There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of (...) inferential contexts , we can see that neither weak nor strong predictivism captures all of the reasons for valuing prediction available. A third path is presented, Pluralist Instrumental Predictivism; PIP for short. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, (...) achieving an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs (...) advanced machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.

heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, (...) and HeartDisease. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This (...) research can enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. (...) It involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The work has (...) five chapters. Chapter 1 lays down its conceptual bases: it explains what learning is taken to be in the current philosophical debate and it points out how Bayesian cognitive science (particularly in its predictive processing formulations) might be well-suited to capture the kind of learning involved in our engagement with the arts. The following chapters test this latter hypothesis with respect to particular art forms, namely literature and literary language (Chapter 2), narrative (Chapter 3), and visual art, music and motor activities (Chapter 4). The fine-grained discussions conducted in each of these areas will enable us to see that the relationship between art and learning is indeed fundamental and pervasive. The final chapter (Chapter 5) examines the consequences of this fact for our understanding of the role of art in our epistemic practices, its ultimate usefulness and value, and its place in the interdisciplinary study of the human mind. The upshot is a novel and wide-ranging picture, both philosophically informed and empirically sound, that bypasses many of the problems and dead ends of the current philosophical debate on the topic and captures the deep sense in which art and learning are interrelated. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)

Why are mistaken beliefs about Covid-19 so prevalent? Political identity, education and other demographic variables explain only a part of individual differences in the susceptibility to Covid-19 misinformation. This paper focuses on another explanation: epistemic vice. Epistemic vices are character traits that interfere with acquiring, maintaining, and transmitting knowledge. If the basic assumption of vice epistemology is right, then people with epistemic vices such as indifference to the truth or rigidity in their belief structures will tend to be more susceptible (...) to believing Covid-19 misinformation. We carried out an observational study (US sample, n = 998) in which we measured the level of epistemic vice of participants using a novel Epistemic Vice Scale. We also asked participants questions eliciting the extent to which they subscribe to myths and misinformation about Covid-19. We find overwhelming evidence to the effect that epistemic vice is associated with susceptibility to Covid-19 misinformation. In fact, the association turns out to be stronger than with political identity, educational attainment, scores on the Cognitive Reflection Test, personality, dogmatism, and need for closure. We conclude that this offers evidence in favor of the empirical presuppositions of vice epistemology. (shrink)

Emotional Intelligence (EI) has been conceptualized in the literature either as a dispositional tendency, in line with a personality trait (trait EI; Petrides and Furnham, 2001), or as an ability, moderately correlated with general intelligence (ability EI; Mayer and Salovey, 1997). Surprisingly, there have been few empirical attempts conceptualizing how the different EI approaches should be related to each other. However, understanding how the different approaches of EI may be interwoven and/or complementary is of primary importance for clarifying the conceptualization (...) of EI and organizing the literature around it. We introduce a theoretical framework explaining how trait EI, ability EI, and emotion information processing – a novel component related to EI recently introduced in the literature (e.g., Fiori and Vesely Maillefer, 2018) – may contribute to effective emotion-related performance and provide initial evidence supporting its usefulness in predicting EI-related outcomes. More specifically, we show that performance in a task in which participants had to infer the mental and emotional states of others, namely a Theory of Mind task, was predicted jointly (e.g., interaction effects) by trait EI, ability EI, and emotion information processing, after controlling for personality and IQ (N = 323). Our results argue for the importance of investigating the joint contribution of different aspects of EI in explaining variability in emotionally laden outcomes. (shrink)

This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory theory (...) defended here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

Over the past two decades, Ginsburg and Jablonka have developed a novel approach to studying the evolutionary origins of consciousness: the Unlimited Associative Learning framework. The central idea is that there is a distinctive type of learning that can serve as a transition marker for the evolutionary transition from non-conscious to conscious life. The goal of this paper is to stimulate discussion of the framework by providing a primer on its key claims and a clear statement of its main (...) empirical predictions. (shrink)

Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical coding’ into (...) a unified conceptual schema, neither the frameworks’ algorithmic specifications nor its hypotheses about their implementations in the brain are clearly unified. I propose a novel way to understand the fruitfulness of the research program in light of a set of research heuristics that are partly shared with those common to Bayesian reverse engineering. An interesting consequence of this proposal is that pluralism is at least as important as unification to promote the positive development of the predictive mind. (shrink)

After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the (...) semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, measures b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use b*, F, or another measure to clarify the Raven Paradox. For this reason, measure c* similar to the correct rate is derived. Measure c* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures F and b* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm “Ravens are black” more strongly than a piece of chalk. Measures F, b*, and c* indicate that the existence of fewer counterexamples is more important than more positive examples’ existence, and hence, are compatible with Popper’s falsification thought. (shrink)

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put (...) forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. (shrink)

Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A (...) neural network model with four layers (1 input and 1 output) was trained on the dataset and achieved an accuracy of 90% and an average error of 0.009. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

Public discourse is often caustic and conflict-filled. This trend seems to be particularly evident when the content of such discourse is around moral issues (broadly defined) and when the discourse occurs on social media. Several explanatory mechanisms for such conflict have been explored in recent psychological and social-science literatures. The present work sought to examine a potentially novel explanatory mechanism defined in philosophical literature: Moral Grandstanding. According to philosophical accounts, Moral Grandstanding is the use of moral talk to seek (...) social status. For the present work, we conducted six studies, using two undergraduate samples (Study 1, N = 361; Study 2, N = 356); a sample matched to U.S. norms for age, gender, race, income, Census region (Study 3, N = 1,063); a YouGov sample matched to U.S. demographic norms (Study 4, N = 2,000); and a brief, one-month longitudinal study of Mechanical Turk workers in the U.S. (Study 5, Baseline N = 499, follow-up n = 296), and a large, one-week YouGov sample matched to U.S. demographic norms (Baseline N = 2,519, follow-up n = 1,776). Across studies, we found initial support for the validity of Moral Grandstanding as a construct. Specifically, moral grandstanding motivation was associated with status-seeking personality traits, as well as greater political and moral conflict in daily life. (shrink)

The ninth and tenth chapters of the Origin mark a profound, if perhaps difficult to detect, shift in the book’s argumentative structure. In the previous few chapters and in the ninth, Darwin has been exploring a variety of objections to natural selection, some more obvious (where are all the fossils of transitional forms?) and some showing careful attention to challenging consequences of evolution (could selection really produce instincts?). Starting in the tenth, however, Darwin turns to showing us what kinds of (...) new and unexpected results evolutionary theory might be able to offer us, again in domains both predictable (extinction) and unexpected (biogeography, embryology). It is notable that it is the fossil record that serves as this pivot point, being both a source of potentially powerful objections to evolutionary theory and home to some of its most compelling new explanations. In this chapter, I present both sets of arguments and consider what role Darwin gave to fossil evidence, in the process attempting to discover why it might have played this unique role in two different parts of Darwin’s “long argument” for evolution by natural selection. Geology’s special place, I argue, derives at least in part from its particular importance in Darwin’s social and intellectual context. (shrink)

Importance: Patients with remitted major depressive disorder (MDD) were previously found to display abnormal functional magnetic resonance imaging connectivity (fMRI) between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions relative to emotions related to blaming others (eg, "indignation or anger toward others"). This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, "feeling guilty for everything"), known to have a key role in cognitive (...) vulnerability to MDD. It is unknown whether this neural signature predicts risk of recurrence, a crucial step in establishing its potential as a prognostic biomarker, which is urgently needed for stratification into pathophysiologically more homogeneous subgroups and for novel treatments. -/- Objective: To use fMRI in remitted MDD at baseline to test the hypothesis that RSATL-SCSR connectivity for self-blaming relative to other-blaming emotions predicts subsequent recurrence of depressive episodes. -/- Design, setting, and participants: A prospective cohort study from June 16, 2011, to October 10, 2014, in a clinical research facility completed by 75 psychotropic medication-free patients with remitted MDD and no relevant comorbidity. In total, 31 remained in stable remission, and 25 developed a recurring episode over the 14 months of clinical follow-up and were included in the primary analysis. Thirty-nine control participants with no personal or family history of MDD were recruited for further comparison. -/- Main outcomes and measures: Between-group difference (recurring vs stable MDD) in RSATL connectivity, with an a priori SCSR region of interest for self-blaming vs other-blaming emotions. -/- Results: We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence. The recurring MDD group showed higher connectivity than the stable MDD group (familywise error-corrected P < .05 over the a priori SCSR region of interest) and the control group. In addition, the recurring MDD group also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy. -/- Conclusions and relevance: To our knowledge, this study is the first to provide a robust demonstration of an fMRI signature of recurrence risk in remitted MDD. Additional studies are needed for its further optimization and validation as a prognostic biomarker. (shrink)

In this paper I challenge and adjudicate between the two positions that have come to prominence in the scientific realism debate: deployment realism and structural realism. I discuss a set of cases from the history of celestial mechanics, including some of the most important successes in the history of science. To the surprise of the deployment realist, these are novel predictive successes toward which theoretical constituents that are now seen to be patently false were genuinely deployed. Exploring the implications (...) for structural realism, I show that the need to accommodate these cases forces our notion of “structure” toward a dramatic depletion of logical content, threatening to render it explanatorily vacuous: the better structuralism fares against these historical examples, in terms of retention, the worse it fares in content and explanatory strength. I conclude by considering recent restrictions that serve to make “structure” more specific. I show however that these refinements will not suffice: the better structuralism fares in specificity and explanatory strength, the worse it fares against history. In light of these case studies, both deployment realism and structural realism are significantly threatened by the very historical challenge they were introduced to answer. (shrink)

I present a recent historical case from cosmology—the story of inflationary cosmology—and on its basis argue that solving explanatory problems is a reliable method for making progress in science. In particular, I claim that the success of inflationary theory at solving its predecessor’s explanatory problems justified the theory epistemically, even in advance of the development of novel predictions from the theory and the later confirmation of those predictions.

I argue that a certain type of naturalist should not accept a prominent version of the no-miracles argument (NMA). First, scientists (usually) do not accept explanations whose explanans-statements neither generate novel predictions nor unify apparently disparate established claims. Second, scientific realism (as it appears in the NMA) is an explanans that makes no new predictions and fails to unify disparate established claims. Third, many proponents of the NMA explicitly adopt a naturalism that forbids philosophy of science from using any (...) methods not employed by science itself. Therefore, such naturalistic philosophers of science should not accept the version of scientific realism that appears in the NMA. (shrink)

Modus ponens is the argument from premises of the form If A, then B and A to the conclusion B. Nearly all participants agree that the modus ponens conclusion logically follows when the argument appears in this Basic form. However, adding a further premise can lower participants’ rate of agreement—an effect called suppression. We propose a theory of suppression that draws on contemporary ideas about conditional sentences in linguistics and philosophy. Semantically, the theory assumes that people interpret an indicative conditional (...) as a context-sensitive strict conditional: true if and only if its consequent is true in each of a contextually determined set of situations in which its antecedent is true. Pragmatically, the theory claims that context changes in response to new assertions, including new conditional premises. Thus, the conclusion of a modus ponens argument may no longer be accepted in the changed context. Psychologically, the theory describes people as capable of reasoning about broad classes of possible situations, ordered by typicality, without having to reason about individual possible worlds. The theory accounts for the main suppression phenomena, and it generates some novel predictions that new experiments confirm. (shrink)

What does it take to understand a phenomenon ideally, or to the highest conceivable extent? In this paper, we answer this question by arguing for five necessary conditions for ideal understanding: (i) representational accuracy, (ii) intelligibility, (iii) truth, (iv) reasonable endorsement, and (v) fitting. Even if one disagrees that there is some form of ideal understanding, these five conditions can be regarded as sufficient conditions for a particularly deep level of understanding. We then argue that grasping, novel predictions, and (...) transparency are not reasonable conditions for ideal understanding. (shrink)

I will first discuss a peculiarity of the realism-antirealism debate. Some authors defending antirealist positions in a philosophical discussion seem to be inconsistent with what they do when treating scientific subjects. In the latter situation, they behave as realists. This tension can be dissolved by distinguishing different discourses belonging to different levels of philosophical radicality. Depending on the respective level, certain presuppositions are either granted or questioned. I will then turn to a discussion of the miracle argument by discussing a (...) simple example of curve fitting. In the example, multiple use-novel predictions are possible without indicating the truth of the fitting curve. Because this situation has similarities with real scientific cases, it sheds serious doubt upon the miracle argument. Next, I discuss the strategy of selective realism, especially its additional crucial component, the continuity argument. The continuity of some X in a series of theories, with X being responsible for the theories’ use-novel predictions, is taken to be a reliable indicator for the reality of X. However, the continuity of X could as well be due to the similarity of the theories in the series with an empirically very successful theory embodying X, without X being real. Thus, the two main arguments for scientific realism show severe weaknesses. (shrink)

The traditional No-Miracles Argument (TNMA) asserts that the novel predictive success of science would be a miracle, and thus too implausible to believe, if successful theories were not at least approximately true. The TNMA has come under fire in multiple ways, challenging each of its premises and its general argumentative structure. While the TNMA relies on explaining novel predictive success via the truth of the theories, we put forth a deductive version of the No-Miracles argument (DNMA) that avoids (...) inference to the best explanation entirely. Instead, a relatively simple empirical framework and a probabilistic analysis can accomplish the ambitious goals of the TNMA while entirely sidestepping its problems. This close-but-distinct argument has many independent strengths and comparatively few weaknesses. Indeed, objections tailored specifically to the DNMA reveal surprising insights into how exactly NMAs are neither circular nor question-begging, as has been widely speculated. (shrink)

This paper argues that scientific realism commits us to a metaphysical determination relation between the mathematical entities that are indispensible to scientific explanation and the modal structure of the empirical phenomena those entities explain. The argument presupposes that scientific realism commits us to the indispensability argument. The viewpresented here is that the indispensability of mathematics commits us not only to the existence of mathematical structures and entities but to a metaphysical determination relation between those entities and the modal structure of (...) the physical world. The no-miracles argument is the primary motivation for scientific realism. It is a presupposition of this argument that unobservable entities are explanatory only when they determine the empirical phenomena they explain. I argue that mathematical entities should also be seen as explanatory only when they determine the empirical facts they explain, namely, the modal structure of the physical world. Thus, scientific realism commits us to a metaphysical determination relation between mathematics and physical modality that has not been previously recognized. The requirement to account for the metaphysical dependence of modal physical structure on mathematics limits the class of acceptable solutions to the applicability problem that are available to the scientific realist. (shrink)

In Logik der Forschung (1934) and later works, Karl Popper proposed a set of methodological rules for scientists. Among these were requirements that theories should evolve in the direction of increasing content, and that new theories should only be accepted if some of their novel predictions are experimentally confirmed. There are currently two, viable theories of cosmology: the standard cosmological model, and a theory due to Mordehai Milgrom called MOND. Both theories can point to successes and failures, but only (...) MOND has repeatedly made novel predictions that were subsequently found to be correct. Standard-model cosmologists, by contrast, have almost always responded to new observations in a post-hoc manner, adjusting or augmenting their theory as needed to obtain correspondence with the facts. I argue that these methodological differences render a comparison of the two theories in terms of their ‘truthlikeness’ or ‘verisimilitude’ essentially impossible since the two groups of scientists achieve correspondence with the facts in fundamentally different ways, and I suggest that a better guide to the theories’ progress toward the truth might be the methodologies themselves. (shrink)

Eric Barnes’ The Paradox of Predictivism is concerned primarily with two facts: predictivism (the fact that novel predictions play an important part in scientificconfirmation) and pluralism (the fact that scientific development is not just a matter of isolated individuals judging the truth, but at least partly a matter of trusting legitimate experts). In the middle part of the book, he peers through these two lenses at the tired realist scarecrow of the no-miracles argument. He attempts to reanimate this weatherworn (...) realist argument, contra suggestions by people like me that it should be abandoned. In this paper, I want to get clear on Barnes’ contribution to the debate. He focuses on what he calls the miraculous endorsement argument, which explains not the success of a specific theory but instead the history of successes for an entire research program. The history of successes is explained by reliable and improving methods, which are the flipside of approximately true background theories. Yet, as Barnes notes, the whole story must begin with methods that are at least minimally reliable. Barnes demands that the realist explain the origin of the minimally reliable take-off point, and he suggests a way that the realist might do so. I contend that his explanation still relies on contingent developments and so fails to completely explain the development of take-off theories. However, this line of argument digs into familiar details of the no-miracles argument and overlooks what’s new in Barnes’ approach. By calling attention to pluralism, he reminds us that we need an account of scientific expertise. This is important, I suggest, because expertise is not indefinite. We do not trust specific experts for everything, but only for things within the bounds of their expertise. Drawing these boundaries relies on our own background theories and is only likely to be reliable if our background theories are approximately true. I argue, then, that pluralism gives us reason to be realists (about some things). (shrink)

Debates in the philosophy of science typically take place around issues such as realism and theory change. Recently, the debate has been reformulated to bring in the role of experiments in the context of theory change. As regards realism, Ian Hacking’s contribution has been to introduce ‘intervention’ as the basis of realism. He also proposed, following Imre Lakatos, to replace the issue of truth with progress and rationality. In this context we examine the case of the vitalism — reductionism debate (...) in biology inspired by the works of Indian physicist-turned-biologist Jagadish Chandra Bose (1858–1937), in the early twentieth century. Both camps had their characteristic hardcores. Vitalists led by John S. Burdon-Sanderson and Augustus D. Waller accepted religious metaphysics to support their research programme, which ultimately degenerated. Bose worked more with the ideals of science such as Occam’s razor, large-scale systematization of phenomena and novel prediction. I argue that his religious metaphysics, instead of acting as a protective shield, helped him to consolidate his position and allowed further problem shift resulting in a research programme that involved consciousness too. His research programme remains relevant even today. (shrink)

Filosofian piirissä on viime aikoina käyty intensiivistä keskustelua metafysiikan naturalisoinnista ja tieteellisen metafysiikan mahdollisuudesta. Yksi tämän keskustelun keskeisistä teoksista on James Ladymanin ja Don Rossin (sekä osin John Collierin ja David Spurrettin) kirjoittama Every Thing Must Go (2007). Tässä kirjassa Ladyman ja Ross puolustavat, omien sanojensa mukaan, neopositivistista skientismiä. Heidän ohjelmansa on skientistinen, koska Ladymanin ja Rossin mukaan tiede on ainoa tapa tutkia todellisuutta objektiivisesti. Neopositivismi ilmenee puolestaan siinä, että heidän ohjelmansa tukeutuu eräänlaiseen verifikaatioperiaatteeseen. Ladymanin ja Rossin verifikaatioperiaate ei kuitenkaan (...) perustu, toisin kuin aiempien positivistien vastaava, kielelliseen merkitykseen tai suoriin havaintoihin vaan tiedeyhteisöön. Tiedeyhteisö päättää, mitkä kysymykset ja teoriat ovat tutkimisen arvoisia. Ladyman ja Ross esittelevät kirjassaan kaksi metafyysistä ohjelmaa: negatiivisen ja positiivisen. Toisaalta he pyrkivät kritisoimaan analyyttistä metafysiikkaa, jota he pitävät liiaksi irtautuneena nykytieteestä, ja toisaalta antamaan oman naturalistisen metafysiikkansa. Pyrimme tässä artikkelissa osoittamaan, että Ladymanin ja Rossin positiivinen ohjelma on jännitteessä, tai jopa ristiriidassa, heidän negatiivisen ohjelmansa kanssa. Tämä näkyy Ladymanin ja Rossin sitoutumisesta modaalirealismiin ja siinä, miten he yrittävät oikeuttaa sitoumuksensa. Jännitteen vuoksi Ladymanin ja Rossin on luovuttava joko modaalirealismista tai metafysiikalle antamistaan tiukoista vaatimuksista. (shrink)

In this paper, I examine Cicero’s oft-neglected De Divinatione, a dialogue investigating the legitimacy of the practice of divination. First, I offer a novel analysis of the main arguments for divination given by Quintus, highlighting the fact that he employs two logically distinct argument forms. Next, I turn to the first of the main arguments against divination given by Marcus. Here I show, with the help of modern probabilistic tools, that Marcus’ skeptical response is far from the decisive, proto-naturalistic (...) assault on superstition that it is sometimes portrayed to be. Then, I offer an extended analysis of the second of the main arguments against divination given by Marcus. Inspired by Marcus’ second main argument, I formulate, explicate, and defend a substantive principle of scientific methodology that I call the “Ciceronian Causal-Nomological Requirement” (CCR). Roughly, this principle states that causal knowledge is essential for relying on correlations in predictive inference. Although I go on to argue that Marcus’ application of the CCR in his debate with Quintus is dialectically inadequate, I conclude that De Divinatione deserves its place in Cicero’s philosophical corpus, and that ultimately, its significance for the history and philosophy of science ought to be recognized. (shrink)

Once lively debates concerning the philosophical significance of self-fulfilling science, or the causal contribution of science to bringing about the states of affairs it depicts, lapsed in the 1970s. Recent claims concerning the influence of economic theory on the behavior it predicts or explains seem poised to revitalize discussion, yet lack of clarity abounds concerning the key features of such cases and the philosophical issues to which they might be relevant. In this paper, I examine a paradigmatic case of self-fulfilling (...) science, clarify its key features, and critically discuss two existing approaches to understanding such phenomena. Ultimately, I suggest a novel approach more well-suited to analyzing such cases and exploring their philosophical significance. (shrink)

Choices confront us with questions. How we act depends on our answers to those questions. So the way our beliefs guide our choices is not just a function of their informational content, but also depends systematically on the questions those beliefs address. This paper gives a precise account of the interplay between choices, questions and beliefs, and harnesses this account to obtain a principled approach to the problem of deduction. The result is a novel theory of belief-guided action that (...) explains and predicts the decisions of agents who, like ourselves, fail to be logically omniscient: that is, of agents whose beliefs may not be deductively closed, or even consistent. (Winner of the 2021 Isaac Levi Prize.). (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm (...) for assessing the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

This paper presents a novel account of focal stress and pitch contour in English dialogue. We argue that one should analyse and treat focus and pitch contour jointly, since (i) some pragmatic interpretations vary with contour (e.g., whether an utterance accepts or rejects; or whether it implicates a positive or negative answer); and (ii) there are utterances with identical prosodic focus that in the same context are infelicitous with one contour, but felicitous with another. We offer an account of (...) two distinct pitch contours that predicts the correct felicity judgements and implicatures, outclassing other models in empirical coverage or formality. Prosodic focus triggers a presupposition, where what is presupposed and how the presupposition is resolved depends on prosodic contour. If resolving the presupposition entails the proffered content, then the proffered content is uninteresting and hence the utterance is in-felicitous. Otherwise, resolving the presupposition may lead to an implicature. We regiment this account in SDRT. (shrink)

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of (...) generated ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez–Batet) and semantic similarity (Lin/Sánchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence. (shrink)

Practical quantum computing devices and their applications to AI in particular are presently mostly speculative. Nevertheless, questions about whether this future technology, if achieved, presents any special ethical issues are beginning to take shape. As with any novel technology, one can be reasonably confident that the challenges presented by "quantum AI" will be a mixture of something new and something old. Other commentators (Sevilla & Moreno 2019), have emphasized continuity, arguing that quantum computing does not substantially affect approaches to (...) value alignment methods for AI, although they allow that further questions arise concerning governance and verification of quantum AI applications. In this brief paper, we turn our attention to the problem of identifying as-yet-unknown discontinuities that might result from quantum AI applications. Wise development, introduction, and use of any new technology depends on successfully anticipating new modes of failure for that technology. This requires rigorous efforts to break systems in protected sandboxes, and it must be conducted at all stages of technology design, development, and deployment. Such testing must also be informed by technical expertise but cannot be left solely to experts in the technology because of the history of failures to predict how non-experts will use or adapt to new technologies. This interplay between experts and non-experts may be particularly acute for quantum AI because quantum mechanics is notoriously difficult to understand. (As Richard Feynman quipped, "Anyone who claims to understand quantum mechanics is either lying or crazy.") We will discuss the extent to which the difficulties in understanding the physics underlying quantum computing challenges attempts to anticipate new failure modes that might be introduced in AI applications intended for unsupervised operation in the public sphere. (shrink)

Despite the recent backlash against epistemic consequentialism, an explicit systematic alternative has yet to emerge. This paper articulates and defends a novel alternative, Epistemic Kantianism, which rests on a requirement of respect for the truth. §1 tackles some preliminaries concerning the proper formulation of the epistemic consequentialism / non-consequentialism divide, explains where Epistemic Kantianism falls in the dialectical landscape, and shows how it can capture what seems attractive about epistemic consequentialism while yielding predictions that are harder for the latter (...) to secure in a principled way. §2 presents Epistemic Kantianism. §3 argues that it is uniquely poised to satisfy the desiderata set out in §1 on an ideal theory of epistemic justification. §4 gives three further arguments, suggesting that it (i) best explains the objective normative significance of the subject's perspective in epistemology, (ii) follows from the kind of axiology needed to solve the swamping problem together with modest assumptions about the relation between the evaluative and the deontic, and (iii) illuminates certain asymmetries in epistemic value and obligation. §5 takes stock and reassesses the score in the debate. (shrink)

In mental action there is no motor output to be controlled and no sensory input vector that could be manipulated by bodily movement. It is therefore unclear whether this specific target phenomenon can be accommodated under the predictive processing framework at all, or if the concept of “active inference” can be adapted to this highly relevant explanatory domain. This contribution puts the phenomenon of mental action into explicit focus by introducing a set of novel conceptual instruments and developing a (...) first positive model, concentrating on epistemic mental actions and epistemic self-control. Action initiation is a functionally adequate form of self-deception; mental actions are a specific form of predictive control of effective connectivity, accompanied and possibly even functionally mediated by a conscious “epistemic agent model”. The overall process is aimed at increasing the epistemic value of pre-existing states in the conscious self-model, without causally looping through sensory sheets or using the non-neural body as an instrument for active inference. (shrink)

Biological theory demands a clear organism concept, but at present biologists cannot agree on one. They know that counting particular units, and not counting others, allows them to generate explanatory and predictive descriptions of evolutionary processes. Yet they lack a unified theory telling them which units to count. In this paper, I offer a novel account of biological individuality, which reconciles conflicting definitions of ‘organism’ by interpreting them as describing alternative realisers of a common functional role, and then defines (...) individual organisms as essentially possessing some mechanisms that play this role. (shrink)

The consensus among spacetime substantivalists is to respond to Leibniz's classic shift arguments, and their contemporary incarnation in the form of the hole argument, by pruning the allegedly problematic metaphysical possibilities that generate these arguments. Some substantivalists do so by directly appealing to a modal doctrine akin to anti-haecceitism. Other substantivalists do so by appealing to an underlying hyperintensional doctrine that implies some such modal doctrine. My first aim in this paper is to pose a challenge for all extant forms (...) of this consensus position. My second aim is to show what form substantivalism must take in order to uphold the consensus while addressing this challenge. The result is a novel "plenitudinous" substantivalist view, which predicts that certain modal facts about spacetime are vague or indeterminate. I then argue against this view on independent grounds, concluding that substantivalists should reject the consensus position. The paper also discusses the way forward for substantivalists in light of this conclusion. (shrink)

In non‐literal uses of language, the content an utterance communicates differs from its literal truth conditions. Loose talk is one example of non‐literal language use (amongst many others). For example, what a loose utterance of (1) communicates differs from what it literally expresses: (1) Lena arrived at 9 o'clock. Loose talk is interesting (or so I will argue). It has certain distinctive features which raise important questions about the connection between literal and non‐literal language use. This paper aims to (i.) (...) introduce a range of novel data demonstrating certain overlooked features of loose talk, and (ii.) develop a new theory of the phenomenon which accounts for these data. In particular, this theory is motivated by the need to explain minimal pairs such as (2)-(3): (2) Lena arrived at 9 o'clock, but she did not arrive at 9 o'clock exactly. (3) ?? Lena did not arrive at 9 o'clock exactly, but she arrived at 9 o'clock. (2) and (3) agree in their truth conditions. Yet they differ in felicity. As such, they constitute a problem for any account which hopes to predict the acceptability of the loose use of a sentence from its truth conditions and the context of utterance alone. Instead, it will be argued, to explain loose talk phenomena we must posit an additional layer of meaning outstripping truth conditions. This layer of meaning is shown to exhibit a range of properties, all of which point to its being semantically encoded. Thus, if correct, the theory provides a new example of how semantic meaning must extend beyond literal, truth‐conditional content. (shrink)

The Ideal Worlds Account of Desire says that S wants p just in case all of S’s most highly preferred doxastic possibilities make p true. The account predicts that a desire report ⌜S wants p⌝ should be true so long as there is some doxastic p-possibility that is most preferred. But we present a novel argument showing that this prediction is incorrect. More positively, we take our examples to support alternative analyses of desire, and close by briefly considering (...) what our cases suggest about the logic of desire. (shrink)

Social norms are commonly understood as rules that dictate which behaviors are appropriate, permissible, or obligatory in different situations for members of a given community. Many researchers have sought to explain the ubiquity of social norms in human life in terms of the psychological mechanisms underlying their acquisition, conformity, and enforcement. Existing theories of the psychology of social norms appeal to a variety of constructs, from prediction-error minimization, to reinforcement learning, to shared intentionality, to domain-specific adaptations for norm acquisition. (...) In this paper, we propose a novel methodological and conceptual framework for the cognitive science of social norms that we call normative pluralism. We begin with an analysis of the explanatory aims of the cognitive science of social norms. From this analysis, we derive a recommendation for a reformed conception of its explanandum: a minimally psychological construct that we call normative regularities. Our central empirical proposal is that the psychological underpinnings of social norms are most likely realized by a heterogeneous set of cognitive, motivational, and ecological mechanisms that vary between norms and between individuals, rather than by a single type of process or distinctive norm system. This pluralistic approach, we suggest, offers a methodologically sound point of departure for a fruitful and rigorous science of social norms. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the (...) dynamics of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Although artificial intelligence has been given an unprecedented amount of attention in both the public and academic domains in the last few years, its convergence with other transformative technologies like cloud computing, robotics, and augmented/virtual reality is predicted to exacerbate its impacts on society. The adoption and integration of these technologies within industry and manufacturing spaces is a fundamental part of what is called Industry 4.0, or the Fourth Industrial Revolution. The impacts of this paradigm shift on the human operators (...) who continue to work alongside and symbiotically with these technologies in the industry bring with it novel ethical issues. Therefore, how to design these technologies for human values becomes the critical area of intervention. This paper takes up the case study of robotic AI-based assistance systems to explore the potential value implications that emerge due to current design practices and use. The design methodology known as Value Sensitive Design (VSD) is proposed as a sufficient starting point for designing these technologies for human values to address these issues. (shrink)