This article examines how well the Kinder, Lydenberg, Domini Research & Analytics ratings measure past corporate social performance and predict future corporate social performance in Diversity and Governance categories. The results show that the KLD ratings effectively measure and predict social performance in both categories. The results also suggest that the KLD ratings may identify differences in the quality of management and firm which can affect future social performance and is not entirely explained by past social performance. The findings (...) of this study lend some support for empirical studies relying on the KLD ratings to operationalize corporate social performance. The findings suggest that users of the ratings need to have a clear understanding of what information they are seeking from the KLD ratings in order to maximize the utility of these ratings. (shrink)

The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid (...) out by Beauchamp and Childress. Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor–patient relationship towards improving population health outcomes and creating a healthier society. (shrink)

An analytic cognitive style denotes a propensity to set aside highly salient intuitions when engaging in problem solving. We assess the hypothesis that an analytic cognitive style is associated with a history of questioning, altering, and rejecting supernatural claims, both religious and paranormal. In two studies, we examined associations of God beliefs, religious engagement, conventional religious beliefs and paranormal beliefs with performance measures of cognitive ability and analytic cognitive style. An analytic cognitive style negatively predicted both religious and paranormal beliefs (...) when controlling for cognitive ability as well as religious engagement, sex, age, political ideology, and education. Participants more willing to engage in analytic reasoning were less likely to endorse supernatural beliefs. Further, an association between analytic cognitive style and religious engagement was mediated by religious beliefs, suggesting that an analytic cognitive style negatively affects religious engagement via lower acceptance of conventional religious beliefs. Results for types of God belief indicate that the association between an analytic cognitive style and God beliefs is more nuanced than mere acceptance and rejection, but also includes adopting less conventional God beliefs, such as Pantheism or Deism. Our data are consistent with the idea that two people who share the same cognitive ability, education, political ideology, sex, age and level of religious engagement can acquire very different sets of beliefs about the world if they differ in their propensity to think analytically. (shrink)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive (...) information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

Currently, most studies are moving towards Big Data Analytics because they are important in research, and this is becoming increasingly important as Internet and Web 2.0 technologies become increasingly popular and how to handle this massive data. Moreover, this proliferation of the Internet and social media has revolutionized the search process. With this Big Data of data generated by users using social media or electronic platforms, the use of these details and daily activities is integrated with tools designed for (...) analysis. The topic of analyzing big social media will be discussed and an intensive explanation will be given to the topic of Big Data. This paper compares Big Data analysis techniques using several methods of analysis, the first technique using neural networks and the second technique using data clustering. The purpose of this study is to infer the ages that use social media and what are their interests in writing and in the end, who are the most widely used social media males or females. (shrink)

PurposeThe purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.Design/methodology/approachData was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the (...) standardized direct and indirect effects in the model.FindingsThe study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.Research limitations/implicationsThe research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.Practical implicationsThe study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.Social implicationsThe use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.Originality/valueThis was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India. (shrink)

Predictive genetic testing is now routinely offered to asymptomatic adults at risk for genetic disease. However, testing of minors at risk for adult-onset conditions, where no treatment or preventive intervention exists, has evoked greater controversy and inspired a debate spanning two decades. This review aims to provide a detailed longitudinal analysis and concludes by examining the debate's current status and prospects for the future. Fifty-three relevant theoretical papers published between 1990 and December 2010 were identified, and interpretative content analysis (...) was employed to catalogue discrete arguments within these papers. Novel conclusions were drawn from this review. While the debate's first voices were raised in opposition of testing and their arguments have retained currency over many years, arguments in favour of testing, which appeared sporadically at first, have gained momentum more recently. Most arguments on both sides are testable empirical claims, so far untested, rather than abstract ethical or philosophical positions. The dispute, therein, lies not so much in whether minors should be permitted to access predictive genetic testing but whether these empirical claims on the relative benefits or harms of testing should be assessed. (shrink)

Confirmed COVID-19 cases in the United States increased exponentially, quickly leading to a pandemic in 2020, which created a serious public-health emergency. During the period in which the COVID-1...

Purpose: The main purpose of the study is to generate an idea about the validity of data-driven predictive analytics related to sustainable banking investment portfolio development. Methodology: Depending on the positivism philosophy, the researcher has focused on generating a hypothesis that can be evaluative and generate an outcome related to the topic under discussion. The descriptive design of the study has helped the researcher develop an idea about the different perspectives related to sustainable investment and ESG goals. The (...) primary quantitative data analysis is conducted by purposefully generating a sample of 101 participants, coming from the managerial level dignitaries from 10 leading banking sectors of the European continental financial market. Results: Analysing the opinions given by the respondents by IBM SPSS software, the results of the study. The results show that most of the male participants have shown their interest in participating in the survey which indicates the career growth of male bankers to be higher than the female ones. The most participating age group is found to be the people belonging to the age group of 21 to 35. The mean value of the correlation coefficient is found to be 0.921 and the mean value of the coefficient of determination is found to be 0.848. On the other hand, the regressive degree of freedom is found to be 11 which indicates the linear relationship present within different variables that are considered generally by the respondents. Conclusion: The interdependence of the variables clearly shows that the market trend regarding the future of the sustainable investment sector present within the banking field can be developed based on predictive analytics inclusion within the level of investment that is focused on meeting the ESG goals for improving sustainability. These factors act as the main motivators for the cross-departmental teams that act simultaneously for performing other tasks along with maintenance of predictive modelling of real-time data to evaluate the future of the banking investment sector. (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)

This is an era of globalization, and to maximize the competitive advantage, the banking industry is acknowledging and recognizing Human Resource practices and contributing their best to employ the organizational resources in best productive manner so that they can excel and enjoy competitive edge. Number of factors are affecting on performance of the employee. This research paper is mainly intended to explore the influence of employee diversity on the performance of employee in Private Banking business of Pakistan. Specifically in this (...) paper, the performance of the employee in a banking sector has been predicted by the influence of employee diversity with respect to their education, age, gender, and ethnicity. The data was collected from 203 private banking representatives by a convenience sampling method. In order to identify the relationship, the data has been tested by SPSS Regression and correlation analysis methods. Research findings suggested that the organizations with specific reference to banking sector should plan, design and execute the policies,, practices and strategies that have a wide range canvas inclusive every individual employee and workforce diversities. (shrink)

Predictive judicial analytics holds the promise of increasing efficiency and fairness of law. Judicial analytics can assess extra-legal factors that influence decisions. Behavioral anomalies in judicial decision-making offer an intuitive understanding of feature relevance, which can then be used for debiasing the law. A conceptual distinction between inter-judge disparities in predictions and inter-judge disparities in prediction accuracy suggests another normatively relevant criterion with regards to fairness. Predictive analytics can also be used in the first step (...) of causal inference, where the features employed in the first step are exogenous to the case. Machine learning thus offers an approach to assess bias in the law and evaluate theories about the potential consequences of legal change. (shrink)

The concept of workforce agility has become increasingly popular in recent years as agile individuals are expected to be better able to handle change and uncertainty. However, agility has rarely been studied in a systematic way. Relations between agility and positive work outcomes, such as higher performance or increased well-being, have often been suggested but rarely been empirically tested. Furthermore, several different workforce agility measures are used in the literature which complicates the comparison of findings. Recognizing these gaps (...) in the literature, we developed a new workforce agility measure, compared this measure to established workforce agility measures, and empirically tested the relations of workforce agility with work outcomes. For this purpose, we surveyed participants from two samples. In a first step, we used Sample 1 to examine the factor structure of the measure for item selection. In a second step, we used Sample 2 to confirm the 10-factor structure and to compare the predictive validity of our measure along with two other agility measures. Findings demonstrate predictive validity for all three workforce agility scales, especially in relation to innovative performance. Furthermore, workforce agility related positively to task and innovative performance, organizational citizenship behavior, job satisfaction, and well-being. (shrink)

Urban resilience is critical for ensuring the sustainability and adaptability of cities in the face of growing challenges such as climate change, population growth, and infrastructure degradation. Predictive analytics, powered by Artificial Intelligence (AI) and the Internet of Things (IoT), offers a transformative approach to enhancing urban resilience. This paper explores how AI-driven predictive analytics can optimize disaster preparedness, infrastructure maintenance, and resource allocation in smart cities. By integrating real-time data from IoT sensors with advanced machine (...) learning models, cities can proactively address vulnerabilities and improve decision-making. This study highlights key applications, challenges, and future directions for leveraging predictive analytics to build resilient urban ecosystems. (shrink)

The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists and (...) academics have begun to raise critical questions about the use of these tools in policing contexts. In this chapter, I review the emerging critical literature on predictive policing and contribute to it by raising ethical questions about the use of predictive analytics tools to identify potential offenders. Drawing from work on the ethics of profiling, I argue that the much-lauded move from reactive to preemptive policing can mean wrongfully generalizing about individuals, making harmful assumptions about them, instrumentalizing them, and failing to respect them as full ethical persons. I suggest that these problems stem both from the nature of predictive policing tools and from the sociotechnical contexts in which they are implemented... (shrink)

“Prediction” and “prescription” are crucial notions for economics. This paper offers a philosophical and methodological approach and takes into account the connection with the problem of science and values. To do this, two steps are followed: firstly, prediction in economics -its characteristics and limits- will be examined and, secondly, the role of prescription in economics (and its relations with internal and external values) will be studied. Thus; the underlying aims of this paper are to make explicit the characters of economic (...) prediction, to show its nexus with the economic prescription and to point out the links of both -especially, the latter- with the specific values of “economic activity” and the values of “economics as activity” (i. e., values of economic undertaking as an activity interconnected with others in the social context). (shrink)

In 1990, Gilles Deleuze publishedPostscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey (...) their own personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control. (shrink)

In this paper, we explore how the development and affordances of predictive analytics may impact how teachers and other educational actors think about and teach students and, more broadly, how society understands education. Our particular focus is on the data dashboards of learning support systems which are based on Machine Learning. While previous research has focused on how these systems produce credible knowledge, we explore here how they also produce compelling, persuasive and convincing narratives. Our main argument is (...) that particular kinds of stories are written by predictive analytics and written into their data dashboards. Based on a case study of a leading predictive analytics system, we explore how data dashboards imply causality between the ‘facts’ they are visualising. To do so, we analyse the stories they tell according to their spatial and temporal dimensions, characters and events, sequentiality as well as tellability. In the stories we identify, teachers are managers, students are at greater or lesser risk, and students’ sociality is reduced to machine-readable interactions. Overall, only data marked as individual behaviours becomes relevant to the system, rendering structural inequalities invisible. Reflecting on the implications of these systems, we suggest ways in which the uptake of these systems can interrupt such stories and reshape them in other directions. (shrink)

Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, (...) different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company, customers who purchase additional products or customers intending to resign from the cooperation. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a (...) hybrid predictive model combining decision trees and cluster analysis. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality. (shrink)

Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above (...) self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither situation nor domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so. (shrink)

The paper is concerned with the psychological relevance of a logical model for deductive reasoning. We propose a new way to analyze logical reasoning in a deductive version of the Mastermind game implemented within a popular Dutch online educational learning system (Math Garden). Our main goal is to derive predictions about the difficulty of Deductive Mastermind tasks. By means of a logical analysis we derive the number of steps needed for solving these tasks (a proxy for working memory load). Our (...) model is based on the analytic tableaux method, known from proof theory. We associate the difficulty of Deductive Mastermind game-items with the size of the corresponding logical trees obtained by the tableaux method. We derive empirical hypotheses from this model. A large group of students (over 37 thousand children, 5–12 years of age) played the Deductive Mastermind game, which gave empirical difficulty ratings of all 321 game-items. The results show that our logical approach predicts these item ratings well, which supports the psychological relevance of our model. (shrink)

Recent studies suggest that learners who are asked to predict the outcome of an event learn more than learners who are asked to evaluate it retrospectively or not at all. One possible explanation for this “prediction boost” is that it helps learners engage metacognitive reasoning skills that may not be spontaneously leveraged, especially for individuals with still-developing executive functions. In this paper, we combined multiple analytic approaches to investigate the potential role of executive functions in elementary school-aged children's science learning. (...) We performed an experiment that investigates children's science learning during a water displacement task where a “prediction boost” had previously been observed—children either made an explicit prediction or evaluated an event post hoc (i.e., postdiction). We then considered the relation of executive function measures and learning, which were collected following the main experiment. Via mixed effects regression models, we found that stronger executive function skills (i.e., stronger inhibition and switching scores) were associated with higher accuracy in Postdiction but not in the Prediction Condition. Using a theory-based Bayesian model, we simulated children's individual performance on the learning task (capturing “belief flexibility”), and compared this “flexibility” to the other measures to understand the relationship between belief revision, executive function, and prediction. Children in the Prediction Condition showed near-ceiling “belief flexibility” scores, which were significantly higher than among children in the Postdiction Condition. We also found a significant correlation between children's executive function measures to our “belief flexibility” parameter, but only for children in the Postdiction Condition. These results indicate that when children provided responses post hoc, they may have required stronger executive function capacities to navigate the learning task. Additionally, these results suggest that the “prediction boost” in children's science learning could be explained by increased metacognitive flexibility in the belief revision process. (shrink)

Emotions play a prominent role in social life, yet the direct impact of emotions on behavior and judgment remains a point of disagreement. The current investigation used meta-analysis to test two theoretical perspectives. The emotion-as-direct-causation perspective asserts that current emotions guide behavior and judgment, whereas the emotion-as-feedback perspective asserts that anticipated emotions guide behavior and judgment. Although the emotion-as-direct-causation perspective was frequently tested, only 22% of tests were significant. Although the emotion-as-feedback perspective was rarely tested, 87% of tests were significant. (...) Our findings suggest that empirical evidence is weak for the default assumption that emotion is the proximal cause of behavior and judgment. Our preliminary findings also suggest that anticipated emotion reliably impacts social behavior and judgment. (shrink)

Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (...) (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable. (shrink)

The experimental research paradigm lies at the core of empirical psychology. New data analytical and computational tools continually enrich its methodological arsenal, while the paradigm’s mission remains the testing of theoretical predictions and causal explanations. Predictions regarding experimental results necessarily point to the future. Once the data are collected, the causal inferences refer to a hypothesis now lying in the past. The experimental paradigm is not designed to permit strong inferences about particular incidents that occurred before predictions were made. In (...) contrast, historical research and scholarship in other humanities focus on this backward direction of inference. The disconnect between forward-looking experimental psychology and backward-looking historical (i.e., narrative) psychology is a challenge in the postmodern era, which can be addressed. To illustrate this possibility, I discuss three historical case studies in light of theory and research in contemporary psychology. (shrink)

A major challenge for Dual Process Theories of reasoning is to predict the circumstances under which intuitive answers reached on the basis of Type 1 processing are kept or discarded in favour of analytic, Type 2 processing (Thompson 2009 ). We propose that a key determinant of the probability that Type 2 processes intervene is the affective response that accompanies Type 1 processing. This affective response arises from the fluency with which the initial answer is produced, such that fluently produced (...) answers give rise to a strong feeling of rightness. This feeling of rightness, in turn, determines the extent and probability with which Type 2 processes will be engaged. Because many of the intuitions produced by Type 1 processes are fluent, it is common for them to be accompanied by a strong sense of rightness. However, because fluency is poorly calibrated to objective difficulty, confidently held intuitions may form the basis of poor quality decisions. (shrink)

Three studies investigated implicit brand attitudes and their relation to explicit attitudes, prod- uct usage, and product differentiation. Implicit attitudes were measured using the Implicit As- sociation Test (IAT; Greenwald, McGhee, & Schwartz, 1998). Study 1 showed expected differ- ences in implicit attitudes between users of two leading yogurt brands, also revealing significant correlations between IAT-measured implicit attitudes and explicit attitudes. In Study 2, users of two fast food restaurants (McDonald’s and Milk Bar) showed implicit attitudi- nal preference for their (...) favorite restaurant. In Study 3, implicit attitudes of users of two soft drinks (Coca-Cola and Pepsi) predicted brand preference, product usage, and brand recognition in a blind taste test. A meta-analytic combination of the three studies showed that the use of IAT measures increased the prediction of behavior relative to explicit attitude measures alone. (shrink)

Many studies find reflective thinking predicts less belief in God or less religiosity — so-called analytic atheism. However, the most widely used tests of reflection confound reflection with ancillary abilities such as numeracy, some studies do not detect analytic atheism in every country, experimentally encouraging reflection makes some non-believers more open to believing in God, and one of the most common sources of online research participants seems to produce lower data quality. So analytic atheism may be less than universal or (...) partially explained by confounding factors. To test this, we developed better measures, controlled for more confounds, and employed more recruitment methods. All four studies detected signs of analytic atheism above and beyond confounds (N > 70,000 people from five of six continental regions). We also discovered analytic apostasy: the better a person performed on reflection tests, the greater their odds of losing their religion since childhood — even when controlling for confounds. Analytic apostasy even seemed to explain analytic atheism: apostates were more reflective than others and analytic atheism was undetected after excluding apostates. Religious conversion was rare and unrelated to reflection, suggesting reflection’s relationships to conversion and deconversion are asymmetric. Detected relationships were usually small, indicating reflective thinking is a reliable albeit marginal predictor of apostasy. (shrink)

In this paper, I discuss the strong thesis of cognitive penetrability (CPs), to wit, th at the perceptual states (P) of a subject (S) are pervasively influenced, affected, or caused by cognitive factors (C) as expectations, memories, thoughts, goals, and so on, at all levels of perceptual processing. I argue that following the predictive coding models of perception (PC), the strong thesis of cognitive penetrability is to be expected.

Forecasting involves an underlying conceptualization of probability. It is this that gives sense to the notion of precision in number that makes us think of economic forecasting as more than simply complicated guesswork. We think of it as well-founded statement, a science and not an art of numbers. However, this understanding is at odds with the nature of social reality and the attributes of the forecaster. We should think differently about how we both anticipate and make the future and what (...) this means. Foresight is perhaps a more appropriate term. This paper addresses two issues that rarely receive attention in the field of economics. First, why is there a continued high demand for economic forecasts despite their lack of success in anticipating significant turning points in any given system? Second, what are forecasts actually assuming about the nature of a system and the future state of the world? In the paper I approach these issues indirectly. My intention is to highlight their significance by setting out a series of arguments that encapsulate the characteristics of a forecaster required to match a common understanding of what forecasting is intended to do. The structure of this paper is unusual for a contribution in the field of economics. It follows a format more commonly used in analytical philosophy when the author wants to focus on a problem and where the intention is to provoke further questioning, rather than supply ready answers. As such, it should not be read as a comprehensive account of all possible approaches to methods or philosophies of forecasting and attendant issues of probability. Three points are worth stating at the outset as a guide to what follows: Forecasting tends to forget that it is conjecture and what that really means. Its scientism overburdens how it is articulated and how it is perceived. We tend to think of forecasting as degrees of precision in prediction and of successful prediction as successful description of phenomena at some future point. There is, as the following discussion demonstrates, something basically inconsistent within the implications of this minimalism. Since the greater part of the credibility of, and authority of, economics resides in its claims to effective forecasting these points are highly relevant to the state of the discipline. Read the Open Peer Discussion on this paper ». (shrink)

The Analytic/Synthetic distinction did not originate in Kant, but in Port-Royal's logical theory. The key for the doctrine is the explicite recognition of two different kinds of relative clauses, e.g. explicative and determinative. In the middle eighteenth century the distinction becomes a topic within the grammars. Although we can find by grammarians different criteria for the distinction, these criteria (for which we can find medieval sources) are for the main predictable from the original theory of ideas, which was presented in (...) Port-Royal's logical writings. The topic of the two relative clauses (somewhat broader than the analytic/synthetic distinction) can be used to give empirical criteria for analyticity and also for revisiting Quine's criticism of the topic. Analyticity yet appears as a master piece of classical linguistic philosophy and not as being the empty dogma of modern empiricism. (shrink)

Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant (...) increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results. (shrink)

Prediction is an important aspect of scientific practice, because it helps us to confirm theories and effectively intervene on the systems we are investigating. In ecology, prediction is a controversial topic: even though the number of papers focusing on prediction is constantly increasing, many ecologists believe that the quality of ecological predictions is unacceptably low, in the sense that they are not sufficiently accurate sufficiently often. Moreover, ecologists disagree on how predictions can be improved. On one side are the ‘theory-driven’ (...) ecologists, those who believe that ecology lacks a sufficiently strong theoretical framework. For them, more general theories will yield more accurate predictions. On the other are the ‘applied’ ecologists, whose research is focused on effective interventions on ecological systems. For them, deeper knowledge of the system in question is more important than background theory. The aim of this paper is to provide a philosophical examination of both sides of the debate: as there are strengths and weaknesses in both approaches to prediction, a pluralistic approach is best for the future of predictive ecology. (shrink)

The aim of this article is to provide a way of resolving the apparent dilemma between our requirement as agents that actions should be free and our demand as spectators that all events should be predictable and explicable on the basis of antecedent conditions. I hope to show that what has often been incorrectly regarded as a logical incompatibility between freedom and determinism is, in fact, a disparity but not an over-all contradiction between the viewpoint of an agent and that (...) of a spectator. While it is logically impossible for a person to deliberate about his conduct in a given matter from the standpoint of an agent and also predict it as a spectator, this is the only logical incompatibility between these positions, and it does not preclude the two points of view from being equally legitimate ways of viewing action. Perhaps it has sometimes been a vague recognition of the fact that one logically cannot both deliberate upon and predict one’s own conduct in a certain matter that has led some philosophers to conclude erroneously that choice on the part of an agent is inconsistent with prediction or explanation of his decision on the part of any spectator. This unwarranted conclusion is equivalent to denying that conduct can be regarded both from the standpoint of the agent and that of the spectator. (shrink)

The Cognitive Reflection Test (CRT) is a measure of analytical reasoning that cues an intuitive but incorrect response that must be rejected for successful performance to be attained. The CRT yields two types of errors: Intuitive errors, which are attributed to Type 1 processes; and non-intuitive errors, which result from poor numeracy skills or deficient reasoning. Past research shows that participants who commit the highest numbers of errors on the CRT overestimate their performance the most, whereas those with the lowest (...) error-rates tend to slightly underestimate. This is an example of the Dunning-Kruger Effect (DKE). The present study examined how intuitive vs. non-intuitive errors contribute to overestimation in the CRT at different levels of performance. Female undergraduate students completed a seven-item CRT test and subsequently estimated their raw score. They also filled out the Faith in Intuition (FI) questionnaire, which is a dispositional measure of intuitive thinking. Data was separated into quartiles based on level of performance on the CRT. The results demonstrated the DKE. Additionally, intuitive and non-intuitive errors predicted miscalibration among low, but not high performers. However, intuitive errors were a stronger predictor of miscalibration. Finally, FI was positively correlated with CRT self-estimates and miscalibration, indicating that participants who perceived themselves to be more intuitive were worse at estimating their score. These results taken together suggest that participants who perform poorly in the CRT and also those who score higher in intuitive thinking disposition are more susceptible to the influences of heuristic-based cues, such as answer fluency, when judging their performance. (shrink)

The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular (...) how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis. (shrink)

With the progress of social production, the competition for talents among enterprises is fierce, and the market often lacks capable leaders, which leads to the lack of management of enterprise employees and cannot bring more economic benefits to enterprises. Traditional leaders make subordinate employees work actively and achieve the common goal of the enterprise by exerting their own leadership characteristics and observing their subordinates, but they cannot take care of the psychological state of each employee, resulting in the employee's work (...) efficiency is not very high. In recent years, charismatic leadership has become an important economic leader in the new era, and the theoretical spirit of charismatic leadership can well guide employees to work actively. Artificial intelligence affective computing can well identify the psychological state of the subject, and the combination of artificial intelligence affective computing and charismatic leadership can achieve effective management of employees through the predictive analysis of employees' psychological state. This paper compares the psychological state prediction analysis of employees' work attitudes between charismatic leaders based on artificial intelligence affective computing and traditional leaders through experiments. The results show that: charismatic leaders based on artificial intelligence affective computing predictive analytics can improve sensitivity to employee needs, resulting in an 8.2% increase in employee trust in leadership, a 4.4% increase in employee commitment to achieving organizational goals, and a 19.3% increase in employee job satisfaction. The psychological state prediction analysis of charismatic leaders based on artificial intelligence affective computing on employees' work attitudes can improve the work efficiency of employees and the economic benefits of enterprises. (shrink)

While individual differences in the willingness and ability to engage analytic processing have long informed research in reasoning and decision making, the implications of such differences have not yet had a strong influence in other domains of psychological research. We claim that analytic thinking is not limited to problems that have a normative basis and, as an extension of this, predict that individual differences in analytic thinking will be influential in determining beliefs and values. Along with assessments of cognitive ability (...) and style, religious beliefs, and moral values, participants judged the wrongness of acts considered disgusting and conventionally immoral, but that do not violate care- or fairness-based moral principles. Differences in willingness to engage analytic thinking predicted reduced judgements of wrongness, independent of demographics, political ideology, religiosity, and moral values. Further, we show that those who were higher in cognitive ability were less likely to indicate that purity, patriotism, and respect for traditions and authority are important to their moral thinking. These findings are consistent with a “Reflectionist” view that assumes a role for analytic thought in determining substantive, deeply-held human beliefs and values. (shrink)

Can we measure and predict character with predictive analytics so a business can better assess, ideally objectively, whether to lend money or extend credit to that person, beyond current objective measures of credit scores and standard financial metrics like solvency and debt ratios? We and the analysts probably do not know enough about character to try to measure it, though it might be more useful to measure and predict a person’s temperance and prudence as virtues, or self-control as (...) psychology, or sense of obligation, particularly a moral commitment or sense of duty to honor a contract and re-pay a loan. The pervasive data surveillance of people that goes with “big data” and predictive analytics is not only an invasion of privacy in general, but an impairment of the aspect of privacy called autonomy that will constrict and alter a person’s choices and development of self. (shrink)

Belief in astrology remains strong even today, and one of the explanations why some people endorse paranormal explanations is the individual differences in analytical thinking. Therefore, the main aim of this paper was to determine the effects of priming an analytical or intuitive thinking style on the credulity of participants. In two experiments (N= 965), analytic thinking was induced and the source of fake profile (astrological reading vs. psychological testing) was manipulated and participants’ prior paranormal beliefs, anomalous explanation, cognitive reflection, (...) and depression were measured. Although analytic thinking was proved to be hard to induce experimentally, the results showed that analytic thinking predicts credulity and belief in the paranormal was linked with experiencing more anomalous experiences and more paranormal explanations. The more people were able to think analytically, the less credulous they were as reflected in the lower acceptance of fake profile as accurate. (shrink)

Beauchamp and Childress’ definition of autonomous decision‐making includes the conditions of intentionality, understanding, and non‐control. In genetics, however, a relational conception of autonomy has been increasingly recognized. This article aims to empirically assess aspects of social influence in genetic testing decision‐making and to connect these with principlist and relational theories of autonomy. We interviewed 18 adult genetic counsellees without capacity issues considering predictive genetic testing for cancer predisposition for themselves and two counselling physicians in Switzerland. We conducted a qualitative (...) analysis, building on a grounded theory study about predictive genetic testing decision‐making. We found that some participants agreed to predictive genetic testing predominantly because relatives wanted them to do it, with some even acting contrary to their own convictions. Others, in contrast, based their decision on purely individualistic reasons but expressed difficulties in explaining their decision to their social environment. Healthcare professionals had a critical influence on decision‐making in many cases without being manipulative, as perceived by counsellees. Still, cases of coercion and social pressure occurred within social relationships. In conclusion, predictive genetic testing decision‐making includes relational and individualistic aspects, and both are compatible with autonomous decision‐making. While the principlist and relational notions of autonomy compete on a theoretical level, they are two sides of the same coin when used as analytical lenses for genetic testing decision‐making. Social acceptance of refusal of testing should be improved to mitigate social pressure. Individuals should be encouraged to decide for themselves how much their social environment influences their decision regarding predictive genetic testing. (shrink)

In the present study, we have demonstrated hysteresis phenomena in the excitability of single, enzymatically dissociated guinea pig ventricular myocytes. Membrane potentials were recorded with patch pipettes in the whole-cell current clamp configuration. Repetitive stimulation with depolarizing current pulses of constant cycle length and duration but varying strength led to predictable excitation (1:l) and non-excitation (1:0) patterns depending on current strength. In addition, transition between patterns depended on the direction of current intensity change and stable hysteresis loops were obtained in (...) stimulus:response pattern vs. current intensity plots in 14 cells. Increase of pulse duration and decrease of stimulation rate contributed to a reduction in hysteresis loop areas. Changes in amplitude and shape of the subthreshold responses during the transitions from one stable pattern to the other, suggested that activity led to an increase in membrane resistance, particularly in the voltage domain between resting potential, and threshold. Therefore, we modelled the dynamic behaviour of the single cells as a function of diastolic membrane resistance, using previously published analytical solutions. Numerical iteration of the analytical model equations closely reproduced the experimental hysteresis loops in both qualitative and quantitative ways. In particular, the effect of stimulation frequency on the model was similar to the experimental findings. The overall study suggests that the excitability pattern of guinea pig ventricular myocytes accounts for hysteresis and bistabilities when current intensity is allowed to fluctuate around threshold levels. (shrink)

The article focuses on exploring the directions for implementing innovative analytical-statistical technologies as a tool for monitoring and detecting corruption in the state. To achieve this goal, the author clarifies the content of key concepts, defines the essence of innovative analytical-statistical technologies, and analyzes the applications of these technologies as elements of the state’s anti-corruption policy. It is determined that modern analytical-statistical technologies are integral to information technologies, which have emerged as a separate branch of production known as the information (...) industry. Information technologies, including analytical-statistical ones, are realized through information systems, which combine technical tools (computers, communication devices and channels, peripherals, input/output devices, etc.) with software applications that facilitate the functioning of the technical components and analytical aspects, including the mathematical component (statistics). The use of analytical-statistical technologies as a tool to counter corruption at the state level is driven by the specificity of the corruption identification process. The application of relevant technologies is possible when perceiving corruption as a form of fraud. Therefore, the search for corruption in modern society requires a comprehensive approach, utilizing mathematical, analytical, and psychophysical research methods. This approach enables not only the identification but also the prediction of probable instances of abuse. Five directions for implementing innovative analytical-statistical technologies in anti-corruption activities are identified: 1) automation of data collection and processing; 2) automation of communication and office processes; 3) automation of direct management processes; 4) automation of decision-making processes and development; 5) development of expert systems. It is noted that analytical-statistical technologies find the most implementation in four of the identified directions: data collection/processing, direct management, decision support, and expert support. (shrink)