The no-miracles argument and the pessimistic induction are arguably the main considerations for and against scientific realism. Recently these arguments have been accused of embodying a familiar, seductive fallacy. In each case, we are tricked by a base rate fallacy, one much-discussed in the psychological literature. In this paper we consider this accusation and use it as an explanation for why the two most prominent `wholesale' arguments in the literature seem irresolvable. Framed probabilistically, we can see very clearly why (...) realists and anti-realists have been talking past one another. We then formulate a dilemma for advocates of either argument, answer potential objections to our criticism, discuss what remains (if anything) of these two major arguments, and then speculate about a future philosophy of science freed from these two arguments. In so doing, we connect the point about base rates to the wholesale/retail distinction; we believe it hints at an answer of how to distinguish profitable from unprofitable realism debates. In short, we offer a probabilistic analysis of the feeling of ennui afflicting contemporary philosophy of science. (shrink)

There is a theorem that shows that it is impossible for an algorithm to jointly satisfy the statistical fairness criteria of Calibration and Equalised Odds non-trivially. But what about the recently advocated alternative to Calibration, Base Rate Tracking? Here, we show that Base Rate Tracking is strictly weaker than Calibration, and then take up the question of whether it is possible to jointly satisfy Base Rate Tracking and Equalised Odds in non-trivial scenarios. We show that it is (...) not, thereby establishing an even more general impossibility theorem. (shrink)

The meaning of an experimental result depends on the experiment's conceptual backdrop, particularly its null hypothesis. This observation provides the basis for a functional interpretation of belief in the base rate fallacy. On this interpretation, if the base rate fallacy is to be labelled a “myth,” then it should be recognized that this label is not necessarily a disparaging one.

During multiple testing, researchers often adjust their alpha level to control the familywise error rate for a statistical inference about a joint union alternative hypothesis (e.g., “H1,1 or H1,2”). However, in some cases, they do not make this inference. Instead, they make separate inferences about each of the individual hypotheses that comprise the joint hypothesis (e.g., H1,1 and H1,2). For example, a researcher might use a Bonferroni correction to adjust their alpha level from the conventional level of 0.050 to 0.025 (...) when testing H1,1 and H1,2, find a significant result for H1,1 (p < 0.025) and not for H1,2 (p > .0.025), and so claim support for H1,1 and not for H1,2. However, these separate individual inferences do not require an alpha adjustment. Only a statistical inference about the union alternative hypothesis “H1,1 or H1,2” requires an alpha adjustment because it is based on “at least one” significant result among the two tests, and so it refers to the familywise error rate. Hence, an inconsistent correction occurs when a researcher corrects their alpha level during multiple testing but does not make an inference about a union alternative hypothesis. In the present article, I discuss this inconsistent correction problem, including its reduction in statistical power for tests of individual hypotheses and its potential causes vis-à-vis error rate confusions and the alpha adjustment ritual. I also provide three illustrations of inconsistent corrections from recent psychology studies. I conclude that inconsistent corrections represent a symptom of statisticism, and I call for a more nuanced inference-based approach to multiple testing corrections. (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)

Concurring to information from the Centresfor Illness Control and Anticipation, instruction and religion are both capable indicators of lasting or dissolving unions. The chance of a marriage finishing in separate was lower for individuals with more knowledge, with over half of relational unions of those who did not complete high school having finished in separate compared with roughly 30 percent of relational unions of college graduates. With this overview, the divorce rate dataset from UCI dataset repository is used for predicting (...) the divorce class target with the following contributions. Firstly, the Divorce rate dataset is subjected with the data cleaning and exploratory data analysis. Secondly, the data set is settled with different classifiers to look at the classification before and after feature scaling. Thirdly, the dataset is processed with various cross validation of training and testing dataset i.e 80:20, 30:70, 40:60, 50:50 to improve the accuracy of all the classifiers. Fourth, the dataset is processed with 15, 20 and 30 components of principal component analysis and then applied with all classifier algorithm to analyze the accuracy of divorce rate prediction. Fifth, the performance analysis is done with precision, recall, accuracy, fscore and running time to infer the classification before and after feature scaling. Experimental results show that the Random Forest classifier is found to have the accuracy of 98% for all PCA reduced dataset with 15, 20 and 30 components. The result showsthat Random Forest classifier is found to have the accuracy of 98% for 40:60, 50:50 of training and testing dataset. (shrink)

Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of (...) books. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

Molecular clocks based upon amino acid sequences in proteins have played a major role in the clarification of evolutionary phylogenies. Creationist criticisms of these methods sometimes rely upon data that might initially seem to be paradoxical. For example, human cytochrome c differs from that of an alligator by 13 amino acids but differs by 14 amino acids from a much more closely related primate, Otolemur garnettii. The apparent anomaly is resolved by taking into consideration the variable substitution rate of cytochrome (...) c, particularly among primates. This paper traces some of the history of extensive research into the topic of rate heterogeneity in cytochrome c including data from cytochrome c pseudogenes. (shrink)

The adequacy of similarity to prototype as an account of categorization in natural concepts was assessed by analyzing the monotonicity of the relation between typicality of an item in a category and the probability of a positive categorization response using data from McCloskey and Glucksberg (1978). The analysis revealed a strong underlying similarity-based threshold curve, with systematic deviations. Further data collection showed that deviations from the curve could be attributed to the effects of unfamiliarity and non-categorial associations on typicality judgments, (...) as well as differences between the perceptual appearance of an item (which tended to boost typicality) and its underlying nature (which tended to boost categorization). The results are discussed in terms of the different presuppositions and task constraints involved in rating typicality as opposed to performing a categorization. (shrink)

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard’s idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard’s intelligence measure is based on the latter growth-rate-measuring method, we survey (...) other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs). (shrink)

In 2012, a major traffic safety organization tasked the MIT AgeLab with developing a data-driven system for rating the effectiveness of new technologies intended to improve safety. Such a system was envisioned as having the potential to educate and guide consumers towards more confident and strategic purchasing decisions, ideally encouraging adoption of technologies with demonstrated safety benefit. In addition, an evaluation of the status and extent of existing data was seen as a way of identifying research gaps in the state (...) of knowledge about safety systems. The focus was on technologies as a class, not on a rating review of individual vehicle model implementations. As conceptualized, the system aimed to complement traditional NCAP style ratings as well as to provide consumers with transparent information on early stage and often improving safety technologies. Development of the rating system and identification of data was undertaken in consultation with a range of academic, industrial, consumer, NGO, and governmental experts as well as with representatives of many of the major automotive manufacturers and suppliers. -/- A key observation that emerged was that data on objectively demonstrable real-world benefits were generally sparse and often lower than expectations based on theoretical considerations, simulation studies, or pre-production evaluations. A number of experts and industry representatives expressed some surprise at both the divergence between theoretical and observed benefits and the relative scarcity of data upon which to make objective assessments, while others were quite aware of these issues and the need for the development of objective data under real-world operating conditions. A number of factors that might be relevant to understanding why such differences between expected and observed benefits exist were identified. One outcome of this effort was the founding of the Advanced Vehicle Technology (AVT) consortium to collect and examine objective data under naturalistic driving conditions of how drivers interact with, engage or don’t engage, various production safety and driver assistance systems. This ongoing effort is contributing to insights concerning actual benefits and reasons for benefit gaps. -/- Drawing from our initial work, as well as newer sources of data, we argue that the evaluation and rating of safety and driver assistance technologies for informing the consumer and the public at large should consider both theoretical potential and existing demonstrated benefit of specific technologies. This position is increasingly relevant as the effectiveness of many newer technologies have the potential to actually improve over the lifecycle of a vehicle through software updates. The emphasis on ratings based on observed benefit for actual drivers under real-world conditions is proposed to be complementary, rather than competing with, ratings focused largely on controlled test track evaluations of engineered capability. In addition, a case is made for providing ratings that assesses benefit relative to overall crash, injury, and fatality rates – and in relation to the specific scenario / crash event type that a given technology is intended to address. This approach should aid consumers in considering the extent to which a specific technology is or is not relevant to their particular driving needs. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

In my presentation I argue for the utility of a philosophical counseling method, called logic-based therapy (LBT), in the treatment of addicted populations. In the context of addiction treatment LBT could be also classified as a philosophical psychotherapy. Philosophical psychotherapy can be understood as an umbrella term for interventions designed to treat mental health disorders, with theoretical foundations that are philosophical. Philosophical psychotherapy would be distinct from philosophical counseling, as the latter does not directly treat mental health disorders. I suggest (...) that LBT has utility beyond philosophical counseling and is a viable intervention in the treatment of certain mental health disorders, like substance use disorders. I provide a brief overview of LBT and then discuss a LBT case study with a client suffering diagnosed with a substance use disorder. In the case study the client was advised to apply the moral philosophy of Friedrich Nietzsche as an uplifting philosophical framework to counteract his unproductive worldview and fallacious thinking. Considering that there is an ostensibly low efficacy rate for the treatment of addiction, articulating the value of philosophical psychotherapies in the context of addiction treatment can assist in the development of novel philosophically-based addiction treatment and recovery-oriented programs––thus expanding the treatment and recovery options available for those seeking recovery from addiction. (shrink)

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the (...) visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

The aim of this article is to address the issue of measuring radio audience from a methodological perspective. Although our approach is contextualized, the case study of radio audience measurement in Romania is relevant to a large number of EU countries, in which scholars and practitioners use Day-After-Recall technique. Considering the size of the radio advertising market, as well as the number of radio listeners (about 90% in EU), it is crucial for both radio broadcasters and advertisers to measure radio (...) audience with high accuracy. First, we briefly review the main definitions of key concepts used in radio audience evaluation: audience, listener, rating, market share, daily-reach, or time spent listening. Next, we discuss two paradigms of audience and two theoretical models of mass communication used as a framework for measuring radio audience. Further, we briefly approach the issue of potential sources of error in rating data, by focusing on response error. Next, we emphasize the advantages and disadvantages of each quantitative method utilized in radio audience measurement, and present the case study of radio audience measurement in Romania. Based on a methodological critical review, we outline challenges and potential solutions for assessing more accurately radio audience. Finally, we reflect on the future of radio in digital era, and refer to new perspectives of upgrading existing research techniques. (shrink)

Gender-Based violence (GBV) is a disturbing phenomenon prevalent in all regions of the world. GBV is seen as any harmful act that is carried out against a person’s consent and that it is as a result of socially ascribed (gender) dissimilarities between males and females. The study exposes that the fight against GBV have been unsuccessful because of several factors which includes the acceptance of such actions by some traditions and cultures therefore bringing to the fore conventional ethical relativism, in (...) other words, cultural relativism. The study concluded that the rate of GBV in Nigeria is soaring high and the neglect to tackle it has made it turnout to be a shadow pandemic ravaging Nigeria. Consequently, the study amongst others recommends that the Nigerian National Assembly should enact new laws against GBV because its eradication can only be possible using the law as an instrument of social change. (shrink)

Power transients faults on high voltage lines are prominently due to high frequency transients. These transients affect the predicted life and efficiency of equipment. The Fast Fourier Transform (FFT) is helpful in analysing the effect of high frequencies and Frequency Response Analysis (FRA) provide support in diagnosis and detection of deformation in a transformers. The major aim of this study is to analyse the incorporation of frequencies based on resonating core of a particular transformer. Using transfer function method an impedance (...) change in transformer has been observed when equipment is subjected to high voltage transients. The effect of change in impedance is that it degrade the life of a core with respect to time. In this paper, research that has been done already on Transformers of different ratings i.e. 100, 50 and 30 kVA are studied and then an experiment is performed on 50-kVA transformer. It was concluded that the core of a transformer having rating equal or less than 50 kVA practically shows single resonance behavior while above 50 kVA for instance 100-kVA transformer core resonates twice. In actual, result defines the core deviating frequency with respect to the rating of a transformer. (shrink)

Routine testing is a practice whereby medical professionals ask all patients whether they would like an HIV test, regardless of whether there is anything unique to a given patient that suggests the presence of HIV. In three respects I aim to offer a fresh perspective on the debate about whether a developing country with a high rate of HIV infection morally ought to adopt routine testing. First, I present a neat framework that organises the moral issues at stake, bringing out (...) the basic principles involved and exhibiting their logical relationships. Second, appealing to the Kantian principle of respect for the dignity of persons, I offer a thorough justification for routine testing when it serves as a gateway to anti-retroviral treatment (ART). Third, I present a respect-based defence of the controversial and novel thesis that routine testing is morally justified even if ART is unaffordable or otherwise unavailable. (shrink)

Objectives To increase postmortem organ donation rates, several countries are adopting an opt-out (presumed consent) policy, meaning that individuals are deemed donors unless they expressly refused so. Although opt-out countries tend to have higher donation rates, there is no conclusive evidence that this is caused by the policy itself. The main objective of this study is to better assess the direct impact of consent policy defaults per se on deceased organ recovery rates when considering the role of (...) the family in the decision-making process. This study does not take into account any indirect effects of defaults, such as potential psychological and behavioural effects on individuals and their relatives. -/- Design Based on previous work regarding consent policies, we created a conceptual model of the decision-making process for deceased organ recovery that included any scenario that could be directly influenced by opt-in or opt-out policies. We then applied this model to internationally published data of the consent process to determine how frequently policy defaults could apply. -/- Main outcome measures We measure the direct impact that opt-in and opt-out policies have per se on deceased organ recovery. -/- Results Our analysis shows that opt-in and opt-out have strictly identical outcomes in eight out of nine situations. They only differ when neither the deceased nor the family have expressed a preference and defaults therefore apply. The direct impact of consent policy defaults is typically circumscribed to a range of 0%–5% of all opportunities for organ recovery. Our study also shows that the intervention of the family improves organ retrieval under opt-in but hinders it under opt-out. -/- Conclusions This study may warn policy makers that, by emphasising the need to introduce presumed consent to increase organ recovery rates, they might be overestimating the influence of the default and underestimating the power granted to families. (shrink)

Latent heat thermal energy storage technique has demonstrate to be a better engineering option mainly due to its benefit of supplying higher energy storage density in a smaller temperature difference between retrieval and storage. For this purpose, a micro electro-mechanical system, MEMS-based heat exchanger with microencapsulated PCM (MEPCM) slurry as cold fluid, has been simulated three dimensionally. This work investigates the influence of using MEPCM-slurry on the temperature of the cold and hot fluids. The MEPCM and water properties have been (...) considered temperature dependent. MEPCM-slurry is used with different volume fractions. The result shows that using MEPCM with 25% volume fraction leads to the improvement in fluids temperatures, that is, for hot fluid the rate of temperature reduction increases up to 23.5% and for cold fluid the rate of temperature rise decreases to 9%, compared to using only water in the MEMS. (shrink)

Bipolar neutrosophic sets are the extension of neutrosophic sets and are based on the idea of positive and negative preferences of information. Projection measure is a useful apparatus for modelling real life decision making problems. In the paper, we define projection, bidirectional projection and hybrid projection measures between bipolar neutrosophic sets. Three new methods based on the proposed projection measures are developed for solving multi-attribute decision making problems. In the solution process, the ratings of performance values of the alternatives with (...) respect to the attributes are expressed in terms of bipolar neutrosophic values. We calculate projection, bidirectional projection, and hybrid projection measures between each alternative and ideal alternative with bipolar neutrosophic information. All the alternatives are ranked to identify the best alternative. Finally, a numerical example is provided to demonstrate the applicability and effectiveness of the developed methods. Comparison analysis with the existing methods in the literature in bipolar neutrosophic environment is also performed. (shrink)

Throughout history, education has always been essential for humanity's justice and fundamental for the creation of a free and satisfying society with the dissemination of knowledge. Hence, in addition to the life occurrences educating people, traditional higher education methods have played an important role for a long period. However, the age of technology has changed the educational system along with the people's lifestyles to meet the continuously changing conditions. During the past twenty years, the Information and Communication Technologies (ICTs) led (...) to the emergence of e-learning non-traditional educational methods bringing about an innovative dimension of virtual learning. With its beneficial features provided by the Internet and various technologies, this method passes the control of time and location from the tutors to the students during the learning process. Despite the entrance of online education into the students' life many years ago, the Covid-19 outbreak initiated in 2020 turned this method into the only possible way of education for almost two years. Afterward, plenty of studies investigated its effects on students' social and academic life, mostly considering its negative impacts on their mental health and motivation due to interaction issues. They also pointed out the specific problems of tactile learner students in the lack of face-to-face design courses. As an innovative solution able to be adopted, game technologies, increasing immersion of the learner in a real-world skill, and triggering motivation are one of the best methods integrated into educational systems. Known as serious games, these games, adopted by almost any domain, convey educational content along with entertainment, increasing interaction. They train or enhance a certain skill or topic to the player by immersing them. While serious games are more expanded nowadays, they are still not being played by the mass public due to people's immersiveness expectations of a game. Hence, the Head-Mounted Displays (HMDs), with their emergence, increased the players' engagement and immersion with all types of virtual environments in digital games. This technology, however, evolved serious games area most, among others, by enabling the opportunity to provide a riskless and low-cost learning environment for real-world skills. In the process of suggesting serious games as a replacement for the traditional educational methods, the most important factor is being aware of their effectiveness for education. Generally, digital games, one of the world's biggest and fastest-growing industries, rely heavily on the virtual landscape, a major part of its development process. Its design demands remarkable effort, and it directly affects the whole game's characteristics. Hence, serious games, as a type of digital game, carry the same attributes. With the appearance of virtual reality technology, gaming started to gain more immersiveness and engagement with artificial elements and environments. Although virtual landscapes were always being developed since the emergence of digital games, the studies taking virtual landscapes into account are mostly using them as a tool for enhancing real spaces, not as a domain for the sake of its development. While other design domains are applying various design methodologies, it is challenging to find a standard design methodology for the design and production of virtual landscapes in the gaming industry, leading to inconsistent and low-quality results. Hence, it comes to the question of what is the virtual landscapes' role in serious games and their effect on the data comprehension rate. Since the literature lacks regarding the gradual change of the virtual landscape in serious games and the content they transmit, this thesis aims to figure out the gradual chronological evolution of the virtual landscapes in serious games. It also aims to seek how this change and the virtual reality technology invention have affected the contents transmitted by digital games and its influence on the player interaction and learning rate. To do so, in the process of this thesis, we developed, tested, and evaluated various serious games, both text-based and with 360° photos. The steps taken revealed the reluctance of the players to follow the providing educational content and their inadequacy of interaction. Hence, the thesis seeks the reasons ending to this result and the ways of increasing this learning rate. Afterward, the thesis uses a currently available but not massively used digital game classification methodology. This methodology, which is based on the principles of architecture, landscape architecture, and urban planning, classifies the games based on their constituting virtual landscapes. Hence, the thesis considers all Steam games, one of the biggest video game digital distribution services, filtered with the 'education' tag, ending in 2531 digital games. Afterward, adding the 'game' tag the result reduced to 1102 games. Finally, with some manual filterings ignoring the irrelevant content, the final number decreased to 702. Sorting chronologically, we made an Excel database including the games' introductory information, the content they transmit, their release date, if they support HMD or not, and whether they are simulation games or not. Due to Steam's dating spectrum, our database includes games dating back from 1992 to 2020. Afterward, we classified all of these games using the virtual landscape classification methodology and generated their related codes based on their player scale, story, dimension, space shape, and interaction level. Investigating the results, they revealed some growth patterns relating to the content educated in the game, the VR technology, and the virtual landscape evolution. Adding the user reviews of Steam, we reverse engineered five of the best serious games on the database due to the digital game design methodology. This methodology divides the games' virtual environment into five layers, namely, player activity map (PAM), story layer, natural environment layer, virtual environment layer, and media layer. Reverse engineering the five best digital games ever, based on the IGN database, enabled us to compare the outcomes. Due to released data, we understand how the games' assets are distributed within the various layers of the virtual environment in the most successful games. Finally, the thesis aims to provide a framework based on these results for developing serious games to increase player interaction and enhance the learning rate. However, these results can be expanded to any virtual landscape other than only serious games' for their interaction enhancement. Currently, the landscape architecture domain only adopts the virtual landscape area as a tool for enhancing design in the real world. However, in the near future, landscape architects will perform in the virtual landscape area as a domain to be enhanced. Hence, that day, the results of this study will play an important role and will be a roadmap for generating highly interactive virtual landscapes. (shrink)

I consider statistical criteria of algorithmic fairness from the perspective of the _ideals_ of fairness to which these criteria are committed. I distinguish and describe three theoretical roles such ideals might play. The usefulness of this program is illustrated by taking Base Rate Tracking and its ratio variant as a case study. I identify and compare the ideals of these two criteria, then consider them in each of the aforementioned three roles for ideals. This ideals program may present a (...) way forward in the normative evaluation of candidate statistical criteria of algorithmic fairness. (shrink)

Some bioethicists argue that a doctor may frame treatment options in terms of effects on survival rather than on mortality in order to influence patients to choose the better option. The debate over such framing typically assumes that the survival and mortality frames convey the same numerical information. However, certain empirical findings contest this numerical equivalence assumption, demonstrating that framing effects may in fact be due to the two frames implying different information about the numerical bounds of survival and mortality (...) rates. In this paper, I use these findings to argue that framing is presumptively wrong because it violates the duty of proper disclosure. Along the way, I highlight morally relevant features affecting the permissibility of framing, tackle three objections and draw some general lessons for the ethics of nudging. (shrink)

A reality for many information technology (IT) organizations is the need to hire IT talent from other cities or countries to supplement their employee staff. As organizations extend their software development work to remote locations, however, a distinct productivity gap can emerge between co-located and distributed teams. The problem this study addresses is the reduced productivity levels for teams practicing the Agile methodology when team members are distributed by location or time zone. Specifically, it was unknown if there are organizational (...) factors which can improve the productivity of these distributed development teams. The purpose of this study was to determine whether any amount of separation of team members impacts productivity, or if various degrees of time zone overlap allows for sufficient synchronous communication to overcome the communication lag inherit with having distributed teams. Productivity was measured by the number of story points per cycle day completed by the team. Two additional variables were collected to study the impact of sourcing and task complexity on distributed team productivity. A quantitative data analysis was conducted on a large, globally distributed technology organization practicing the Agile methodology across the Americas, Europe, Africa, Asia, and Australia. Teams were classified in the study based on the amount of time zone overlap of the team members. The results of the study found that teams which were co-located or had any amount of time zone overlap performed at similar levels of productivity, while teams with no overlapping business day experienced significantly lower levels of productivity (p < .05). Conversely, sourcing of the resources either as contractors or employees did not demonstrate a significant difference in team productivity. The data analysis on task complexity showed mixed results; there was a different level of productivity when some teams were working tasks of different complexity, but the data was further nuanced by team member location. This study indicates the importance of resource location as a key factor in team productivity. Further study on organizational design could be beneficial to better understand how organizations should select locations to optimize productivity. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass '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,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...) layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

Historic preservation, which is a vital act for conveying people’s understanding of the past, such as events, ideas, and places to the future, allows people to preserve history for future generations. Additionally, since the historic properties are currently concentrated in urban areas, an urban-oriented approach will contribute to the issue. Hence, public awareness is a key factor that paves the way for this conservation. Public history, a history with a public audience and special methods of representation, can serve society in (...) this regard. Historical video games, which get their game objectives from the past, can be considered a form of public history, raising people’s awareness and letting them gain historical experience. While plenty of studies focused on this content with various approaches, this study takes the topic under consideration with a player-oriented focus. The research uses the Steam video game digital distribution service database to explore the existing historical games based on their genre, player scale, dimension, VR compatibility attributes, and player rating. The database includes 1,148 games published between 2002 and 2022, filtered as historical by Steam tags and sorted by player review. For the data collection, the study uses the Octoparse web scraping tool for data extraction, and for the data analysis, it uses RapidMiner Studio data mining software. The study aims to provide a framework applicable to the development of commercially playable historical video games contributing to heritage preservation by raising public awareness in the context of public history. (shrink)

This paper examined managing and improving education for sustaining national income and economic growth rate in Nigeria. It attempts to explore the ways in which education can be effectively managed to lead to an improvement in national income and economic growth rate in Nigeria. Concepts relating to the topic were clearly defined. The meaning of national income, economic growth, and economic growth rate was explained. The importance and determinants of national income were identified and explained. Efforts were made to explain (...) the reasons for the slow national income growth rate in Nigeria as well as the ways to improve the national income growth rate. The role of education in sustaining national income and economic growth rate was shown using literature from other scholars. Conclusions and recommendations were made based on the observations from the study. (shrink)

This study explores the relation between pain sensitivity and the cognitive processing of words. 130 participants evaluated the pain-relatedness of a total of 600 two-syllabic nouns, and subsequently reported on their own pain sensitivity. The results demonstrate that pain-sensitive people (based on their self-report) associate words more strongly with pain than less sensitive people. In particular, concrete nouns like syringe, wound, knife, and cactus, are considered to be more pain-related for those who are more pain-sensitive. We discuss our results in (...) the light of three theoretical frameworks – cognitive bias, prototype theory, embodied account. We argue that the latter is best suited to explain the results of this study in the sense in which it implies the principle of body specificity, according to which different bodily characteristics lead to corresponding differences in the way in which people construct concepts and word meanings. (shrink)

In this essay I explore the notion of philosophy as a way of life as a recovery pathway for individuals in addiction recovery. My hypothesis is that philosophy as a way of life can be a compelling, and legitimate recovery pathway for individuals in addiction recovery, as one of many recovery pathways. I will focus on logic-based therapy applied in the context of addiction recovery. The aim of presenting a case study is to show how a client receiving LBT is (...) provided with techniques and a worldview that can contribute to a philosophically oriented recovery program. In the case study the client was advised to apply the moral philosophy of Friedrich Nietzsche as an uplifting philosophical framework to counteract his unproductive worldview and fallacious thinking. Considering that there is an ostensibly low efficacy rate for the treatment of addiction, articulating the value of philosophy as a way of life as a recovery pathway provides a conceptual and methodological framework for the development of novel philosophically-based addiction treatment and recovery-oriented programs—thus expanding the treatment and recovery options available for those seeking recovery from addiction. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

In article I explore how Logic-based Therapy (LBT) can inform a philosophically oriented recovery pathway for individuals in addiction recovery. Considering that there is an ostensibly low efficacy rate for the treatment of addiction, there is significant value in highlighting the utility of LBT for the development of novel philosophically based addiction treatment and recovery-oriented programs, which would expand the treatment and recovery options. I propose that LBT may be a suitable intervention when challenging the unrealistic conclusions derived from illogical (...) premises in practical reasoning that contribute to addiction, because it can contest irrational beliefs in a way that could mitigate the fragmentation anxiety that often arises when individuals relinquish maladaptive self-object organizations. (shrink)

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier (...) evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. (shrink)

With the rapid development of information technology, the electricity consumption of Internet Data Centers (IDCs) increases drastically, resulting in considerable carbon emissions that need to be reduced urgently. In addition to the introduction of Renewable Energy Sources (RES), the joint use of the spatial migration capacity of IDC workload and the temporal flexibility of the demand of Electric Vehicle Charging Stations (EVCSs) provides an important means to change the carbon footprint of the IDC. In this paper, a sustainability improvement strategy (...) for the IDC carbon emission reduction was developed by coordinating the spatial-temporal dispatch flexibilities of the IDC workload and the EVCS demand. Based on the Minkowski sum algorithm, a generalized flexible load model of the EVCSs, considering traffic flow and Road Impedance (RI) was formulated. The case studies show that the proposed method can effectively increase the renewable energy consumption, reduce the overall carbon emissions of multi-IDCs, reduce the energy cost of the DCO, and utilize the EV dispatching potential. Discussions are also provided on the relationship between workload processing time delay and the renewable energy consumption rate. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...) positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...) relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track the desired (...) steam that is given to the system as input for two desired steam input signals (step and sine wave). In order to analyze the performance of the system, comparison of the proposed controllers is done by simulating the system for the two reference signals for the system with and without sensor noise disturbance. Finally the comparison results prove the effectiveness of the presented process control system with model reference controller. (shrink)

This article presents a new methodology for pricing and hedging commodity derivatives. A generic model calibration is provided. The calibration procedure consists of an offline step where the mean reversion rates, the ratio of the long and short factor volatilities and the correlation between the long and short factors are determined via historical analysis. This offline step is performed relatively infrequently. There’s also an online step of the calibration which happens every time the model is used to price an (...) option or to simulate price paths. Empirical study shows that the model is quite accurate. (shrink)

The current increase of air pollution rates and its consequences of global warming, health problems and threats to human lives necessitate the continuous search for more efficient and cost effective gas monitoring devices. Devices that is easily available and implementable worldwide. In developing countries the cost and the availability of the equipment is one of the obstacles that contribute to not having an efficient monitoring system. Hence, modifying cheap and easily available devices to work as pollution monitoring system will (...) be an asset in this direction. In this work I am presenting the design of a computer based pollution detection and measuring system using cheap CO (Carbon Monoxide) alarms used in homes. The outcome is a cost-effective computer based CO monitoring system that is easily available and could be easily modified to work as a network based CO nose. A C# based program is also designed to show and log the sensor readings in an easy to use and read Window. (shrink)

On the basis of PM2.5 data of the national air quality monitoring sites, local population data, and baseline all-cause mortality rate, PM2.5-related health economic benefits of the Air Improvement Action Plan implemented in Wuhan in 2013–2017 were investigated using health-impact and valuation functions. Annual avoided premature deaths driven by the average concentration of PM2.5 decrease were evaluated, and the economic benefits were computed by using the value of statistical life (VSL) method. Results showed that the number of avoided premature deaths (...) in Wuhan are 21,384 (95% confidence interval (CI): 15,004 to 27,255) during 2013–2017, due to the implementation of the Air Improvement Action Plan. According to the VSL method, the obtained economic benefits of Huangpi, Wuchang, Hongshan, Xinzhou, Jiang’an, Hanyang, Jiangxia, Qiaokou, Jianghan, Qingshan, Caidian, Dongxihu, and Hannan District were 8.55, 8.19, 8.04, 7.39, 5.78, 4.84, 4.37, 4.04, 3.90, 3.30, 2.87, 2.42, and 0.66 billion RMB (1 RMB = 0.1417 USD On 14 October 2019), respectively. These economic benefits added up to 64.35 billion RMB (95% CI: 45.15 to 82.02 billion RMB), accounting for 4.80% (95% CI: 3.37% to 6.12%) of the total GDP of Wuhan in 2017. Therefore, in the process of formulating a regional air quality improvement scheme, apart from establishing hierarchical emission-reduction standards and policies, policy makers should give integrated consideration to the relationship between regional economic development, environmental protection and residents’ health benefits. Furthermore, for improving air quality, air quality compensation mechanisms can be established on the basis of the status quo and trends of air quality, population distribution, and economic development factors. (shrink)

RESUMO A inseminação artificial em tempo fixo (IATF) é uma das biotecnologias de reprodução mais estudadas dos últimos anos, e a busca pelo equilíbrio entre a fisiologia animal e o controle hormonal fomenta uma série de estudos. Sendo assim, o objetivo com este trabalho foi avaliar a eficiência da aplicação do hormônio liberador de gonadotrofinas (GnRH) no dia zero (D0) do protocolo de IATF, a base de estrógeno e progesterona sobre a taxa de penhez por IATF. O experimento foi (...) desenvolvido na Universidade Federal de Uberlândia (UFU), na Fazenda Experimental do Glória, localizada no município de Uberlândia, no estado de Minas Gerais. Foram avaliadas 69 inseminações de vacas leiteiras mestiças, divididas em dois grupos com diferentes protocolos; Controle: sem aplicação de GnRH e Tratamento: aplicação de GnRH no dia zero do protocolo. Todos os animais foram inseminados com sêmen previamente analisado. O diagnóstico de gestação foi realizado 32 dias após a inseminação, com aparelho de ultrassonografia equipado com probe linear transretal de 7,5 MHz. Os dados foram analisados por análise descritiva, sendo avaliado o efeito da relação protocolo e presença de corpo lúteo no início do protocolo e efeito da relação protocolo e ordem de IA Pós-Parto sobre a taxa de concepção. A taxa de concepção após a realização do Protocolo Tratamento foi de 53% (17/32) e do Protocolo Controle foi de 43% (16/37). Portanto, conclui-se que o protocolo de sincronização de vacas leiteiras mestiças com a aplicação de GnRH no dia zero do protocolo resultou em um aumento no número da taxa de concepção de vacas leiteiras mestiças. Palavras-chave: biotecnologia; bovinocultura; hormônio; ovulação; reprodução. ABSTRACT The artificial insemination at fixed time (AIFT) is one of the most studied reproduction biotechnologies in recent years, and the search for a balance between animal physiology and hormonal control fosters a series of studies. Therefore, the objective of this work was to to evaluate the efficiency of gonadotrofine hormone inducer (GnRH) on day zero (D0) of the AIFT protocol, an estrogen and progesterone base on an AIFT rate. The experiment was developed at the Federal University of Uberlândia (UFU), at the Experimental Farm of Glória, located in the municipality of Uberlândia, in the state of Minas Gerais. Were evaluated 69 inseminations of crossbred dairy cows divided into two groups with different protocols; Control: without application of GnRH and Treatment: application of GnRH on day zero of the protocol. All animals were inseminated with previously analyzed semen. The diagnosis of pregnancy was performed 32 days after insemination, with an ultrasound device equipped with a 7.5 MHz transrectal linear probe. The data were analyzed by descriptive analysis, evaluating the effect of the protocol relationship and the presence of corpus luteum at the beginning of the protocol and the effect of the protocol relationship and postpartum AI order on the conception rate. The conception rate after performing of the Treatment Protocol was 53% (17/32) and the Control Protocol was 43% (16/37). Therefore, it is concluded that the synchronization protocol of crossbred dairy cows with the application of GnRH on day zero of the protocol resulted in an increase in the number of conception rates of crossbred dairy cows. Keywords: biotechnology; cattle breeding; hormone; ovulation; reproduction. -/- REFERÊNCIAS BIBLIOGRÁFICAS ATANASOV, A. S.; DINEVA, J. D.; YOTOV, S. A. 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Measuring the effectiveness of medical interventions faces three epistemological challenges: the choice of good measuring instruments, the use of appropriate analytic measures, and the use of a reliable method of extrapolating measures from an experimental context to a more general context. In practice each of these challenges contributes to overestimating the effectiveness of medical interventions. These challenges suggest the need for corrective normative principles. The instruments employed in clinical research should measure patient-relevant and disease-specific parameters, and should not be sensitive (...) to parameters that are only indirectly relevant. Effectiveness always should be measured and reported in absolute terms (using measures such as 'absolute risk reduction'), and only sometimes should effectiveness also be measured and reported in relative terms (using measures such as 'relative risk reduction')-employment of relative measures promotes an informal fallacy akin to the base-rate fallacy, which can be exploited to exaggerate claims of effectiveness. Finally, extrapolating from research settings to clinical settings should more rigorously take into account possible ways in which the intervention in question can fail to be effective in a target population. (shrink)

—One of the promising applications of wireless sensor networks (WSNs) is monitoring of the human body for health concerns. For this purpose, a large number of small sensors are implanted in the human body. These sensors altogether provide a network of wireless sensors (WBANs) and monitor the vital signs and signals of the human body; these sensors will then send this information to the doctor. The most important application of the WBAN is the implementation of the monitoring network for patient (...) safety in the hospital environment. In this case, supporting patients’ mobility is one of the basic needs, which has been underestimated in recent studies. The problem that involves providing the required energy for the units used in this type of network is challenging; for this reason, sent/ received units with very low power consumption and with a very small radius are used in order to save energy. The resulting small sending range, leads to the lack of support for patients' mobility. In this paper, the AD HOC mode is suggested for use to establish a network and a multi-path routing algorithm, for the purpose of importing patients’ mobility in hospital setting. The results of the simulation show that in addition to supporting patients' mobility, the use of the proposed idea instead of previously presented protocols, reduces delays in data transmission and energy consumption; and it also increases the delivery rate depending on the destination and the lifetime of the network, while on the other hand, it increases routing overhead. (shrink)

Economic diversification has been the glamour of successive administrations in Nigeria, especially amidst the dwindling oil-revenue in recent years, which has resulted from the fluctuations in world crude oil prices. This study aims at investigating the impact of diversifying the economy on the economic growth in Nigeria. Secondary data on GDP growth rate as a proxy for economic growth, non-oil GDP as a proxy for GDP diversification, non-oil export as a proxy for export diversification, investment and exchange rate, between 1981 (...) and 2016, were adopted in the study. An econometric approach of Ordinary Least Squares (OLS) was adopted to empirically analyze the collected data and the result revealed that non-oil gross domestic product impacted positively and significantly on economic growth while exchange rate had an inverse but significant nexus on economic growth in Nigeria, within the period covered in the study. However, non-oil export and investment impacted positively but insignificantly on economic growth in Nigeria. The study recommends the encouragement of increased productivity in the real sector as well as the adoption of stable and favourable exchange rate policies by the government in order to accelerate economic growth in Nigeria. (shrink)

​I evaluate a suggestion, floated by Kimberly Ferzan (this volume), that the twitter hashtag campaign #BelieveWomen is best accommodated by non-reductionist views of testimonial justification. I argue that the issue is ultimately one about the ethical obligation to trust women, rather than a question of what grounds testimonial justification. I also suggest that the hashtag campaign does not simply assert that ‘we should trust women’, but also militates against a pernicious striking-property generic (roughly: ‘women make false sexual assault accusations’), that (...) distorts our evaluation of women’s testimony concerning sexual assault. I conclude #BelieveWomen does not demand that we believe against the evidence, or uncritically, or be more trusting than we have evidential justification to be. Rather, it aims to bring our trust closer to what is merited by the base-rate of reliable testimony from women concerning sexual assault. (shrink)

I review recent empirical findings on knowledge attributions in lottery cases and report a new experiment that advances our understanding of the topic. The main novel finding is that people deny knowledge in lottery cases because of an underlying qualitative difference in how they process probabilistic information. “Outside” information is generic and pertains to a base rate within a population. “Inside” information is specific and pertains to a particular item’s propensity. When an agent receives information that 99% of all (...) lottery tickets lose (outside information), people judge that she does not know that her ticket will lose. By contrast, when an agent receives information that her specific ticket is 99% likely to lose (inside information), people judge that she knows that her ticket will lose. Despite this difference in knowledge judgments, people rate the likelihood of her ticket losing the exact same in both cases (i.e. 99%). The results shed light on other factors affecting knowledge judgments in lottery cases, including formulaic expression and participants’ own estimation of whether it is true that the ticket will lose. The results also undermine previous hypotheses offered for knowledge denial in lottery cases, including the hypotheses that people deny knowledge because they either deny justification or acknowledge a chance for error. (shrink)

Worrall argued that structural realism provides a ‘synthesis’ of the main pro-realist argument – the ‘No Miracles Argument’, and the main anti-realist argument – the ‘Pessimistic Induction’. More recently, however, it has been claimed that each of these arguments is an instance of the same probabilistic fallacy – sometimes called the ‘base-rate fallacy’. If correct, this clearly seems to undermine structural realism and Magnus and Callender have indeed claimed that both arguments are fallacious and ‘without [them] we lose the (...) rationale for … structural realism ’. I here argue that what have been shown to be fallacious are simply misguided formalisations of ‘the’ arguments and that when they are properly construed they continue to provide powerful motivation for favouring structural realism. (shrink)

This essay is an accessible introduction to the proof paradox in legal epistemology. -/- In 1902 the Supreme Judicial Court of Maine filed an influential legal verdict. The judge claimed that in order to find a defendant culpable, the plaintiff “must adduce evidence other than a majority of chances”. The judge thereby claimed that bare statistical evidence does not suffice for legal proof. -/- In this essay I first motivate the claim that bare statistical evidence does not suffice for legal (...) proof. I then introduce and motivate a knowledge-centred explanation of this fact. The knowledge-centred explanation rests on two premises. The first is that legal proof requires knowledge of culpability. The second is that one cannot attain knowledge that p from bare statistical evidence that p. To motivate the second premise, I suggest that beliefs based on bare statistical evidence fail to be safe—they could easily be wrong—and bare statistical evidence cannot eliminate relevant alternatives. -/- I then cast doubt on the first premise; I argue that legal proof does not require knowledge. I thereby dispute the knowledge-centred explanation of the inadequacy of bare statistical evidence for legal proof. Instead of appealing to the nature of knowledge, I suggest we should seek a more direct explanation by appealing to those more foundational epistemic properties, such as safety or eliminating relevant alternatives. (shrink)