bug but a feature: indeed, we suggest that noise is fundamental to how cognition works. Specifically, we propose that the brain approximates probabilistic inference with a local sampling algorithm, one that uses randomness to drive its exploration of alternative hypotheses. Reframing cognition in this way explains the rich structure of noise and leads to a surprising conclusion: that noise is not a symptom of cognitive malfunction but plays a central role in underpinning human intelligence. (shrink)

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

It is increasingly common in cognitive science and philosophy of perception to regard perceptual processing as a probabilistic engine, taking into account uncertainty in computing representations of the distal environment. Models of this kind often postulate probabilistic representations, or what we will call probabilistic states,. These are states that in some sense mark or represent information about the probabilities of distal conditions. It has also been argued that perceptual experience itself in some sense represents uncertainty (Morrison _Analytic (...) Philosophy_ 57 (1): 15 48, 2016 ). In this article, we will first consider three models of sensory activity from perceptual neuroscience, namely signal detection theory (SDT), probabilistic population codes (PPC), and sampling. We will then reflect on the sense in which the probabilistic states introduced in these models are probabilistic representations. To sharpen this discussion, we will compare and contrast these probabilistic states to credences as they are understood in epistemology. We will suggest that probabilistic representation, in an appropriately robust sense, can be understood as a form of analog representation. In the last part of the paper, we apply this to the issue of whether conscious experience represents uncertainty—we will interpret this as the claim that there are phenomenal features of experience that serve as analog probabilistic representations. (shrink)

There is widespread recognition at universities that a proper understanding of science is needed for all undergraduates. Good jobs are increasingly found in fields related to Science, Technology, Engineering, and Medicine, and science now enters almost all aspects of our daily lives. For these reasons, scientific literacy and an understanding of scientific methodology are a foundational part of any undergraduate education. Recipes for Science provides an accessible introduction to the main concepts and methods of scientific reasoning. With the help of (...) an array of contemporary and historical examples, definitions, visual aids, and exercises for active learning, the textbook helps to increase students’ scientific literacy. The first part of the book covers the definitive features of science: naturalism, experimentation, modeling, and the merits and shortcomings of both activities. The second part covers the main forms of inference in science: deductive, inductive, abductive, probabilistic, statistical, and causal. The book concludes with a discussion of explanation, theorizing and theory-change, and the relationship between science and society. The textbook is designed to be adaptable to a wide variety of different kinds of courses. In any of these different uses, the book helps students better navigate our scientific, 21st-century world, and it lays the foundation for more advanced undergraduate coursework in a wide variety of liberal arts and science courses. Selling Points Helps students develop scientific literacy—an essential aspect of _any_ undergraduate education in the 21 st century, including a broad understanding of scientific reasoning, methods, and concepts Written for all beginning college students: preparing science majors for more focused work in particular science; introducing the humanities’ investigations of science; and helping non-science majors become more sophisticated consumers of scientific information Provides an abundance of both contemporary and historical examples Covers reasoning strategies and norms applicable in all fields of physical, life, and social sciences, _as well as_ strategies and norms distinctive of specific sciences Includes visual aids to clarify and illustrate ideas Provides text boxes with related topics and helpful definitions of key terms, and includes a final Glossary with all key terms Includes Exercises for Active Learning at the end of each chapter, which will ensure full student engagement and mastery of the information include earlier in the chapter Provides annotated ‘For Further Reading’ sections at the end of each chapter, guiding students to the best primary and secondary sources available Offers a Companion Website, with: For Students: direct links to many of the primary sources discussed in the text, student self-check assessments, a bank of exam questions, and ideas for extended out-of-class projects For Instructors: a password-protected Teacher’s Manual, which provides student exam questions with answers, extensive lecture notes, classroom-ready Power Point presentations, and sample syllabi Extensive Curricular Development materials, helping any instructor who needs to create a Scientific Reasoning Course, ex nihilo. (shrink)