The causal structure of Darwinian evolution by natural selection is investigated. Its basic scheme is reproduction resulting from a feedback loop driven by internal and external causes. Causation internal to the loop connects genotype, development, phenotype, and fitness, with environmental constraints on the latter preventing runaway reproduction. External causes driving the core loop are environmental change and genetic change. This basic causal structure is complicated by modern additions such as control of mutation rate, niche construction, interactions between evolution and development, (...) and epigenetic effects. In this article it is argued that at least one of these additions, control of mutation rate, produces an additional feedback loop incorporating a continual, targeted modulation of stochasticity, and thereby produces an unusual form of causation. This form of causation entangles deterministic and stochastic causation in such a way that it imposes, in effect, an active, goal-driven role on the organisms in a line of descent. The goal is defined by the factor driving the stochasticity, which must be regarded as an intrinsic estimate made implicitly by the organism of its own fitness. The estimator itself is an evolved property of the organism, and is aligned with the apparent goal-directedness of natural selection. (shrink)

The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as (...) those based on the maximum likelihood solution, e.g., output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters of underidentified models, as we illustrate on a simple errors-in-variables model. (shrink)

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when (...) using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development. (shrink)

This paper separates conditional parameter estima- tion, which consistently raises test set accuracy on statistical NLP tasks, from conditional model struc- tures, such as the conditional Markov model used for maximum-entropy tagging, which tend to lower accuracy. Error analysis on part-of-speech tagging shows that the actual tagging errors made by the conditionally structured model derive not only from label bias, but also from other ways in which the independence assumptions of the conditional model structure are unsuited to linguistic sequences. The (...) paper presents new word-sense disambiguation and POS tagging experiments, and integrates apparently conflicting reports from other recent work. (shrink)

Characterizing the genetic structure of populations is of importance to evolutionary biology, to human disease gene mapping, and to forensic science. Sewall Wright introduced a set of F-statistics to describe population structure in 1951, and he emphasized that these quantities were ratios of variances. Responding to uncertainty over the best way to estimate F-statistics, Weir and Cockerham published a method-of-moments set of estimators in 1984. This paper continues to be widely cited, with over 7,000 citations to date. Some background to (...) the publishing history of the Weir and Cockerham paper is given here, along with subsequent developments and a discussion of current uses of Wright's F-statistics. (shrink)

Despite their importance in public discourse, numbers in the range of 1 million to 1 trillion are notoriously difficult to understand. We examine magnitude estimation by adult Americans when placing large numbers on a number line and when qualitatively evaluating descriptions of imaginary geopolitical scenarios. Prior theoretical conceptions predict a log-to-linear shift: People will either place numbers linearly or will place numbers according to a compressive logarithmic or power-shaped function (Barth & Paladino, ; Siegler & Opfer, ). While about (...) half of people did estimate numbers linearly over this range, nearly all the remaining participants placed 1 million approximately halfway between 1 thousand and 1 billion, but placed numbers linearly across each half, as though they believed that the number words “thousand, million, billion, trillion” constitute a uniformly spaced count list. Participants in this group also tended to be optimistic in evaluations of largely ineffective political strategies, relative to linear number-line placers. The results indicate that the surface structure of number words can heavily influence processes for dealing with numbers in this range, and it can amplify the possibility that analogous surface regularities are partially responsible for parallel phenomena in children. In addition, these results have direct implications for lawmakers and scientists hoping to communicate effectively with the public. (shrink)

Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precisions. To investigate the extent of this variability, we consider the simplest non-trivial non-linear model of a v-structure (...) on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading-order asymptotics, we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result that is not captured by the recent leading-order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size and cannot rely on purely graphical criteria. (shrink)

We present a new approach to representation and acquisition of normative information for machine ethics. It combines an influential philosophical account of the fundamental structure of morality with argumentation theory and machine learning. According to the philosophical account, the deontic status of an action – whether it is required, forbidden, or permissible – is determined through the interaction of “normative reasons” of varying strengths or weights. We first provide a formal characterization of this account, by modeling it in(weighted) argumentation graphs. (...) We then use it to model ethical learning: the basic idea is to use a set of cases for which deontic statuses are known to estimate the weights of normative reasons in operation in these cases, and to use these weight estimates to determine the deontic statuses of actions in new cases. The result is an approach that has the advantages of both bottom-up and top-down approaches to machine ethics: normative information is acquired through the inter-action with training data, and its meaning is clear. We also report the results of some initial experiments with the model. (shrink)

The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on (...) inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation. (shrink)

The inbreeding coefficient of a population, estimated from ecclesiastical Roman Catholic dispensations, results from the relative contribution of different degrees of relationships (uncle–niece, first cousin, etc.). The interpopulation comparisons of consanguinity patterns may be obscured by the fact that in 1918 the Roman Catholic Church norm regulating the closest marriageable kinship was modified, limiting the application for an ecclesiastical dispensation to relatives of third degree (second cousins) or closer. Depending on the length of the period before or after the change (...) of regulation, coefficients and rates may differ. Deviation of frequencies for multiple marriages may also occur. The aim of the present paper is to determine how the chosen procedure based on ecclesiastical dispensations may affect results, regarding the inbreeding coefficient, the consanguinity rate, the structure of consanguinity and the close/remote kinship ratio. As a sample case, information from the Gredos mountain range (central Spain) has been used. (shrink)

Suspended sediment modeling is an important subject for decision-makers at the catchment level. Accurate and reliable modeling of suspended sediment load is important for planning, managing, and designing of water resource structures and river systems. The objective of this study was to develop artificial intelligence- based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI-based models, that is, support vector machine, adaptive neurofuzzy inference system, feed-forward neural network, and one conventional multilinear regression modes, were (...) used for SSL modeling. Besides, four different ensemble methods, neural network ensemble, ANFIS ensemble, weighted average ensemble, and simple average ensemble, were developed by combining the outputs of the four single models to improve their predictive performance. The study used two-year discharge and SSL data for training and verification of the applied models. Determination coefficient and root mean square error were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI-based ensemble in particular provide excellent performance in SSL estimation. (shrink)

(a) Learn a grammar GA for the source language (A). (b) Estimate a structural statistical language model SSLMA for (A). Given a grammar (consisting of terminals and nonterminals) and a partial sentence (sequence of terminals (t1 . . . ti)), an SSLM assigns probabilities to the possible choices of the next terminal ti+1.

BackgroundThe amount and value of researchers’ peer review work is critical for academia and journal publishing. However, this labor is under-recognized, its magnitude is unknown, and alternative ways of organizing peer review labor are rarely considered.MethodsUsing publicly available data, we provide an estimate of researchers’ time and the salary-based contribution to the journal peer review system.ResultsWe found that the total time reviewers globally worked on peer reviews was over 100 million hours in 2020, equivalent to over 15 thousand years. The (...) estimated monetary value of the time US-based reviewers spent on reviews was over 1.5 billion USD in 2020. For China-based reviewers, the estimate is over 600 million USD, and for UK-based, close to 400 million USD.ConclusionsBy design, our results are very likely to be under-estimates as they reflect only a portion of the total number of journals worldwide. The numbers highlight the enormous amount of work and time that researchers provide to the publication system, and the importance of considering alternative ways of structuring, and paying for, peer review. We foster this process by discussing some alternative models that aim to boost the benefits of peer review, thus improving its cost-benefit ratio. (shrink)

Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. Subsequently, Kalisch and B¨uhlmann (...) [J. Mach. Learn. Res. 8 613–636] described a uniformly consistent estimator of the Markov equivalence class of a linear Gaussian causal structure under the Causal Markov and Strong Causal Faithfulness assumptions. However, the Strong Faithfulness assumption may be false with high probability in many domains. We describe a uniformly consistent estimator of both the Markov equivalence class of a linear Gaussian causal structure and the identifiable structural coefficients in the Markov equivalence class under the Causal Markov assumption and the considerably weaker k-Triangle-Faithfulness assumption. (shrink)

Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimators for the mean response to a dynamic treatment regime may be conceived through the augmented inverse probability weighted (AIPW) estimating function, defined as the sum of the inverse probability weighted (IPW) estimating function and an augmentation term. The IPW estimating function of the causal estimand (...) via marginal structural model is defined as the complete-case score function for those subjects whose treatment sequence is consistent with the dynamic regime in question divided by the probability of observing the treatment sequence given the subject's treatment and covariate histories. The augmentation term is derived by projecting the IPW estimating function onto the nuisance tangent space and has mean-zero under the truth. The IPW estimator of the causal estimand is consistent if (i) the treatment assignment mechanism is correctly modeled and the AIPW estimator is consistent if either (i) is true or (ii) nested functions of intermediate and final outcomes are correctly modeled.Hence, the AIPW estimator is doubly robust and, moreover, the AIPW is semiparametric efficient if both (i) and (ii) are true simultaneously. Unfortunately, DR estimators can be inferior when either (i) or (ii) is true and the other false. In this case, the misspecified parts of the model can have a detrimental effect on the variance of the DR estimator. We propose an improved DR estimator of causal estimand in dynamic treatment regimes through a technique originally developed by [4] which aims to mitigate the ill-effects of model misspecification through a constrained optimization.In addition to solving a doubly robust system of equations, the improved DR estimator simultaneously minimizes the asymptotic variance of the estimator under a correctly specified treatment assignment mechanism but misspecification of intermediate and final outcome models. We illustrate the desirable operating characteristics of the estimator through Monte Carlo studies and apply the methods to data from a randomized study of integrilin therapy for patients undergoing coronary stent implantation. The methods proposed here are new and may be used to further improve personalized medicine, in general. (shrink)

How to estimate the randomness in random sequence generation tasks? The aim of the paper was to discuss the accuracy of the multiple indexes used for random sequences generation results calculation. In the first part of the paper the models explaining deviations from randomness were presented. The key role of the structural limitations interpretation was suggested. Secondly, the multiple indexes of the deviation from randomness used in random sequence generation task studies were presented. The authors concluded that too many (...) indexes are used in the studies of deviation from randomness. In order to avoid such problems two indexes were proposed: entropy and correlation function. The last part of the paper presents the preliminary version of the mathematical of random sequences generation in which the limited capacity of the short-term memory assumption was introduce. (shrink)

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCA_M, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite (...) of indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCA_M and existing methods. These methods are also applied to fit a substantively well-established model to real data. (shrink)

This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of (...) porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation. (shrink)

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are (...) used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent. (shrink)

To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad‐coverage tools from natural‐language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context‐free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better (...) model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next‐word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure‐building predominantly in the left posterior temporal lobe: CCG‐derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure‐building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds. (shrink)

I explored the association between components constituting the basis for moral and optimal human functioning, i.e., moral reasoning, moral identity, empathy, and purpose, via network analysis. I employed factor scores instead of composite scores that most previous studies used for better accuracy in score estimation in this study. Then, I estimated the network structure among collected variables and centrality indicators. For additional information, the structure and indicators were compared between two groups, participants who engaged in civic activities highly versus (...) lowly. The results demonstrated significant intra- and inter-scale associations in the network. The network structure was invariant across the two groups. Despite the network invariance, I found that the global connectivity between and centrality indicators of examined factors were higher among the high civic engagement group in general. I discussed the implications of the findings in research on moral functioning based on moral psychology and virtue ethics. (shrink)

A computer-based age estimation is a technique that predicts an individual's age based on visual traits derived by analyzing a 2D picture of the individual's face. Age estimation is critical for access control, e-government, and effective human–computer interaction. The other-race effect has the potential to cause techniques designed for white faces to underperform when used in a region with black faces. The outcome is the consequence of intermittent training with faces of the same race and the encoding structure (...) of the trained face images, which is based on the feature extraction technique used. This study contributes to a constructive comparison of three feature-extraction techniques, namely, local binary pattern, Gabor Wavelet, and wavelet transformation, used in the development of a genetic algorithm -artificial neural network -based age estimation system. The feature extraction techniques used are proven to produce a wealth of shape and textural information. The GA-ANN constitutes the age classifier module. The correct classification rate was chosen as the performance metrics in this study. The results demonstrated that the LBP is a more robust representation of the black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and Wavelet transformation age estimation systems, respectively. (shrink)

Meditation comprises a series of practices mainly developed in eastern cultures aiming at controlling emotions and enhancing attentional processes. Several authors proposed to divide meditation techniques in focused attention and open monitoring techniques. Previous studies have reported differences in brain networks underlying FA and OM. On the other hand common activations across different meditative practices have been reported. Despite differences between forms of meditation and their underlying cognitive processes, we propose that all meditative techniques could share a central process that (...) would be supported by a core network for meditation since their general common goal is to induce relaxation, regulating attention and developing an attitude of detachment from one’s own thoughts. To test this hypothesis, we conducted a quantitative meta-analysis based on activation likelihood estimation of 10 neuroimaging studies on different meditative techniques to evidence the core cortical network subserving meditation. We showed activation of basal ganglia , limbic system and medial prefrontal cortex . We discuss the functional role of these structures in meditation and we tentatively propose a neurocognitive model of meditation that could guide future research. (shrink)

This study considers two problems: the extent to which feminist opinions are distinct from feminist identity and the generalizability of these separate constructs across gender and time. Using pooled cross-sectional data from the six National Election Study surveys conducted from 1972 through 1992, the author employs a series of measurement and structural equation models to evaluate the validity and reliability of various feminist orientations and to estimate the relationships among feminist identity, feminist opinion, and individual characteristics associated with feminism. (...) The results indicate that identity and opinion are separate but related constructs. This configuration generalizes across women and men and two decades of American electoral history. (shrink)

Psychiatric diagnostic covariation suggests that the underlying structure of psychopathology is not one of circumscribed disorders. Quantitative modeling of individual differences in diagnostic patterns has uncovered several broad domains of mental disorder liability, of which the Internalizing and Externalizing spectra have garnered the greatest support. These dimensions have generally been estimated from lifetime or past-year comorbidity patters, which are distal from the covariation of symptoms and maladaptive behavior that ebb and flow in daily life. In this study, structural models (...) are applied to daily diary data ( Median = 94 days) of maladaptive behaviors collected from a sample ( N = 101) of individuals diagnosed with personality disorders (PDs). Using multilevel and unified structural equation modeling, between-person, within-person, and person-specific structures were estimated from 16 behaviors that are encompassed by the Internalizing and Externalizing spectra. At the between-person level (i.e., individual differences in average endorsement across days) we found support for a two-factor Internalizing–Externalizing model, which exhibits significant associations with corresponding diagnostic spectra. At the within-person level (i.e., dynamic covariation among daily behavior pooled across individuals) we found support for a more differentiated, four-factor, Negative Affect-Detachment-Hostility-Disinhibition structure. Finally, we demonstrate that the person-specific structures of associations between these four domains are highly idiosyncratic. (shrink)

Le débat actuel sur la pensée structurale soulève diverses questions qui intéressent l'interprétation de la philosophie hégélienne, et particulièrement l'interprétation hégélienne de la psychanalyse.Lucien Sebag estime que l'histoire et la structure représentent deux types d'explications qui se distinguent nettement par leur méthode d'opération tout autant que par les résultats obtenus. On peut conceptualiser, nous dit-il, de deux façons: I) en considérant des conduites individuelles ou collectives dont le compte rendu forme alors un récit. Ce mode de pensée est de type (...) causal. (shrink)

This paper explores Hegel’s perspective on development within nature, his supposed rejection of evolution, and his concept of nature as a “system of stages.” It argues that interpreting Hegel through the lens of emergentist thinking provides a more accurate understanding of his conception of nature and its development, as well as his critique of evolution. The paper is structured in three parts. First, we introduce emergentist theory, exploring its contemporary and historical meanings to establish where Hegel fits within this framework. (...) Second, we carefully examine Hegel’s critique of evolution, particularly his opposition to simple causal chains, and clarify what “evolution” meant in his time. Finally, we argue that viewing Hegel through emergentist theory not only rehabilitates his ideas but also resolves lingering concerns about his understanding of development within nature. This paper aims to open new avenues for interpreting Hegel’s Philosophy of Nature. (shrink)

Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In (...) a human study, we showed that human spatial estimates of the location of cities were very similar regardless of whether participants read Tolkien’s texts or memorized a map of Middle Earth. However, text‐based location estimates obtained from statistical linguistic frequencies better predicted the human text‐based estimates than the human map‐based estimates. These findings suggest that language encodes spatial structure of cities, and that human cognitive map representations can come from implicit statistical linguistic patterns, from explicit non‐linguistic perceptual information, or from both. (shrink)

It is important to reveal how humans evaluate an explanation of the recent development of explainable artificial intelligence. So, what makes people feel that one explanation is more likely than another? In the present study, we examine how explanatory virtues affect the process of estimating subjective posterior probability. Through systematically manipulating two virtues, Simplicity—the number of causes used to explain effects—and Scope—the number of effects predicted by causes—in three different conditions, we clarified two points in Experiment 1: that Scope's effect (...) is greater than Simplicity's; and that these virtues affect the outcome independently. In Experiment 2, we found that instruction about the explanatory structure increased the impact of both virtues' effects but especially that of Simplicity. These results suggest that Scope predominantly affects the estimation of subjective posterior probability, but that, if perspective on the explanatory structure is provided, Simplicity can also affect probability estimation. (shrink)

Debt-ridden corporate growth and increased vulnerability was one of the causes of the 1997 financial crisis in Korea. Introduction of the outside director system has been the core part of the board reforms following the crisis. Our estimation using instruments obtained from a natural experiment illustrates that outside monitoring has improved capital structure of firms even when we control for the leverage regulation effect, enhanced compliance with leverage regulation and thus reduced business risks, and reduced excessive growth and excessive (...) investment more consistently for the top 10 largest chaebols than non-chaebol firms and smaller sized chaebol affiliates. Our results shed some light on why existing studies report the positive effect of outsiders on firm value and add value to existing agency theory by illustrating that the effect of improved governance on capital structure could be non-linear. (shrink)

Structural equation model trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios (...) is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics. These score-based tests monitor fluctuations in case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation. (shrink)

It is well-documented that mathematics achievement is an important predictor of many positive life outcomes like college graduation, career opportunities, salary, and even citizenship. As such, it is important for researchers and educators to help students succeed in mathematics. Although there are undoubtedly many factors that contribute to students' success in mathematics, much of the research and intervention development has focused on variations in instructional techniques. Indeed, even a cursory glance at many educational journals and granting agencies reveals that there (...) is a large amount of time, energy, and resources being spent on determining the best way to convey information through direct, declarative instruction. The proposed project is motivated by recent calls to expand the focus of research in mathematics education beyond direct, declarative instruction. The overarching goal of the presented experiment is to evaluate the efficacy of a novel mathematics intervention designed using principles taken from the literature on non-declarative learning. The intervention combines errorless learning and structured cue fading to help second grade students improve their understanding of symbolic magnitude. Results indicate that students who learned about symbolic magnitude using the novel intervention did better than students who were provided with extensive declarative support. These findings offer preliminary evidence in favor of using learning combination of errorless learning and cue fading techniques in the mathematics classroom. (shrink)

Semantic framework of the discussion about the equivalence and interchangeability of the original drugs and generics is considered in the article. Generics are identical to the original drugs in terms of chemical structure, nevertheless some patients and doctors consider that generics are less effective and have more severe side effects than original drugs. These discussions are considered as an example of a public deliberation concerning the achievements of technoscience. The conflicting parties determined the identity either from the chemical structure of (...) the drug (according to D. Chalmers – secondary intensional) or from the phenomenal characteristics of the situation of its application (primary intensional). In this regard, the method of resolving the conflict is the alignment of the hierarchy of methods for determining equivalence in biomedicine. The methodology of evidence-based medicine already has such a hierarchy, which makes it possible to determine the validity of outcomes of clinical trial. According to this hierarchy, the phenomenal characteristics of the outcome of treatment (quality and life expectancy of patients) are more important than instrumentally established indicators. Thus, new clinical data on the efficacy and safety of a generic should give the opportunity to cancel the recognition of drug equivalence. More generally, this means that technoscience achievement which directly affects a person can only be assessed by him/her. The recognition of such priority is the basis of the ‘division of linguistic labor’ during public deliberation concerning the achievements of technoscience. (shrink)

We propose an alternative resolution of Simpson's paradox in multiple classification experiments, using a different maximum likelihood estimator. In the center of our analysis is a formal representation of free choice and randomization that is based on the notion of incompatible measurements.We first introduce a representation of incompatible measurements as a collection of sets of outcomes. This leads to a natural generalization of Kolmogoroff's axioms of probability. We then discuss the existence and uniqueness of the maximum likelihood estimator for a (...) probability weight on such a generalized sample space, given absolute frequency data.As a first example, we discuss an estimation problem with censured data that classically admits only biased ad hoc estimators.Next, we derive an explicit solution of the maximum likelihood estimation problem for a large class of experiments that arise from various kids of compositions of sample spaces. We identify the (categorical) direct sum of sample spaces as a representation of “free choice,” and the (categorical) direct product as a representation of “randomization.”Finally, we apply the foregoing discussion to the case of multiple classification experiments in order to show that there is no Simpson's paradox if the difference between free choice and randomization is recognized in the structure of the experiment.A comparison between our new estimator and the “usual” calculation can be summarized as follows: Pooling the data over one classification factor in the “usual” way in fact destroys or ignores the information contained in it, whereas our proposed maximum likelihood estimator is a proper marginal over this factor that “averages out” the information contained in it. The estimators agree with each other in the case of proportional sample sizes. (shrink)

Background: Longitudinal studies exploring the complex interplay between family structures and residential mobility on educational achievement and failure are lacking. We investigate the interplay between the number of residential moves during late childhood, parental education level, family living situation, and the probability of completing upper secondary education. Methodology Detailed longitudinal data for a random sample of 30% of the entire Norwegian population born 1982 to 1989 ( N = 121,247) and information on all their relocations between Norwegian enumeration districts from (...) ages 10 to 18 years were extracted from the Norwegian population registries. Family structures were grouped into four intersectional family strata defined by combining categories of parental education level (distinguishing poorly educated and well-educated families) and the family’s living situation (comparing non-intact families with intact families). We applied two-level logistic regression models, which incorporated individual and family contextual factors, to estimate possible differences in completion rates of upper secondary education. Results Non-completion of secondary education (which constitutes 29% of the study sample) increases incrementally with the number of residential changes across all four family structures, but this effect was not distributed evenly between the different family strata. Individuals in “well-educated, intact families” seem to be least affected by residential moves. On the other hand, the highest disadvantage of frequent moves was among adolescents in the stratum “poorly educated, intact families.” In poorly educated families the probabilities of completing secondary school among non-intact and intact families converge toward each other as the number of moves increase. About 43% of the variation in school completion may be attributed to differences between families. The highest risk of school non-completion was found among adolescents in poorly educated families, which accounted for 74% of the non-completers. Conclusion We demonstrated underlying links between residential mobility and family structures on non-completion of upper secondary education. The adverse effect of frequent moves calls for attention in schools, public health agencies, and housing policies. The findings should be considered in a life course perspective, as the accumulation of unfavorable conditions during childhood and adolescence tends to constrict future prospects in terms of health and quality of life. (shrink)

Log-file data from computer-based assessments can provide useful collateral information for estimating student abilities. In turn, this can improve traditional approaches that only consider response accuracy. Based on the amounts of time students spent on 10 mathematics items from the PISA 2012, this study evaluated the overall changes in and measurement precision of ability estimates and explored country-level heterogeneity when combining item responses and time-on-task measurements using a joint framework. Our findings suggest a notable increase in precision with the incorporation (...) of response times and indicate differences between countries in how respondents approached items as well as in their response processes. Results also showed that additional information could be captured through differences in the modeling structure when response times were included. However, such information may not reflect the testing objective. (shrink)

In this paper we consider classes of finite structures where we have good control over the sizes of the definable sets. The motivating example is the class of finite fields: it was shown in [1] that for any formulain the language of rings, there are finitely many pairs (d,μ) ∈ω×Q>0so that in any finite fieldFand for any ā ∈Fmthe size |ø(Fn,ā)| is “approximately”μ|F|d. Essentially this is a generalisation of the classical Lang-Weil estimates from the category of varieties to that of (...) the first-order-definable sets. (shrink)

Working memory is the system responsible for maintaining and manipulating information, in the face of ongoing distraction. In turn, WM span is perceived to be an individual-differences construct reflecting the limited capacity of this system. Recently, however, there has been some evidence to suggest that WM capacity can increase through training, raising the possibility that training can functionally alter the neural structures supporting WM. To address the hypothesis that the neural substrates underlying WM are targeted by training, we conducted a (...) meta-analysis of functional magnetic resonance imaging studies of WM training using Activation Likelihood Estimation. Our results demonstrate that WM training is associated exclusively with decreases in blood oxygenation level-dependent responses in clusters within the fronto-parietal system that underlie WM, including the bilateral inferior parietal lobule, middle and superior frontal gyri, and medial frontal gyrus bordering on the cingulate gyrus. We discuss the various psychological and physiological mechanisms that could be responsible for the observed reductions in the BOLD signal in relation to WM training, and consider their implications for the construct of WM span as a limited resource. (shrink)

This book explores the principal issues involved in bridging the gap between the pure theory of consumer behavior and its empirical implementation. The authors focus upon the structure of preferences, the treatment of demographic variables, the treatment of dynamics, and the specification of the stochastic structure of the demand system.

The difficulty of conducting relevant experiments has long been regarded as the central challenge to learning about the economy from data. The standard solution, going back to Haavelmo's famous “The Probability Approach in Econometrics” (1944), involved two elements: first, it placed substantial weight on a priori theory as a source of structural information, reducing econometric estimates to measurements of causally articulated systems; second, it emphasized the need for an appropriate statistical model of the data. These elements are usually seen (...) as tightly linked. I argue that they are, to a large extent, separable. Careful attention to the role of an empirically justified statistical model in underwriting probability explains puzzles not only in economics, but more generally with respect to recent criticisms of Reichenbach's principle of the common cause, which lies behind graph-theoretic causal search algorithms. And it provides an antidote to the pessimistic understanding of the possibilities for passive observation of causal structure in econometrics and related areas of Nancy Cartwright and others. (shrink)

Marginal structural models can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting can be sensitive to model misspecification and lead to high-variance estimates due to extreme weights. Various methods have been proposed to partially address this, including covariate balancing propensity score to mitigate treatment model misspecification, and truncation and stabilized-IPTW to temper extreme weights. In this article, (...) we present kernel optimal weighting, a convex-optimization-based approach that finds weights for fitting the MSMs that flexibly balance time-dependent confounders while simultaneously penalizing extreme weights, directly addressing the above limitations. We further extend KOW to control for informative censoring. We evaluate the performance of KOW in a simulation study, comparing it with IPTW, sIPTW, and CBPS. We demonstrate the use of KOW in studying the effect of treatment initiation on time-to-death among people living with human immunodeficiency virus and the effect of negative advertising on elections in the United States. (shrink)

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...) strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism. These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally and appear to be domain-general. Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors. (shrink)

Experts have been claimed to perceive risks in a different way than the general public. It is likely that experts often see risks in their own field of expertise as smaller than the public does, but that does not imply that their risk perception is also driven by other factors. Topical experts and general risk assessors are two quite different types of experts. Still, common assertions as to the simple structure of expert risk perception, said to be related only to (...) technical or statistical risk estimates, builds on a meager database consisting of 15 general risk assessors. Results from a study reported here show that the factors explaining experts’ risk perception are similar to those of a comparable group of nontopical experts with a similar level of technological literacy and to the general public, and that the level of explained variance is quite comparable for experts and the general public. (shrink)

Management scholars have paid close attention to the construct of organizational or corporate reputation, particularly in the applied business ethics and corporate social responsibility fields. Extant research demonstrates that CR is one of the key mediators between CSR and important organizational outcomes, which ultimately improve organizational performance. Yet, hitherto the research focused on CR construct has been plagued by multiple definitions, conflicting conceptualizations, and unclear operationalizations. The purpose of this article is to provide theoretical ground for positioning of CR as (...) an assessment construct that is modeled as a second-order factor affecting individual first-order dimensions, and to provide methodological and empirical support toward such conceptualization. We assert that intangible, socially complex, and causally ambiguous CR can be accurately estimated through its individual measurable dimensions. Using survey data from Peru, we empirically test the hypothesized second-order reflective model within a hierarchy of nested and non-nested models, and compare its model fit and predictive power with alternative conceptualizations. Modeling CR as a second-order reflective construct relies on a set of theoretical propositions and yields several methodological advantages, including strong conceptual interpretability and parsimony when tested within a nomological context. We explicitly demonstrate positive organizational outcomes of CR: customer trust, corporate identification, in-role behavior, and extra-role behavior. Then, we demonstrate that the shorter scales of CR can be used as a good proxy for the full construct measure. The paper concludes by highlighting theoretical insights, and methodological and managerial implications of the findings. (shrink)

Decision-making competence refers to the ability to make better decisions, as defined by decision-making principles posited by models of rational choice. The adult decision-making competence (A-DMC) scale is a relatively mature evaluation tool used for decision-making competence. However, the A-DMC is yet far from other mature psychological evaluation tools, and especially the structure of A-DMC remains unclear. In the current study, we estimated a regularized partial correlation network of decision-making competence in a Chinese sample consisting of 339 adults who were (...) evaluated by the A-DMC, and then the centrality indicators were calculated. The results revealed that all nodes of the decision-making competence networks are positively associated, except for the association of resistance to framing (RF) and resistance to sunk costs (SC). The strongest edge was between RF and applying decision rules (DR; regularized partial correlation = 0.37). The centrality indicators of RF and applying DR were highest, revealing that these two variables may play important roles in the decision-making competence network. Our study conceptualizes the decision-making competence from network perspectives, so as to provide some insights for future researches. (shrink)

Multivariate behavioral research often focuses on latent constructs—such as motivation, self-concept, or wellbeing—that cannot be directly observed. Typically, these latent constructs are measured with items in standardized instruments. To test the factorial structure and multidimensionality of latent constructs in educational and psychological research, Morin et al. proposed bifactor exploratory structural equation modeling. This meta-analytic review aimed to estimate the extent to which B-ESEM model fit differs from other model representations, including confirmatory factor analysis, exploratory structural equation modeling, hierarchical (...) CFA, hierarchical ESEM, and bifactor-CFA. The study domains included learning and instruction, motivation and emotion, self and identity, depression and wellbeing, and interpersonal relations. The meta-analyzed fit indices were the χ2/df ratio, the comparative fit index, the Tucker-Lewis index, the root mean square error of approximation, and the standardized root mean squared residual. The findings of this meta-analytic review indicate that the B-ESEM model fit is superior to the fit of reference models. Furthermore, the results suggest that model fit is sensitive to sample size, item number, and the number of specific and general factors in a model. (shrink)

Previous research has demonstrated how emotion resembling cues in the face help shape impression formation. Perhaps most notable in the literature to date, has been work suggesting that gender-related appearance cues are visually confounded with certain stereotypic expressive cues. Only a couple studies to date have used computer vision to directly map out and test facial structural resemblance to emotion expressions using facial landmark coordinates to estimate face shape. In one study using a Bayesian network classifier trained to detect (...) emotional expressions structural resemblance to a specific expression on a non-expressive face was found to influence trait impressions of others. In another study, a connectionist model trained to detect emotional expressions found different emotion-resembling cues in male vs. female faces. Despite this seminal work, direct evidence confirming the theoretical assertion that humans likewise utilize these emotion-resembling cues when forming impressions has been lacking. Across four studies, we replicate and extend these prior findings using new advances in computer vision to examine gender-related, emotion-resembling structure, color, and texture and their impact on gender-stereotypic impression formation. We show that all three are meaningfully related to human impressions of emotionally neutral faces. Further when applying the computer vision algorithms to experimentally manipulate faces, we show that humans derive similar impressions from them as did the computer. (shrink)