Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of (...) Factors, a set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem’s outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP’s results are quite strong, and SMILE’s much weaker, SMILE + IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts. (shrink)

The prediction of legal judgments is one of the most recognized fields in Natural Language Processing, Artificial Intelligence, and Law combined. By legal prediction, we mean intelligent systems capable of predicting specific judicial characteristics such as the judicial outcome, the judicial class, and the prediction of a particular case. In this study, we used an artificial intelligence classifier to predict the decisions of Brazilian courts. To this end, we developed a text crawler to extract data from official Brazilian (...) electronic legal systems, consisting of two datasets of cases of second-degree murder and active corruption. We applied various classifiers, such as Support Vector Machines, Neural Networks, and others, to predict judicial outcomes by analyzing text features from the dataset. Our research demonstrated that Regression Trees, Gated Recurring Units, and Hierarchical Attention Networks tended to have higher metrics across our datasets. As the final goal, we searched the weights of one of the algorithms, Hierarchical Attention Networks, to find samples of the words that might be used to acquit or convict defendants based on their relevance to the algorithm. (shrink)

This article is an axiomatic approach to the problem of ranking game forms in terms of the predictability they offer to individuals. Two criteria are proposed and characterized, the CardMin and the CardMax. Both compare game forms on the basis of the number of distinct outcomes that can result from the choice of a CardMin (resp. CardMax) strategy. The CardMin (resp. CardMax) strategy is defined as a strategy leading to the smallest (resp. highest) number of different outcomes. In (...) both cases, the lower these numbers the better the game form. (shrink)

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to draw (...) predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. (shrink)

In this paper, we show that Case-based decision theory, proposed by Gilboa and Schmeidler :605–639, 1995), can explain the aggregate dynamics of cooperation in the repeated Prisoner’s Dilemma, as observed in the experiments performed by Camera and Casari. Moreover, we find CBDT provides a better fit to the dynamics of cooperation than does the existing Probit model, which is the first time such a result has been found. We also find that humans aspire to a payoff above the mutual (...) defection outcome but below the mutual cooperation outcome, which suggests they hope, but are not confident, that cooperation can be achieved. Finally, our best-fitting parameters suggest that circumstances with more details are easier to recall. We make a prediction for future experiments: if the repeated PD were run for more periods, then we would be begin to see an increase in cooperation, most dramatically in the second treatment, where history is observed but identities are not. This is the first application of Case-based decision theory to a strategic context and the first empirical test of CBDT in such a context. It is also the first application of bootstrapped standard errors to an agent-based model. (shrink)

Within many animal taxa there is a trend for the species of larger body size to eat food of lower caloric value. For example, most large extant lizards are herbivorous. Reasonable arguments based on energetic considerations are often invoked to explain this trend, yet, while these factors set limits to feasible body size, they do not in themselves mathematically produce optimum body sizes. A simple optimization model is developed here which considers food search, capture, and eating rates and the metabolic (...) cost of these activities for animals of different sizes. The optimization criterion is defined as the net caloric gain a consumer accrues per day. This model does produce an optimum intermediate body size which increases with food quality - not the reverse. This discrepancy is accounted for, however, because the model also predicts that body size should be even more sensitive to increases in food abundance. In nature, many poor quality foods are also relatively abundant foods, hence the consumers eating them may maximize their daily energetic profit by evolving a relatively large body size. Optimum consumer body size also decreases with increases in consumer metabolic rate and prey speed. (shrink)

Principal Component Analysis is one of the data mining methods that can be used to analyze multidimensional datasets. The main objective of this method is a reduction of the number of studied variables with the mainte- nance of as much information as possible, uncovering the structure of the data, its visualization as well as classification of the objects within the space defined by the newly created components. PCA is very often used as a preliminary step in data preparation through the (...) creation of independent components for further analysis. We used the PCA method as a first step in analyzing data from IVF (in vitro fertilization). The next step and main purpose of the analysis was to create models that predict pregnancy. Therefore, 805 different types of IVF cy- cles were analyzed and pregnancy was correctly classified in 61-80% of cases for different analyzed groups in obtained models. (shrink)

Epigenetic markers could potentially be used for risk assessment in risk-stratified population-based cancer screening programmes. Whereas current screening programmes generally aim to detect existing cancer, epigenetic markers could be used to provide risk estimates for not-yet-existing cancers. Epigenetic risk-predictive tests may thus allow for new opportunities for risk assessment for developing cancer in the future. Since epigenetic changes are presumed to be modifiable, preventive measures, such as lifestyle modification, could be used to reduce the risk of cancer. Moreover, epigenetic markers (...) might be used to monitor the response to risk-reducing interventions. In this article, we address ethical concerns related to personal responsibility raised by epigenetic risk-predictive tests in cancer population screening. Will individuals increasingly be held responsible for their health, that is, will they be held accountable for bad health outcomes? Will they be blamed or subject to moral sanctions? We will illustrate these ethical concerns by means of a Europe-wide research programme that develops an epigenetic risk-predictive test for female cancers. Subsequently, we investigate when we can hold someone responsible for her actions. We argue that the standard conception of personal responsibility does not provide an appropriate framework to address these concerns. A different, prospective account of responsibility meets part of our concerns, that is, concerns about inequality of opportunities, but does not meet all our concerns about personal responsibility. We argue that even if someone is responsible on grounds of a negative and/or prospective account of responsibility, there may be moral and practical reasons to abstain from moral sanctions. (shrink)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions (...) can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

Supervised machine learning has found its way into ever more areas of scientific inquiry, where the outcomes of supervised machine learning applications are almost universally classified as predictions. I argue that what researchers often present as a mere terminological particularity of the field involves the consequential transformation of tasks as diverse as classification, measurement, or image segmentation into prediction problems. Focusing on the case of machine-learning enabled poverty prediction, I explore how reframing a measurement problem as a prediction (...) task alters the primary epistemic aim of the application. Instead of measuring a property, machine learning developers conceive of their models as predicting a given measurement of this property. I argue that this _predictive reframing_ common to supervised machine learning applications is epistemically and ethically problematic, as it allows developers to externalize concerns critical to the epistemic validity and ethical implications of their model’s inferences. I further hold that the predictive reframing is not a necessary feature of supervised machine learning by offering an alternative conception of machine learning models as measurement models. An interpretation of supervised machine learning applications to measurement tasks as _automatically-calibrated model-based measurements_ internalizes questions of construct validity and ethical desirability critical to the measurement problem these applications are intended to and presented as solving. Thereby, this paper introduces an initial framework for exploring technical, historical, and philosophical research at the intersection of measurement and machine learning. (shrink)

Emotional creativity refers to cognitive abilities and personality traits related to the originality of emotional experience and expression. Previous studies have found that the COVID-19 epidemic and the restrictions imposed increased the levels of negative emotions, which obstructed adaptation. This research suggests that EC predicts the motivation for innovative adaptive behavior under the restrictions of COVID-19. In the case study of university professors, we show that EC predicts the motivation to creatively capitalize on the imposed online teaching in looking (...) for innovative research and personal development. Methodologically, we rely on the Emotional Creativity Inventory administered to a sample of 463 university professors from the Czech Republic, Slovak Republic, and Russia. The indicators for motivation for innovative performance included motivations to use distant methods of scientific research, to look for partners for conducting scientific research in other cities or abroad, to conduct interdisciplinary research, starting distance learning to enhance qualifications, and the perception that due to online teaching, there is more time for personal development. We employ a set of ordinal regression analyses controlling for age, gender, position, type of science, and country. The results suggest that Emotional Creativity and its three components predict the motivation of university professors to creatively capitalize on the imposed online teaching in looking for innovative research and personal development under the conditions of COVID-19. Furthermore, our results confirmed the gender and age differences in EC. The differences in EC according to position and type of science were not statistically significant. These results compel us to be aware of the importance of the emotional side of creativity to optimize stress-related behavior under the conditions of limited abilities to continue as usual. More space devoted to the manifestation of all the aspects of emotional creativity would improve adaptation to challenging circumstances and even allow one to capitalize on new opportunities. Moreover, we suggest that if personal intrinsic Emotional Creativity is high, the crises, such as the COVID-19 epidemic, may improve adaptation and trigger creative outcomes. (shrink)

Legal decision-support systems have the potential to improve access to justice, administrative efficiency, and judicial consistency, but broad adoption of such systems is contingent on development of technologies with low knowledge-engineering, validation, and maintenance costs. This paper describes two approaches to an important form of legal decision support—explainable outcome prediction—that obviate both annotation of an entire decision corpus and manual processing of new cases. The first approach, which uses an attention network for prediction and attention weights to highlight salient (...) class='Hi'>case text, was shown to be capable of predicting decisions, but attention-weight-based text highlighting did not demonstrably improve human decision speed or accuracy in an evaluation with 61 human subjects. The second approach, termed semi-supervised case annotation for legal explanations, exploits structural and semantic regularities in case corpora to identify textual patterns that have both predictable relationships to case decisions and explanatory value. (shrink)

When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning approaches (...) in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance. Furthermore, we demonstrate that we can achieve a relatively high classification performance when predicting outcomes based only on the surnames of the judges that try the case. (shrink)

A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing (...) that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)

When courts started publishing judgements, big data analysis within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning approaches (...) in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance. Furthermore, we demonstrate that we can achieve a relatively high classification performance when predicting outcomes based only on the surnames of the judges that try the case. (shrink)

How can one build AI systems such that they pursue the goals their designers want them to pursue? This is the alignment problem. Numerous authors have raised concerns that, as research advances and systems become more powerful over time, misalignment might lead to catastrophic outcomes, perhaps even to the extinction or permanent disempowerment of humanity. In this paper, I analyze the severity of this risk based on current instances of misalignment. More specifically, I argue that contemporary large language models (...) and game-playing agents are sometimes misaligned. These cases suggest that misalignment tends to have a variety of features: misalignment can be hard to detect, predict and remedy, it does not depend on a specific architecture or training paradigm, it tends to diminish a system’s usefulness and it is the default outcome of creating AI via machine learning. Subsequently, based on these features, I show that the risk of AI alignment magnifies with respect to more capable systems. Not only might more capable systems cause more harm when misaligned, aligning them should be expected to be more difficult than aligning current AI. (shrink)

In this paper, I propose an account that accommodates the possibility of experimentation being exploratory in cases where the procedures necessary to plan and perform an experiment are dependent on the theoretical accounts of the phenomena under investigation. The present account suggests that experimental exploration requires the implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the (...) notion of exploratory experimentation, which is often attributed in the relevant literature to the works of Friedrich Steinle and Richard Burian, as a particular type of experimental exploration carried out in the special cases where no well-formed theoretical framework of the phenomena under investigation exists. I illustrate the present account in the context of the ATLAS experiment at CERN’s Large Hadron Collider, where the long-sought Higgs boson has been discovered in 2012. I argue that the data selection procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present account. I point out that this particular data selection procedure is theory-laden in that its implementation is crucially dependent on the theoretical models of high energy particle physics which the ATLAS experiment is aimed to test. However, I argue that the foregoing procedure is not driven by the above-mentioned theoretical models, but rather by a particular data selection strategy. I conclude that the ATLAS experiment illustrates that, contrary to what previous studies have suggested, there are cases of experimentation in which exploration serves to test theoretical predictions and that theory-ladenness plays an essential role in experimentation being exploratory. (shrink)

The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire (...) survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...) with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Empirical research has distinguished moral judgments that focus on an act and the actor’s intention or mental states, and those that focus on results of an action and then seek a causal actor. Studies indicate these two types of judgments may result from a “dual-process system” of moral judgment (Cushman 2008, Kneer and Machery 2019). Results-oriented judgements may be subject to the problem of resultant moral luck because different results can arise from the same action and intention. While some argue (...) luck should not bear on persons’ culpability, Victor Kumar has argued that the tendency to hold unlucky agents responsible for harm is justified by consequentialist aims of punishment (Kumar 2019). In contrast, judgments that focus on acts and intentions may be primarily retributive. Kumar claims that judgments focused on results track external, public harm because this increases the reliability of punishment and better achieves instrumental aims, including deterrent effect. In this chapter I examine rape cases using Kumar’s theory of punishment. Rape involves outcomes that are not publicly available. If judgments of punishment depend on outcomes, then we would expect such judgments to be less stable for those instances of wrongdoing that lack public outcomes such as rape, because such judgments would rely instead on often biased and unreliable inferential processes to establish the presence of mental states, which are essentially private. In this way Kumar’s theory actually predicts the way in which we see criminal justice institutions fail with regard to arrest, prosecution, and punishment related to rape; and we might expect similar failures for other crimes that lack publicly available results. In sum, a fundamental problem with institutionalized punishment centered upon results may be that some crimes sit within a moral blindspot. (shrink)

ABSTRACTThe Theory of Event Coding predicts that exposure to affective cues can automatically trigger affectively congruent behaviour due to shared representational codes. An intriguing hypothesis from this theory is that exposure to aversive cues can automatically trigger actions that have previously been learned to result in aversive outcomes. Previous work has indeed found such a compatibility effect on reaction times in forced-choice tasks, but not for action selection in free-choice tasks. Failure to observe this compatibility effect for aversive cues (...) in free choice tasks suggests that control processes aimed at directing behaviour toward positive outcomes may overrule the automatic activation of affectively congruent responses in case of aversive cues. The present study tested whether minimising such control could cause selection of actions that have been learned to result in aversive outcomes. Results showed incidental exposure to aversive cues biased selection of behaviours with learned aver... (shrink)

In this paper I argue that Mill’s ‘Doctrine of Philosophical Necessity’ regarding the human will and action cannot apply on all cases, and that the human mind has potentially the capacity to create freely a will or action that, no matter what kind of knowledge we possess, cannot be predicted. More precisely, I argue against Mill’s attempt of conjunction between the freedom of the will and the ‘Doctrine of Philosophical Necessity’ while I attempt a comparison with the relevant Kantian approach. (...) I then claim that a will cannot be free and be predicted at the same time, as both the elements of freedom and unpredictability of the will are founded on the very process of its formulation as an outcome of genuine creativity. I, thus, attempt to propose a more substantially free view of free will and action than the ones presented by the prominent conceptions. (shrink)

Giorgia Pozzi’s feature article1 on the risks of testimonial injustice when using automated prediction drug monitoring programmes (PDMPs) turns the spotlight on a pressing and well-known clinical problem: physicians’ challenges to predict patient behaviour, so that treatment decisions can be made based on this information, despite any fallibility. Currently, as one possible way to improve prognostic assessments of patient behaviour, Machine Learning-driven clinical decision support systems (ML-CDSS) are being developed and deployed. To make her point, Pozzi discusses ML-CDSSs that are (...) supposed to deliver physicians an accurate estimation of the likelihood of narcotic, sedative and stimulant opioid misuse by a given patient (like, e.g., ‘NarxCare’). Regarding cases of deviating assessment between human evaluators and automated systems, the medico-ethical discussion has so far mainly focused on disagreement between clinicians and Machine Learning (ML) algorithms,2 for example in ‘second opinions’ that have been reconstructed as disagreements between physician testimony and ML-CDSS testimony.3 Examples such as those of PDMPs, by contrast, draw attention to situations in which patient testimony potentially contradicts ML-CDSS testimony. Pozzi examines decision-making situations in which ML-CDSS testimony factually overrides the testimony of the person affected. Thus, in the presented case of patient Kathryn, the testimony of her own physical and mental condition was insufficient to overturn the unfavourable outcome for …. (shrink)

Feigning symptoms distorts diagnostic evaluations. Therefore, dedicated tools known as symptom validity tests have been developed to help clinicians differentiate feigned from genuine symptom presentations. While a deviant SVT score is an indicator of a feigned symptom presentation, a non-deviant score provides support for the hypothesis that the symptom presentation is valid. Ideally, non-deviant SVT scores should temper suspicion of feigning even in cases where the patient fits the DSM’s stereotypical yet faulty profile of the “antisocial” feigner. Across three studies, (...) we tested whether non-deviant SVT scores, indeed, have this corrective effect. We gave psychology students and clinical experts a case alluding to the DSM profile of feigning. In successive steps, they received information about the case, among which non-deviant SVT outcomes. After each step, participants rated how strongly they suspected feigning and how confident they were about their judgment. Both students and experts showed suspicion rates around the midpoint of the scale and did not respond to non-deviant SVT outcomes with lowered suspicion rates. In Study 4, we educated participants about the shortcomings of the DSM’s antisocial typology of feigning and the importance of the negative predictive power of SVTs, after which they processed the case information. Judgments remained roughly similar to those in Studies 1–3. Taken together, our findings suggest that students and experts alike have difficulties understanding that non-deviant scores on SVTs reduce the probability of feigning as a correct differential diagnosis. (shrink)

"Prophecy by magic is a myth. But prophecy by science is a fact." With these words, Lenin begins his article "Prophetic Words" [Prorocheskie slova], in Poln. sobr. soch. [Complete Works], Vol. 36, p. 472. Written in the middle of 1918, a very difficult time for the young Soviet republic, it is devoted entirely to Engels' forecast three decades earlier of the possible outcomes of a world war. With amazing forecasting ability, Engels described the destruction and upheavals that war would (...) cause, the downfall of capitalist empires and the inevitable triumph of the proletariat. Engels warned the kings and statesmen of the capitalist class: "If you unleash forces with which you will not subsequently be able to contend, then, no matter how things develop, at the end of the tragedy you will be a ruin, and the triumph of the proletariat will either already have been won or in any case be inevitable.". (shrink)

This paper investigates three morphosyntactic alternations in Estonian – those between the exterior locative cases allative, adessive and ablative and the corresponding postpositionspeale‘onto’,peal‘on’ andpealt‘off’. Based on the Complexity Principle (e.g., Rohdenburg, Günter. 2002. Processing complexity and the variable use of prepositions in English. In Hubert Cuyckens & Günter Radden (eds.),Perspectives on prepositions, 79–100. Tübingen: Niemeyer), we expect cognitively more complex constructions to use more explicit (i.e., morphologically more substantial) marking by means of a postposition. Further, we expect variation to be (...) conditioned similarly in all three semantic categories. For each of the three alternations, a random sample of the two outcomes (case vs. postposition) from the Estonian National Corpus is used, resulting in 3,000 data points. Using properties of the Landmark phrase as independent variables in Bayesian mixed-effects logistic regression models, we predict the choice of postpositions over case-marked realisations. Of the patterns found, only the frequency-related one supports our complexity-related hypothesis. We conclude that the Complexity Principle, in its general form, has little explanatory power for the Estonian constructions at hand and, in particular, that the derived principle of ‘analytic support’ is not generally applicable. We show, however, that the grammatical knowledge of Estonian exterior locative cases and the corresponding postpositions is regulated by our three factors in a relatively uniform way. (shrink)

Empirical success is a central criterion for scientific decision-making. Yet its understanding in philosophical studies of science deserves renewed attention: Should philosophers think differently about the advancement of science when they deal with the uncertainty of outcome in ongoing research in comparison with historical episodes? This paper argues that normative appeals to empirical success in the evaluation of competing scientific explanations can result in unreliable conclusions, especially when we are looking at the changeability of direction in ongoing investigations. The challenges (...) we encounter arise from the inherent dynamics of disciplinary and experimental objectives in research practice. In this paper we discuss how these dynamics inform the evaluation of empirical success by analyzing three of its requirements: data accommodation, instrumental reliability, and predictive power. We conclude that the assessment of empirical success in developing inquiry is set against the background of a model’s interactive success and prospective value in an experimental context. Our argument is exemplified by the analysis of an apparent controversy surrounding the model of a quantum nose in research on olfaction. Notably the public narrative of this controversy rests on a distorted perspective on measures of empirical success. (shrink)

Tremor of the upper extremity is a significant cause of disability in some patients with multiple sclerosis. The MS tremor is complex because it contains an ataxic intentional tremor component due to the involvement of the cerebellum and cerebellar outflow pathways by MS plaques, which makes the MS tremor, in general, less responsive to medications or deep brain stimulation than those associated with essential tremor or Parkinson's disease. The cerebellar component has been thought to be the main reason for making (...) DBS less effective, although it is not clear whether it is due to the lack of suppression of the ataxic tremor by DBS or else. The goal of this study was to clarify the effect of DBS on cerebellar tremor compared to non-cerebellar tremor in a patient with MS. By wearing an accelerometer on the index finger of each hand, we were able to quantitatively characterize kinetic tremor by frequency and amplitude, with cerebellar ataxia component on one hand and that without cerebellar component on the other hand, at the beginning and end of the hand movement approaching a target at DBS Off and On status. We found that cerebellar tremor surprisingly had as good a response to DBS as the tremor without a cerebellar component, but the function control on cerebellar tremor was not as good due to its distal oscillation, which made the amplitude of tremor increasingly greater as it approached the target. This explains why cerebellar tremor or MS tremor with cerebellar component has a poor functional transformation even with a good percentage of tremor control. This case study provides a better understanding of the effect of DBS on cerebellar tremor and MS tremor by using a wearable device, which could help future studies improve patient selection and outcome prediction for DBS treatment of this disabling tremor. (shrink)

According to the received view, the transportation view, animal extrapolation consists in inductive prediction of the outcome of a mechanism in a target, based on an analogical mechanism in a model. Through an analysis of the failure of preclinical studies of TGN1412, an innovative drug, to predict the tragic consequences of its first-in-man trial in 2006, the received view is challenged by a proposed view of animal extrapolation, the chimera view. According to this view, animal extrapolation is based on a (...) hypothesis about how human organisms work, supported by the amalgamation of results drawn from various experimental organisms, and only predicting the ‘predictive grid’, that is, a global framework of the effects to be expected. (shrink)

Until recently philosophy of physics has been overshadowed by the idea that the important philosophical issues that can be derived from physics are related only to fundamental theories, such as quantum mechanics and relativity. Applied fields of physics were deemed as unimportant. The argument for such a position lays in thinking that these applied fields of physics depend in their theoretical representations on fundamental theories and hence are reducible to these fundamental theories. It would be hard to defend such a (...) position, keeping in mind that applied fields of physics have a life of its own totally separate from fundamental physics and have in fact a lot to say to philosophers. The field of polymers is one of the branches of applied physics that has a lot to say to philosophers. It is a field of physics where theoreticians failed to present a coherent theory that can capture the different ways polymers can be built. This paper examines the difficulties that keep theoreticians away from having such a theory, revealing what kind of philosophical lessons polymers might have. The main thesis is: even in the cases where a theory predict the possibility of developing a specific type of polymers that was not previously known, the exact model that represent the outcome polymer theoretically will not be even in principle derivable from the theory that predicted its existence in the first place. (shrink)

Likert scales are useful for collecting data on attitudes and perceptions from large samples of people. In particular, they have become a well-established tool in soundscape studies for conducting in situ surveys to determine how people experience urban public spaces. However, it is still unclear whether the metrics of the scales are consistently interpreted during a typical assessment task. The current work aims at identifying some general trends in the interpretation of Likert scale metrics and introducing a procedure for the (...) derivation of metric corrections by analyzing a case study dataset of 984 soundscape assessments across 11 urban locations in London. According to ISO/TS 12913-2:2018, soundscapes can be assessed through the scaling of 8 dimensions: pleasant, annoying, vibrant, monotonous, eventful, uneventful, calm, and chaotic. The hypothesis underlying this study is that a link exists between correlations across the percentage of assessments falling in each Likert scale category and a dilation/compression factor affecting the interpretation of the scales metric. The outcome of this metric correction value derivation is introduced for soundscape, and a new projection of the London soundscapes according to the corrected circumplex space is compared with the initial ISO circumplex space. The overall results show a general non-equidistant interpretation of the scales, particularly on the vibrant-monotonous direction. The implications of this correction have been demonstrated through a Linear Ridge Classifier task for predicting the London soundscape responses using objective acoustic parameters, which shows significant improvement when applied to the corrected data. The results suggest that the corrected values account for the non-equidistant interpretation of the Likert metrics, thereby allowing mathematical operations to be viable when applied to the data. (shrink)

One of the fundamental questions of life sciences is one of whether there are genuinely random biological processes. An affirmative or negative answer to this question may have important methodological consequences. It appears that a number of biological processes are explicitly classified as random. One of them is the so-called somatic hypermutation. However, closer analysis of somatic hypermutation reveals that it is not a genuinely random process. Somatic hypermutation is called random because the exact outcome of this process is difficult (...) to predict in practice. The case of somatic hypermutation suggests that there may be no scientific evidence of a single case of ontologically random process in the biological world. (shrink)

In school education, teaching-learning interaction is deemed as a core process in the classroom. The fundamental neural basis underlying teaching-learning interaction is proposed to be essential for tuning learning outcomes. However, the neural basis of this process as well as the relationship between the neural dynamics and the learning outcomes are largely unclear. With non-invasive technologies such as fNIRS, hyperscanning techniques have been developed since the last decade and been applied to the field of educational neuroscience for simultaneous (...) multi-brain scanning. Hyperscanning studies suggest that the interpersonal neural synchronization during teaching-learning interaction might be an ideal neural biomarker for predicting learning outcomes. To systematically evaluate such a relationship, this meta-analysis ran on a random-effects model on 16 studies with 23 independent samples. Further moderator analyses were also performed to examine the potential influences of the style, mode, content, and the assessment method of learning outcomes. The random-effects modeling results confirmed a robust positive correlation between INS and learning outcomes. Subsequent analyses revealed that such relationship was mainly affected by both interaction style and mode. Therefore, the present meta-analysis provided a confirmatory neurocognitive foundation for teaching-learning interaction, as well as its relation to the learning outcomes, consolidated future learning and teaching studies in various disciplines including second language education with a firm methodological reference. (shrink)

The COVID-19 pandemic has changed the lives of millions of people around the globe and some of the unprecedent emerged disruptions, are likely to have been particularly challenging for young children. Studying the impact of such extraordinary circumstances on their well-being is crucial to identify processes leading to risk and resilience. To better understand how Spanish children have adapted to the stressful disruptions resulting from the pandemic outbreak, we examined the effects of child coping and its interactions with contextual stressors (...) on child adjustment, incorporating in our analysis a developmental perspective. Data was collected in April 2020, through parent-reports, during the acute phase of the pandemic and, temporarily coinciding with the mandatory national quarantine period imposed by the Spanish Government. A sample of 1,123 Spanish children aged 3 to 12 participated in the study. Results showed differences in the use of specific strategies by children in different age groups. Despite the uncontrollable nature of the pandemic-related stressors, child disengagement coping was distinctively associated to negative outcomes, whereas engagement coping predicted psychosocial adjustment across all age groups. Moreover, interactively with child coping, parent fear of the future and parent dispositional resilience appear as relevant contextual factors to predict both negative and positive outcomes, but their effects seem to be age dependent, suggesting a higher contextual vulnerability for younger children. These findings might have implications for identifying individual and contextual risk and informing potential preventive interventions aimed to reduce the impact of future pandemic outbreaks on children of different ages. (shrink)

This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health (...) interventions. While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims, but associations and classification schema are more plausibly discovered in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s (...) outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

BackgroundIn Parkinson's disease, the side of motor symptoms onset may influence disease progression, with a faster motor symptom progression in patients with left side lateralization. Moreover, worse neuropsychological outcomes after subthalamic nucleus deep brain stimulation have been described in patients with predominantly left-sided motor symptoms. The objective of this study was to evaluate if the body side of motor symptoms onset may predict motor outcome of bilateral STN-DBS.MethodsThis retrospective study included all consecutive PD patients treated with bilateral STN-DBS at (...) Grenoble University Hospital from 1993 to 2015. Demographic, clinical and neuroimaging data were collected before and 1 year after surgery. The predictive factors of motor outcome at one-year follow-up, measured by the percentage change in the MDS-UPDRS-III score, were evaluated through univariate and multivariate linear regression analysis.ResultsA total of 233 patients were included with one-year follow-up after surgery [143 males ; 121 right body onset; 112 left body onset; mean age at surgery, 55.31 ± 8.44 years; mean disease duration, 11.61 ± 3.87]. Multivariate linear regression analysis showed that the left side of motor symptoms onset did not predict motor outcome.ConclusionsIn this retrospective study, the body side of motor symptoms onset did not significantly influence the one-year motor outcome in a large cohort of PD patients treated with bilateral STN-DBS. (shrink)

As workers struggle to combine work and family responsibilities, discrimination against workers based on their status as caregivers is on the rise. Although both women and men feel the pinch, caregiver discrimination is particularly damaging for women, because care is intricately tied to gendered norms and expectations. In this article, we analyze caregiver discrimination cases resolved by Canadian Human Rights Tribunals from 1985 through 2016, to explore how work and caregiving clash. We identify issues involved in disputes and the ways (...) gendered expectations about work–life facilitation inform disputes and outcomes. We find that although women are more likely to bring claims and obtain favorable outcomes, the legal interpretation of claims is highly gendered. Women bring claims involving both their presumed status as caregivers and the practical challenges of seeking accommodations for care, whereas men’s claims are largely accommodation based. In adjudicating cases, Tribunals are more likely to see women than men as lacking credibility when making their claims, questioning their competence and legitimacy. In contrast, men struggle to demonstrate the legal basis of work–family interference, failing to convey how seriously work interferes with family responsibilities. (shrink)

Background There has been little attention paid to research on the outcomes of clinical ethics support or critical reflection on what constitutes a good CES outcome. Understanding how CES users perceive the importance of CES outcomes can contribute to a better understanding, use of and normative reflection on CES outcomes. Objective To describe the perceptions of Dutch healthcare professionals on important outcomes of moral case deliberation, prior to MCD participation, and to compare results between respondents. (...) Methods This mixed-methods study used both the Euro-MCD instrument and semi-structured interviews. Healthcare professionals who were about to implement MCD were recruited from nursing homes, hospitals, psychiatry and mentally disabled care institutions. Results 331 healthcare professionals completed the Euro-MCD instrument, 13 healthcare professionals were interviewed. The outcomes perceived as most important were ‘more open communication’, ‘better mutual understanding’, ‘concrete actions’, ‘see the situation from different perspectives’, ‘consensus on how to manage the situation’ and ‘find more courses of action’. Interviewees also perceived improving quality of care, professionalism and the organization as important. Women, nurses, managers and professionals in mentally disabled care rated outcomes more highly than other respondents. Conclusions Dutch healthcare professionals perceived the MCD outcomes related to collaboration as most important. The empirical findings can contribute to shared ownership of MCD and a more specific use of MCD in different contexts. They can inform international comparative research on different CES types and contribute to normative discussions concerning CES outcomes. Future studies should reflect upon important MCD outcomes after having experienced MCD. (shrink)