Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

Although philosophers have considered some of the implications of the nature of quantum statistics of many-particle systems for the interpretation problem, e.g., Reichenbach, they have not produced a complete analysis of the relationship between aspects of quantum statistics and complications and/or possible solutions of the interpretation problem. While the present work by no means provides a complete account, it does explore some heretofore uncharted regions. One of the latter is an analysis of a situation that I call 'The (...) Paradox of Identical Particles ,' which arises in Bose-Einstein statistics. PIP refers to the fact that Elementary Quantum Mechanics seems to be committed to the thesis that systems of particles of the same species, "identical" particles, exhibit behavior differing from that of systems of particles of different species, simply in virtue of the fact that the former contain entities of the same species while the latter do not. Taken at face value this is tantamount to the Empedoclean "axiom" that like attracts like, while like and unlike repel, a somewhat embarassingly naive proposition for modern physicists to embrace, and one which, furthermore, poses a host of perplexing problems, e.g., how do the particles "know" whether they are of the same species or not? A result proven in the present work is that any "pluralistic" interpretation of quantum theory is unable to provide a satisfactory way of ridding quantum physicists of this appeal to what I call "species sensitivity." Arguing that Quantum Field Theory is most naturally and plausibly interpreted as a "monistic" theory--which means, inter alia, that for a system of n identical particles to exist is not for n distinct objects of any kind to exist, but rather for one unified underlying entity, a quantum field, to be in a certain kind of state or condition--I show that from this point of view PIP can be dissolved. On the basis of this and other considerations, I argue that the interpretation problem is more amenable to solution if we take the QFT point of view as the fundamental expression of quantum theory. (shrink)

Many cosmopolitans link their moral defence of specific principles of justice to a critique of the normative standing of states. This article explores some conceptual distinctions between morality and justice by focusing on the nature of claims they entail, the obligations they generate and the distribution of agency that they require. It then draws out some implications of these distinctions so as to illustrate how states play a non-arbitrary role in the process of both rendering determinate the principles of global (...) justice and allocating agency compatibly with strict rather than large obligations. Contrary to many existent defences of the state, it shows how a similar conception promotes rather than undermining the ideal of global justice. Keywords: cosmopolitanism; statism; equality; justice; morality (Published: 1 September 2010) Citation: Ethics & Global Politics, Vol. 3, No. 3, 2010, pp. 171-192. DOI: 10.3402/egp.v3i3.5487. (shrink)

Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI.Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. (...) Fifty six drug-naive ADHD children and 56 age matched TD controls were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine, Random Forest, and Gradient Boosting using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls. (shrink)

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 (...) months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. (shrink)

We propose a solution concept for games that are played among players with present-biased preferences that are possibly naive about their own, or about their opponent’s future time inconsistency. Our perception-perfect outcome essentially requires each player to take an action consistent with the subgame perfect equilibrium, given her perceptions concerning future types, and under the assumption that other present and future players have the same perceptions. Applications include a common pool problem and Rubinstein bargaining. When players are naive (...) about their own time inconsistency and sophisticated about their opponent’s, the common pool problem is exacerbated, and Rubinstein bargaining breaks down completely. (shrink)

Individual choices are commonly taken to manifest personal preferences. The present study investigated whether social and statistical cues influence young children's inferences about the generalizability of preferences. Preschoolers were exposed to either 1 or 2 demonstrators’ selections of objects. The selected objects constituted 18%, 50%, or 100% of all available objects. We found that children took a single demonstrator's choices as indicative only of his or her personal preference. However, when 2 demonstrators made the same selection, then children inferred that (...) it generalized to other agents of the same kind as the original demonstrator's, but not to agents of a different kind. Lastly, only when both demonstrators blatantly violated random selection (i.e., in the 18% condition) did children generalize the preference even to an agent of a different kind. Thus, from a young age, social and statistical cues inform children's naïve sociology. (shrink)

Many important decisions depend on unknown states of the world. Society is increasingly relying on statistical predictive models to make decisions in these cases. While predictive models are useful, previous research has documented that (a) individual decision makers distrust models and (b) people’s predictions are often worse than those of models. These findings indicate a lack of awareness of how to evaluate predictions generally. This includes concepts like the loss function used to aggregate errors or whether error is training error (...) or generalisation error. To address this gap, we present three studies testing how lay people visually evaluate the predictive accuracy of models. We found that (a) participant judgements of prediction errors were more similar to absolute error than squared error (Study 1), (b) we did not detect a difference in participant reactions to training error versus generalisation error (Study 2), and (c) participants rated complex models as more accurate when comparing two models, but rated simple models as more accurate when shown single models in isolation (Study 3). When communicating about models, researchers should be aware that the public’s visual evaluation of models may disagree with their method of measuring errors and that many may fail to recognise overfitting. (shrink)

Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students. The ranking of outcomes by likelihood coincided with the ranking (...) by representativeness, and Ss erroneously predicted rare events and extreme values if these happened to be representative. The experience of unjustified confidence in predictions and the prevalence of fallacious intuitions concerning statistical regression are traced to the representativeness heuristic. (shrink)

It has been claimed that Massimi’s recent perspectival approach to science sits in tension with a realist stance. I shall argue that this tension can be defused in the quantum context by recasting Massimi’s perspectivalism within a phenomenological framework. I shall begin by indicating how the different but complementary forms of the former are manifested in the distinction between certain so-called ‘-epistemic’ and ‘-ontic’ understandings of quantum mechanics, namely QBism and Relational Quantum Mechanics, respectively. A brief consideration of Dieks’ perspectivism (...) will then lead to a consideration of the much-maligned and typically dismissed role of the observer in the measurement process. This opens the door to London and Bauer’s presentation of a form of ‘phenomenological quantum perspectivalism’ that brings together Massimi’s two forms and explicitly eschews the ‘naïve’ realism that creates the above tension. I shall conclude with some reflections on how intersubjectivity can still be established within this framework, focusing in particular on how Massimi’s idea of ‘interlacing’ scientific perspectives can be accommodated, using the example of a ‘new cosmopolitanism’ that gave rise to Bose-Einstein statistics. (shrink)

The Cosmopolitan idea of the World Government is quite rarely proposed in theory of international relations. Kant already claimed that this idea oscillates between anarchy and brute despotism. This is the reason why he described this standpoint as naive. The author tries to show that alternative theories, such as realism, Kantian and Rawlsian versions of statism and the conception of multilayered scheme of sovereignty, lead to more serious problems. The first one is rejected for the reason of the 'prisoner's (...) dilemma' it implies. It is also argued that the Kantian version of statism is either inconsistent, or allows for totalitarian states if they have peaceful international politics. Many liberals reject Rawls's position because of his tolerant attitude towards 'decent peoples'. On the other hand, the conception of multilayered scheme of sovereignty is dismissed because of the non-existence of a unified decision-making procedure in global politics. At the end of the paper, the author defends Classical Cosmopolitanism theory from Kant's objections and indicates the main obstacles to its realization. Kosmopolitska ideja o svetskoj vladi je redje zastupana teza u teoriji medjunarodnih odnosa. Jos je Kant uputio takvoj ideji prigovor da vodi ili u anarhiju ili u bezdusni despotizam. Zbog toga je takav kosmopolitizam nazivao naivnim. Autor zeli da ukaze na ozbiljnije nedostatke alternativnih teorija poput realizma, Kantove i Rolsove verzije drzavizma, kao i viseslojne sheme suvereniteta. Jedan od osnovnih problema realizma kao tradicionalne teorije jeste slucaj zatvorenikove dileme. Kantova verzija drzavizma je ili nekonzistentna, ili dozvoljava postojanje totalitarnih rezima sa miroljubivom spoljnom politikom. S druge strane, Rolsovo tolerisanje pristojnih naroda predstavlja znacajan ustupak realizmu. Ideji multifunkcionalizma nedostaje jedinstveni princip odlucivanja na globalnom planu. Na kraju teksta autor pokusava da pokaze kako ideja klasicnog kosmopolitizma moze da izadje na kraj sa Kantovim prigovorima, kao i da ukaze na glavnu prepreku za ostvarenje takve ideje. (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)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

Of this article's seven experiments, the first five demonstrate that virtually no Americans know the basic global warming mechanism. Fortunately, Experiments 2–5 found that 2–45 min of physical–chemical climate instruction durably increased such understandings. This mechanistic learning, or merely receiving seven highly germane statistical facts, also increased climate-change acceptance—across the liberal-conservative spectrum. However, Experiment 7's misleading statistics decreased such acceptance. These readily available attitudinal and conceptual changes through scientific information disconfirm what we term “stasis theory”—which some researchers and many (...) laypeople varyingly maintain. Stasis theory subsumes the claim that informing people about climate science may be largely futile or even counterproductive—a view that appears historically naïve, suffers from range restrictions, and/or misinterprets some polarization and correlational data. Our studies evidenced no polarizations. Finally, we introduce HowGlobalWarmingWorks.org—a website designed to directly enhance public “climate-change cognition.”. (shrink)

Nearly all psychological research on basic cognitive processes of category formation and reasoning uses sample populations associated with large research institutions in technologically-advanced societies. Lopsided attention to a select participant pool risks biasing interpretation, no matter how large the sample or how statistically reliable the results. The experiments in this article address this limitation. Earlier research with urban-USA children suggests that biological concepts are thoroughly enmeshed with their notions of naive psychology, and strikingly human-centered. Thus, if children are to (...) develop a causally appropriate model of biology, in which humans are seen as simply one animal among many, they must undergo fundamental conceptual change. Such change supposedly occurs between 7 and 10 years of age, when the human-centered view is discarded. The experiments reported here with Yukatek Maya speakers challenge the empirical generality and theoretical importance of these claims. Part 1 shows that young Maya children do not anthropocentrically interpret the biological world. The anthropocentric bias of American children appears to owe to a lack of cultural familiarity with non-human biological kinds, not to initial causal understanding of folkbiology as such. Part 2 shows that by age of 4-5 Yukatek Maya children employ a concept of innate species potential or underlying essence much as urban American children seem to, namely, as an inferential framework for understanding the affiliation of an organism to a biological species, and for projecting known and unknown biological properties to organisms in the face of uncertainty. Together, these experiments indicate that folkpsychology cannot be the initial source of folkbiology. They also underscore the possibility of a species-wide and domain-specific basis for acquiring knowledge about the living world that is constrained and modified but not caused or created by prior non-biological thinking and subsequent cultural experience. (shrink)

Intuitive judgments of probability are based on a limited number of heuristics that are usually effective but sometimes lead to severe and systematic errors. Research shows, for example, that people judge the probability of a hypothesis by the degree to which it represents the evidence, with little or no regard for its prior probability. Other heuristics lead to an overestimation of the probabilities of highly available or salient events, and to overconfidence in the assessment of subjective probability distributions. These biases (...) are not readily corrected, and they are shared by both naive and statistically sophisticated subjects. The implications of the psychology of judgment to the analysis of rational behaviour are explored. (shrink)

Howard argues that the existence of unitarily inequivalent representations in Quantum Field Theory presents a problem for structural realism in this context. I consider two potential ways round this problem: 1), follow Wallace in adopting the 'naive' Lagrangian form of QFT with cut-offs; 2), adapt Ruetsche's 'Swiss Army Knife' approach. The first takes us into the current debate between Wallace and Fraser on conventional vs. algebraic QFT. The second involves consideration of the role of inequivalent representations in understanding spontaneous (...) symmetry breaking and quantum statistics. In both cases, I suggest, the structural realist has sufficient room to manoeuvre. (shrink)

The work of Gleason and of Kochen and Specker has been thought to refute a naïve realist approach to quantum mechanics. The argument of this paper substantially bears out this conclusion. The assumptions required by their work are not arbitrary, but have sound theoretical justification. Moreover, if they are false, there seems no reason why their falsity should not be demonstrable in some sufficiently ingenious experiment. Suitably interpreted, the work of Bell and Wigner may be seen to yield independent arguments (...) for the falsity of naïve realistic approach to quantum mechanics. Quantum mechanics is no more like classical statistical mechanics than its creators thought it was. (shrink)

There is an ethical dilemma present when considering updating predictive clinical artificial intelligence (AI) models, which should be part of the departmental quality improvement process. One needs to consider whether withdrawing the AI model is necessary to obtain the relevant information from a naive patient population or whether to use causal inference techniques to obtain this information. Withdrawing an AI model from patient care might pose challenges if the AI model is considered standard of care, while use of causal (...) inference will not be reliable if the relevant statistical assumptions do not hold true. Hence, each of these two updating strategies is associated with patient risks, but lack of reliable data might endanger future patients. Similarly, not withdrawing an outdated AI might also expose patients to risk. Here I propose a high level ethical framework – epistemic risk management - that provides guidance for which route of model updating should be taken based on the likelihood of the assumptions used during the creation of the original AI model and the assumptions required for causal inference holding true. This approach balances our uncertainty about the status of the AI as standard of care with the risk of not obtaining the necessary data, so as to increase the probability of benefiting current and future patients for whose care the AI is being used. (shrink)

A shared goal amongst most educators, we argue, is to supplant students’ raw or “naive” intuitions with more refined intuitions about a particular domain. Educators want students, and people more generally, to recognize when ideas, frameworks, and processes don’t “look right”. When we know that something does not look right, sound right, or feel right, we investigate further. We seek to fill in the gaps between our knowledge and we attempt to learn new approaches for solving problems. Lifelong learning, (...) in other words, builds from a recognition of incoherence. We hope students will be more likely to recognize flawed reasoning or rhetorical appeals, even if they have long forgotten the details of logic, statistics, and argumentation. They have acquired a refined (one might even say “aesthetic”) sense of what is likely correct or incorrect; what is likely promising or empty. This sense is “aisthesis”. -/- Aisthesis is a marker of education. Although it doesn’t refer to particular sets of knowledge or particular skills in analyzing and evaluating problems, it provides a overarching framework that binds educators in both literature and science. Consequently, aisthesis provides a novel lens for pedagogy. Insofar as we are correct that education is about the cultivation of an aesthetic discrimination between good and bad work, aisthesis could be more explicitly targeted. Educators hope to instill in students an aisthetic sense of how to recognize when statements, issues, or artifacts are problematic and require further engagmeent. (shrink)

How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation is built up from experienced events, and (...) enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction. (shrink)

Is reality three-dimensional and becoming real (Presentism), or is reality four-dimensional and becoming illusory (Eternalism)? Both options raise difficulties. I argue that we do not need to be trapped by this dilemma. There is a third possibility: reality has a more complex temporal structure than either of these two naive options. Fundamental becoming is real, but local and unoriented. A notion of present is well defined, but only locally and in the context of approximations.

In their contribution to the Review of philosophy and psychology (19 March 2010), David Faraci and David Shoemaker object to Susan Wolf’s sane deep self view of moral responsibility, which is supposed to accord with our pretheoretical intuitions about deprived childhood victims better than the plain deep-self view. Wolf’s account hinges on the intuitiveness of a particular example, which asks us to consider JoJo, the son of an evil dictator of a small, undeveloped country who grows up to adopt his (...) father’s sadistic values. Faraci and Shoemaker conduct a survey to test naïve reasoners’ intuitions on this case. From the study results, they draw three general conclusions: (i) that Wolf’s view is false, (ii) that DSV is basically adequate, subject to the addition of a scalar dimension which measures degrees of responsibility, and (iii) that JoJo is partially excused by naïve moral reasoners because he is seen as suffering from a rare and peculiar form of moral ignorance, “different in kind from the moral ignorance people usually experience” (p. 327). I dispute these conclusions and present an alternative interpretation of the study, which defends an externalist, Strawsonian account of moral responsibility, and I use this approach to show that JoJo’s circumstances are inherently extenuating (at least to a significant degree). Unlike Wolf’s view and DSV, my preferred account is capable of accommodating the fact that JoJo’s formative circumstances were the only factor that made a statistically significant difference to the subjects’ moral judgments. This is because it is the only view capable of according any degree of intrinsic relevance to such external factors. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, (...) decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

Climate change is difficult to connect with personally, because people only regard the phenomenon as important if it becomes a perceived threat to themselves. Arguments like statistics and policy debates are extrinsic motivators, which do not necessarily align people’s own intrinsic motives with those of climate action. Instead, narratives and visual communication can influence viewers implicitly by the way they show and reinforce actions and thoughts that align with climate action. In this design study, we used comics created for (...) human-level climate change influence to promote ideas like future-based thinking, sharing of responsibility, and caring for each other. We also created data visuals that illustrate future consequences of climate change for the purpose of averting negative alternative realities. To see whether our design can affect audience perception of climate change on the human level of goals and desires, we showed the comics to readers unfamiliar with the themes of the stories, presenting them as manga about characters and situations. The survey showed that data stories can affect the way naive readers interpret narratives to align with pro-climate attitudes such as sharing and future-vision, and that readers are focused on the human-level of the data and story as opposed to the physical resource level. Speculative fiction and data visuals provide a potentially effective way to influence individuals’ climate change attitudes by showing alternative realities and attributes of collective responsibility and planning-for-the-future as data stories. (shrink)

To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical (...) information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral. (shrink)

BackgroundSonographic mesenteric pattern in celiac disease (CD) suggests a hyperdynamic circulation. Despite the well-known CD-related neurological involvement, no study has systematically explored the cerebral hemodynamics to transcranial Doppler sonography.Materials and methodsMontreal Cognitive Assessment (MoCA) and 17-item Hamilton Depression Rating Scale (HDRS) were assessed in 15 newly diagnosed subjects with CD and 15 age-, sex-, and education-matched healthy controls. Cerebral blood flow (CBF) velocities and indices of resistivity (RI) and pulsatility (PI) from the middle cerebral artery (MCA), bilaterally, and the basilar (...) artery (BA) were recorded. We also assessed cerebral vasomotor reactivity (CVR) through the breath-holding test (BHT).ResultsWorse scores of MoCA and HDRS were found in patients compared to controls. Although patients showed higher values of CBF velocity from MCA bilaterally compared to controls, both at rest and after BHT, no comparison reached a statistical significance, whereas after BHT both RI and PI from BA were significantly higher in patients. A significant negative correlation between both indices from BA and MoCA score were also noted.ConclusionThese treatment-naïve CD patients may show some subtle CVR changes in posterior circulation, thus possibly expanding the spectrum of pathomechanisms underlying neuroceliac disease and in particular gluten ataxia. Subclinical identification of cerebrovascular pathology in CD may help adequate prevention and early management of neurological involvement. (shrink)

In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics (...) of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing and graph coloring. The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance. (shrink)

SummaryCertain revolutionary changes in medicine—measurement, chemistry, genetics—have led to recasting both the criteriology and the conceptualization of the terms of discourse. But advances along this path rest no longer on naive observation but intimately and inextricably involve modeling, that is, a system of inference which derives no immediate warrant from the primordial data of the senses. This system is not totally new in quality, since all “fact” involves interpretation of data; nor is it entirely new in having heuristic value (...) in unearthing, or calling attention to, unsuspected evidence. But it differs in two major respects. First, it requires patterns of surmise which can no longer be immediately corroborated at all stages by naive means but rest more and more on internal theoretical consistency and confirmatory predictions to be verified by other sophisticated and indirect means. Second, it is leading to fundamental upheavals in even the broad perception of human biology, undermining well-established groupings and exposing certain classical fields of inquiry as meaningless. The problem yet to be confronted in this field is how an epistemology of the model (in this sense) is to be constructed. The model is useless (as the whole scientific endeavor is useless) if it has not a more economical structure than the totality of the raw data, even if the success of this economy is in some degree factitious (“robustness”). On the other hand, the model is also useless unless it has heuristic value and helps in adjudicating among competing interpretations (“power”). A good model will help also to distinguish relevant aspects of the data from the irrelevancies ascribed to error. The paradigmatic function of the model, then, defines certain “sufficient statistics” which condense the relevant features of the data. In fact a close analogy can be developed between the methodology of statistical inference and nosology. Both are necessarily tentative, and healthy development of both, in practice, is iterative; that is, the evidence and the model are mutually correcting and lead to advancement one of another. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the (...) analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

Automatically clustering web pages into semantic groups promises improved search and browsing on the web. In this paper, we demonstrate how user-generated tags from largescale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages. This paper explores the use of tags in 1) K-means clustering in an extended vector space model that includes tags as well as page text and 2) a novel (...) generative clustering algorithm based on latent Dirichlet allocation that jointly models text and tags. We evaluate the models by comparing their output to an established web directory. We find that the naive inclusion of tagging data improves cluster quality versus page text alone, but a more principled inclusion can substantially improve the quality of all models with a statistically signifi- cant absolute F-score increase of 4%. The generative model outperforms K-means with another 8% F-score increase. (shrink)

Knowledge discovery in databases has been studied intensively recent years. In KDD, inductive classifier learning methods which were developed in statistics and machine learning have been used to extract classification rules from databases. Although in KDD we have to deal with large databases in many cases, many of the previous classifier learning methods are not suitable for large databases. They were designed under assumption that any data in databases is accessible on demand and they usually need to access a (...) datum several times in a process of learning. So, they require a huge memory space or a large I/O cost to access storage devices. In this paper, we propose a classifier learning method, we call CIDRE, in which data summaries are constructed and classifiers are learned from the summaries. This learning method is realized by using a clustering method, we call MCF-tree, which is an extension of CF-tree proposed by Zhang et al. In the method, we can specify the size of memory space occupied by data summaries, and databases are swept only once to construct the summaries. In addition, new instances can be inserted into the summaries incrementally. Thus, the method possesses important properties which are desirable to deal with large databases. We also show empirical results, which indicate that our method performs very well in comparison to C4.5 and naive Bayes, and the extension from CF-tree to MCF-tree is indispensable to achieve high classification accuracy. (shrink)

In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The (...) ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems. (shrink)

A growing number of left-wing scholars criticize practices of transnational solidarity. Pointing to the cooptation of “globalism” by neoliberal capitalism, these scholars utilize this critique to advance leftwing nationalism. In this article, I reconstruct symptomatic texts of this genre and identify the critique of (liberal) cosmopolitanism as the common denominator in their calls for nationalizing the Left. As a consequence of their opposition to cosmopolitanism, these authors reject freedom of movement or global justice activism. In order to examine whether the (...) project of transnational solidarity is affected by this critique, I reconstruct its justifications in Critical Theory and postcolonial-feminist theory. Hauke Brunkhorst and Chandra Mohanty exemplarily theorize transnational solidarity in different ways, but each based on a substantial critique of liberal cosmopolitanism. By that, I show that a principled return to nationalism derived from a critique of cosmopolitanism is informed by coincidental evidence that rejects practices of transnational solidarity because of their alleged embeddedness in cosmopolitan reason. Drawing on La Via Campesina, I illustrate principles of a transnational solidarity project that is not justified with liberal cosmopolitan ideas. Their experiences challenge the nationalist/statist critique while not falling back into naïve and/or neoliberal cosmopolitanism. (shrink)

In moments of daring, some physical scientists consider problems of social inquiry, hoping naively that the methods of physical inquiry will provide them with special insight. In my own work on problems of industrial production where I am searching for “practical” means for optimizing production in some socially satisfactory sense, I find that the physical scientist cannot escape the responsibility for social inquiry. So far as I can understand the nature of this work, it requires for its fruitful pursuit a (...) methodology which leans heavily on the concepts of statistical control of Shewhart, of cybernetics of Wiener and of experimentalism of Singer, Churchman and Ackoff. (shrink)

We critically analyse the point of view for which laws of nature are just a mean to compress data. Discussing some basic notions of dynamical systems and information theory, we show that the idea that the analysis of large amount of data by means of an algorithm of compression is equivalent to the knowledge one can have from scientific laws, is rather naive. In particular we discuss the subtle conceptual topic of the initial conditions of phenomena which are generally (...) incompressible. Starting from this point, we argue that laws of nature represent more than a pure compression of data, and that the availability of large amount of data, in general, is not particularly useful to understand the behaviour of complex phenomena. (shrink)

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate (...) cognitive plausibility by using an age-appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition. (shrink)

Paul Gilbert has written a fascinating, stimulating book, one that offers a strong challenge to whoever might believe that the progress of scientific thought has rendered classical metaphysics obsolete, meaningless, or worthy only of historical scholarship. His work, based on the notion that a modern experimental science such as physics is not chiefly grounded on empirical evidence, is quite contrary to that naive empiricist conception already in jeopardy owing to Heisenberg's uncertainty principle and the present day prevalence of statistical (...) explanation. Physics' distinguishing trait is, in fact, the mathematical form given to its laws. However, mathematics is still awaiting a solid and complete foundation since Gödel's incompleteness theorem and the failure of Hilbert's metamathematical program. Such foundational difficulties are no surprise to Gilbert. Like those encountered by modern science's metaphysical predecessors, such difficulties are necessary consequences of the insuperable gap between reality and its discursive representation, between the unity of being, the multiplicity of its sensible appearances, and its analytical and synthetic reconstruction in thought. They reflect the "ontological differences" and conceptual separations that can be properly reunified only in action and not in words, that is, by means of a specific human action performed mentally when one is thinking that he is effectively thinking about something. Now, this type of solution is of course precluded by the prevailing quest for "objectivity" or "knowledge without a subject." For Gilbert, there can be no such thing as knowledge that is not animated by a subject's desire to fulfill the need to know, by doubts and astonishment at the recognition that reality is far more complex than its sensible appearances by which one immediately conceptualizes it. Even one's emotions, passions, freedom, the will to transcend oneself, to build a viable society, and to join with others while respecting their otherness must be taken into account. Any solid foundation for human knowledge must then encompass an ethical dimension. This is the major conclusion which the author wished to arrive at in this book and which had to be established first, before proceeding to the ultimate foundation aimed at by metaphysics. (shrink)

The eliminative view of gauge degrees of freedom—the view that they arise solely from descriptive redundancy and are therefore eliminable from the theory—is a lively topic of debate in the philosophy of physics. Recent work attempts to leverage properties of the QCD $$\theta _{\text {YM}}$$ θ YM -term to provide a novel argument against the eliminative view. The argument is based on the claim that the QCD $$\theta _{\text {YM}}$$ θ YM -term changes under “large” gauge transformations. Here we review (...) geometrical propositions about fiber bundles that unequivocally falsify these claims: the $$\theta _{\text {YM}}$$ θ YM -term encodes topological features of the fiber bundle used to represent gauge degrees of freedom, but it is fully gauge-invariant. Nonetheless, within the essentially classical viewpoint pursued here, the physical role of the $$\theta _{\text {YM}}$$ θ YM -term shows the physical importance of bundle topology (or superpositions thereof) and thus counts against (a naive) eliminativism. (shrink)

There is evidence that bidders fall prey to the winner’s curse because they fail to extract information from hypothetical events—like winning an auction. This paper investigates experimentally whether bidders in a common value auction perform better when the requirements for this cognitive issue—also denoted by contingent reasoning—are relaxed, leaving all other parameters unchanged. For my underlying research question, I used a lab experiment with two stages. In stage I, the subjects participate in a non-standard common value auction, called the wallet (...) game, in which a naïve bidding strategy can lead to both winner’s curse and loser’s curse. In stage II, the subjects in the treatment group learn whether their initial bid was the winning bid or not and they get the opportunity to change this bid. In this sense, the bidders face the same decision problems as in stage I again, but the need for hypothetical thinking is reduced in stage II. The overall pattern of the data suggests that the problem of irrational over- and underbidding can be weakened by giving the subjects ex ante feedback about their bid, but unlike related studies I also find negative effects of additional information. (shrink)

Classic analysis of the subject and the development of personal probability; one of the greatest controversies in modern statistcal thought.

ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral educational studies (...) can provide additional useful statistical information, particularly that associated with the strength of evidence supporting alternative hypotheses, which has not been provided by the classical frequentist approach focusing on P-values. Finally, we introduce several practical guidelines pertaining to how to utilize Bayesian statistics, including the utilization of newly developed free statistical software, Jeffrey’s Amazing Statistics Program, and thresholding based on Bayes Factors, to scholars in the field of moral education. (shrink)

The exponential growth of social data both in volume and complexity has increasingly exposed many of the shortcomings of the conventional frequentist approach to statistics. The scientific community has called for careful usage of the approach and its inference. Meanwhile, the alternative method, Bayesian statistics, still faces considerable barriers toward a more widespread application. The bayesvl R package is an open program, designed for implementing Bayesian modeling and analysis using the Stan language’s no-U-turn (NUTS) sampler. The package combines (...) the ability to construct Bayesian network models using directed acyclic graphs (DAGs), the Markov chain Monte Carlo (MCMC) simulation technique, and the graphic capability of the ggplot2 package. As a result, it can improve the user experience and intuitive understanding when constructing and analyzing Bayesian network models. A case example is offered to illustrate the usefulness of the package for Big Data analytics and cognitive computing. (shrink)

After sketching the conflict between objectivists and subjectivists on the foundations of statistics, this paper discusses an issue facing statisticians of both schools, namely, model validation. Statistical models originate in the study of games of chance, and have been successfully applied in the physical and life sciences. However, there are basic problems in applying the models to social phenomena; some of the difficulties will be pointed out. Hooke's law will be contrasted with regression models for salary discrimination, the latter (...) being a fairly typical application in the social sciences. (shrink)

A widespread assumption in the contemporary discussion of probabilistic models of cognition, often attributed to the Bayesian program, is that inference is optimal when the observer's priors match the true priors in the world—the actual “statistics of the environment.” But in fact the idea of a “true” prior plays no role in traditional Bayesian philosophy, which regards probability as a quantification of belief, not an objective characteristic of the world. In this paper I discuss the significance of the traditional (...) Bayesian epistemic view of probability and its mismatch with the more objectivist assumptions about probability that are widely held in contemporary cognitive science. I then introduce a novel mathematical framework, the observer lattice, that aims to clarify this issue while avoiding philosophically tendentious assumptions. The mathematical argument shows that even if we assume that “ground truth” probabilities actually do exist, there is no objective way to tell what they are. Different observers, conditioning on different information, will inevitably have different probability estimates, and there is no general procedure to determine which one is right. The argument sheds light on the use of probabilistic models in cognitive science, and in particular on what exactly it means for the mind to be “tuned” to its environment. (shrink)

This important collection of essays is a synthesis of foundational studies in Bayesian decision theory and statistics. An overarching topic of the collection is understanding how the norms for Bayesian decision making should apply in settings with more than one rational decision maker and then tracing out some of the consequences of this turn for Bayesian statistics. There are four principal themes to the collection: cooperative, non-sequential decisions; the representation and measurement of 'partially ordered' preferences; non-cooperative, sequential decisions; (...) and pooling rules and Bayesian dynamics for sets of probabilities. The volume will be particularly valuable to philosophers concerned with decision theory, probability, and statistics, statisticians, mathematicians, and economists. (shrink)