The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi’s (...) Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

This study examines the dynamic relationship between face-saving mechanisms—proxied by age, income, and gender—and the peers’ safe-driving information on the driving behaviors of Chinese drivers. Using the Bayesian Mindsponge Framework (BMF) and Granular Interaction Thinking Theory (GITT) to analyze data from 1,039 Chinese drivers, we uncover a complex interplay of factors. Our findings suggest that peers serving as role models and actively supporting careful driving positively influence drivers’ safe driving behaviors. The effect of role-model peers is strengthened among (...) drivers with higher age and income levels. In contrast, the impact of actively engaged peers is lower among these groups and male drivers (though the gender’s negative moderation effect is weakly reliable). These results highlight the crucial role of peer influence in promoting safe driving and underscore the dynamic effects of cultural values, particularly face-saving concerns, on drivers’ cognitive and behavioral processes in China. (shrink)

Given the reproducibility crisis (or replication crisis), more psychologists and social-cultural scientists are getting involved with Bayesian inference. Therefore, the current article provides a brief overview of programs (or software) and steps to conduct Bayesian data analysis in social sciences.

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

Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 males, (...) 705 females) with 2,410 publications in the 2008 – 2018 period was extracted and analyzed. Various factors were considered including age, gender, new authors, leading authors, co-authorship, and Impact Factor. The findings suggest a high level of contribution from authors at the age of 40 – 44 in economics (858 publications) in a 12-years period, which is equivalent to the social medicine total output, and two times more than the total output of the education. Moreover, the presence and reinforcement of male researchers are still dominating in Economics and other fields, with the only exception of education. Despite the rapid rise in the number of Vietnamese lead authors, gender disparity among disciplines is an issue. Contrary to the strong international collaboration-oriented tendency in social medicine, economics, and other fields, educational authors are not open to international collaborating. Finally, most of the publications in economics belong to the group with JIF from 0 to 2, in contrast with the high number of social medicine publications with JIF from 2 to 5, which suggesting the field of economics is fulfilling the quantity, but still, need more quality publications. (shrink)

The gambler’s fallacy is the tendency to expect random processes to switch more often than they actually do—for example, to think that after a string of tails, a heads is more likely. It’s often taken to be evidence for irrationality. It isn’t. Rather, it’s to be expected from a group of Bayesians who begin with causal uncertainty, and then observe unbiased data from an (in fact) statistically independent process. Although they converge toward the truth, they do so in an (...) asymmetric way—ruling out “streaky” hypotheses more quickly than “switchy” ones. As a result, the majority (and the average) exhibit the gambler’s fallacy. If they have limited memory, this tendency persists even with arbitrarily-large amounts of data. Indeed, such Bayesians exhibit a variety of the empirical trends found in studies of the gambler’s fallacy: they expect switches after short streaks but continuations after long ones; these nonlinear expectations vary with their familiarity with the causal system; their predictions depend on the sequence they’ve just seen; they produce sequences that are too switchy; and they exhibit greater rates of gambler’s reasoning when forming binary predictions than when forming probability estimates. In short: what’s been thought to be evidence for irrationality may instead be rational responses to limited data and memory. (shrink)

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if (...) in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method’s sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results. (shrink)

For centuries, fiction—particularly fables—has seamlessly combined storytelling, moral lessons, and societal reflections to engage readers on both emotional and intellectual levels. Despite extensive research on the benefits of reading and the emotional responses it evokes, a critical gap remains in understanding what drives readers to form deep emotional connections with specific works. This study seeks to identify the characteristics of a book that foster such connections. Using Bayesian Mindsponge Framework analytics, we analyzed a dataset of 129 Amazon reviews of (...) Wild Wise Weird, a collection of 42 fables that intertwine traditional storytelling with contemporary sensibilities, offering life lessons, humor, and social commentary. Of these reviews, approximately 66% expressed love for the book. The findings reveal that readers who describe the book as unique, whimsical, quirky, or innovative are more likely to express affection for it. Similarly, readers who are drawn to the book’s illustrations and characters tend to form stronger emotional connections. The cultural richness and representativeness of the book also emerged as significant factors in fostering readers’ love. The book successfully captures and conveys the depth, charm, and moral essence of Vietnamese culture to a global audience. Readers have expressed heartfelt gratitude after engaging with the book, underscoring its success in fostering meaningful emotional connections. Thus, insights from this analysis offer actionable recommendations for stakeholders in the literary ecosystem, from authors and publishers to marketers, to enhance readers’ emotional attachment and commitment to books. (shrink)

Precognition is an anomaly in information transmission and interpretation. Extant literature suggests that paranormal beliefs and gender may have significant influences on this unknown information process. This study examines the effects of these two factors, including their interactions, on precognition performance by employing the Bayesian Mindsponge Framework (BMF) analytics. Using Bayesian analysis on secondary data of 60 participants, we found that men may have higher chances to score a hit in a precognition task compared to women. (...) Interestingly, stronger beliefs in the paranormal may decrease the success probability in performing precognition tasks. Considering the interactions between the two factors, the effect of paranormal beliefs on precognition task performance is stronger in men than women. Using mindsponge-based reasoning, we argue that paranormal beliefs may increase the interference of imagination in the reception of hypothetical precognitive information. Women tend to rely more on intuition, which may lessen the interference effect of imagination on hypothetical psi reception. Based on the findings, we suggest that researchers should be careful when assessing participants’ psi potential for experiments. We also demonstrate some advantages of utilizing the BMF in parapsychological research. (shrink)

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

Market crises amplify fear, disrupting rational decision-making of stock investment. This study examines the relationship between investors’ information priorities—such as intuition, company performance, technical analysis, and other factors—and their fear responses (freeze, flight, and hiding) during market crashes. Using the Bayesian Mindsponge Framework (BMF) to analyze data from 1,526 investors in China and Vietnam, the findings reveal complex dynamics. We found positive associations between investors’ prioritization of social influence and intuition for investment decision-making with being freeze (i.e., (...) not knowing what to do with the owned stock), between prioritization of professionals’ evaluation and intuition with fleeing (i.e. giving up stock investment permanently), and between prioritization of professionals’ evaluation and social influence with hiding (i.e., stopping investing in stock for a while). In contrast, prioritization of companies’ performance metrics (e.g., net profit, debt, etc.) was negatively associated with being freeze, fleeing, and hiding. Meanwhile, prioritization of professionals’ evaluation, technical analysis, and liquidity was negatively associated with being freeze, fleeing, and hiding, respectively. The study highlights the importance of disseminating transparent, accurate information through government and media channels, along with implementing financial education programs, to promote rational investment decisions and reduce fear-driven behaviors. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

International trade enables countries to expand their markets, access more products, improve resource allocation, and boost economic growth by leveraging comparative advantage and specialization. The aim of this article is to analyze the primary factors that influence Malawi’s international trade flows. The study is relevant because it examines Malawi’s trade patterns with its main partners, which include surrounding nations and traditional trade allies. The novelty is that, through the analysis, the research offers valuable insights into the primary factors that (...) influence Malawi’s international commerce. Panel data is gathered from various sources, including the International Monetary Fund (IMF) and World Bank, covering the period from 2000 to 2023 for 11 countries that are trading partners with Malawi. We employ the Bayesian Mindsponge gravity methodology. The results show that the economic size (characterized by the gross domestic product) of the origin and destination countries, bilateral agreements, and the population size of the destination country have a beneficial impact on Malawi’s exports. In conclusion, Malawi’s export performance is significantly enhanced by the economic size of both Malawi and its trading partners, the existence of bilateral agreements, and the population size of the destination countries, underscoring the importance of strategic economic partnerships and targeted trade policies in boosting Malawi’s international trade. We recommend Malawi authorities focus on strengthening bilateral agreements and targeting trade relationships with larger and more populous economies to boost international trade. (shrink)

The inconsistent relation between word frequency and free recall performance and the non-monotonic relation found between the two cannot all be explained by current theories. We propose a theoretical framework that can explain all extant results. Based on an ecological psychology analysis of the free recall situation in terms of environmental and informational resources available to the participants, we propose that because participants’ cognitive system has been shaped by their native language, free recall performance is best understood as the (...) end result of relational properties that preexist the experimental situation and of the way the words from the experimental list interact with those. In addition to this, we borrow from predictive coding theory the idea that the brain constantly predicts “what is coming next” so that it is mainly prediction errors that will propagate information forward. Our ecological psychology analysis indicates there will be “prediction errors” because the word frequency distribution in an experimental word list is inevitably different from the particular Zipf’s law distribution of the words in the language that shaped participants’ brains. We further propose the particular distributional discrepancies inherent to a given word list will trigger, as a function of the words that are included in the list, their order, and of the words that are absent from the list, a surprisal signal in the brain, something that is isomorphic to the concept of Bayesian surprise. The precise moment when Bayesian surprise is triggered will determine to what word of the list that Bayesian surprise will be associated with, and the word the Bayesian surprise will be associated with will benefit from it and become more memorable as a direct function of the magnitude of the surprisal. Two experiments are presented that show a proxy of Bayesian surprise explains the free recall performance and that no effect of word frequency is found above and beyond the effect of that proxy variable. We then discuss how our view can account for all data extant in the literature on the effect of word frequency on free recall. (shrink)

ABSTRACT In the present study, I explore the relationship between purpose, which was measured by the Claremont Purpose Scale, and moral psychological indicators, moral reasoning, moral identity, and empathy. Purpose was quantified in terms of three subcomponents: meaning, goal, and beyond-the-self motivation. Moral reasoning was assessed in terms of utilization of postconventional moral reasoning. Moral identity was examined with two subscales: moral internalization, and symbolization. Among diverse subscales of empathy, I focused on empathic concern and perspective taking, which have been (...) reported to be strongly related to morality. To explore the best prediction models using the data, I employed Bayesian Model Selection and Bayesian regression analysis. In general, purpose was significantly predicted by most surveyed moral psychological indicators but not by moral symbolization. The best prediction model for beyond-the-self motivation included the most moral psychological indicator predictors including moral reasoning, which did not significantly predict other dependent variables. (shrink)

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., (...) class='Hi'>Bayesian model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

Our society is undergoing digital change. Dealing with digital technologies has become a daily practice. Many healthcare facilities are implementing digital technologies. Nurses are placed in a strategic position to be the leader of the digital healthcare workforce. Nursing students are more exposed to this technological advancement as they are future professional nurses. This study aimed to examine how information-processing and exchanging skills in digital spaces affect digital problem-solving skills among nursing students. The Bayesian mindsponge framework (BMF) was used (...) as a conceptual framework. This quantitative study utilized a cross-sectional design. The population was all nursing students in two private nursing institutions located in Bangkok, Thailand (n=476) and Surabaya, Indonesia (n=179). Samples were the total population. Self-developed instrument was used in data collection. Data analysis used BMF analytics. The majority were females aged 20-22 years old who use smartphones and tablets daily. The information-processing and information-exchanging skills in digital spaces were positively associated with digital problem-solving skills among nursing students (MInformationProcess=0.30 and SDInformationProcess=0.05; MInformationExchange=0.37, and SDInformationExchange=0.05). More efforts are needed to improve nursing students’ problem-solving skills by using digital content, for instance, by modifying the information-processing and information-exchanging skills in digital spaces. Collaborative learning methods should be promoted in order to support the creation of conducive environment for supporting knowledge or information-exchanging behavior among nursing students by using digital technology. (shrink)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is (...) used to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

ABSTRACT In the present study, we examined how the perceived attainability and relatability of moral exemplars predicted moral elevation and pleasantness among both adult and college student participants. Data collected from two experiments were analyzed with Bayesian multilevel modeling to explore which factors significantly predicted outcome variables at the story level. The analysis results demonstrated that the main effect of perceived relatability and the interaction effect between attainability and relatability shall be included in the best prediction model, (...) and thus, were deemed to predict the outcome variables significantly. The main effect of relatability as well as its interaction with attainability positively predicted elevation and pleasantness. We discussed educational implications of the findings in terms of how relatability may be the first point of emphasis for moral educators to focus on and attainability can then bolster the effectiveness. These relatable and attainable moral exemplars can be sources for moral elevation and pleasantness, which promote motivation to emulate moral behavior presented by the exemplars. (shrink)

In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but less-studies (...) topic with NeuroSynth, a tool for large-scale brain image analysis, while addressing issues associated with reverse inference. The result from analysis demonstrated that brain regions in the cerebellum, the right Crus I and Crus II in particular, were specifically associated with morality in general. I discussed the potential implications of the finding based on clinical and functional neuroimaging studies of the cerebellum, emotional functioning, and neural networks for diverse psychological processes. (shrink)

Currently, under the conditions of permanent financial risks that hamper the sustainable economic growth in the financial sector, the development of evaluation and risk management methods both regulated by Basel II and III and others seem to be of special importance. The reputation risk is one of significant risks affecting reliability and credibility of commercial banks. The importance of reputation risk management and the quality of their assessment remain relevant as the probability of decrease in or loss of business reputation (...) influences the financial results and the degree of customers’, partners’ and stakeholders’ confidence. By means of imitating modeling based on Bayesian Networks and the fuzzy data analysis, the article characterizes the mechanism of reputation risk assessment and possible losses evaluation in banks by plotting normal and lognormal distribution functions. Monte-Carlo simulation is used to calculate the probability of losses caused by reputation risks. The degree of standardized histogram similarity is determined on the basis of the fuzzy data analysis applying Hamming distance method. The tree-like hierarchy based on the OWA-operator is used to aggregate the data with Fishburne's coefficients as the convolution scales. The mechanism takes into account the impact of criteria, such as return on equity, goodwill value, the risk assets ratio, the share of the productive assets in net assets, the efficiency ratio of interest bearing liabilities, the risk ratio of credit operations, the funding ratio and reliability index on the business reputation of the bank. The suggested methods and recommendations might be applied to develop the decision-making mechanism targeted at the implementation of reputation risk management system in commercial banks as well as to optimize risk management technologies. (shrink)

Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we (...) show how information and communication technologies enhance the reliability, scalability, and connectivity of crowdsourced e-research, giving online citizen science projects powerful epistemic advantages over more traditional modes of scientific investigation. These results highlight the essential role played by technologically mediated social interaction in contemporary knowledge production. We conclude by calling for an explicitly sociotechnical turn in the philosophy of science that combines insights from statistics and logic to analyse the latest developments in scientific research. (shrink)

The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I am (...) skeptical about algorithmic homuncularism, and I argue against it by focusing on recent methods for complex data analysis in systems neuroscience. I claim that analyses such as principle components analysis and linear discriminant analysis prevent individuating vehicles as algorithmic homuncularism recommends. Rather, each individual part contributes to a global state space, trajectories of which vary with important task parameters. I argue that, while homuncularism is false, this view still supports a kind of “vehicle realism,” and I apply this view to debates about the explanatory role of representation. (shrink)

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and developments (...) and superior accuracy in sample reputation.Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. (shrink)

In light of Taiwan's extensive exposure to the Chinese Communist Party's "cognitive domain infiltration warfare," this paper proposes new response mechanisms and strategies for cybersecurity and national defense. The focus is primarily on assessing the CCP's cognitive infiltration tactics to develop policy recommendations in cybersecurity linguistics. These recommendations are intended to serve as a reference for future national defense and information security policies. Within the constraints of limited resources, this study attempts to provide an integrated analysis method combining qualitative (...) and quantitative tools. This method involves text mining in cybersecurity linguistics, focusing on "textual content" and incorporating discourse deconstruction. The aim is to enhance the success rate in identifying and defining contentious information in cognitive warfare, thereby offering policy suggestions. -/- Keywords: Cognitive Infiltration, Big Data Corpus, Cybersecurity Linguistics, Text Mining Analysis, Narrative Language Deconstruction. (shrink)

There is a need to rapidly assess the impact of new technology initiatives on the Counter Improvised Explosive Device battle in Iraq and Afghanistan. The immediate challenge is the need for rapid decisions, and a lack of engineering test data to support the assessment. The rapid assessment methodology exploits available information to build a probabilistic model that provides an explicit executable representation of the initiative’s likely impact. The model is used to provide a consistent, explicit, explanation to decision makers (...) on the likely impact of the initiative. Sensitivity analysis on the model provides analytic information to support development of informative test plans. (shrink)

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a result (...) of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited. (shrink)

Coastal environments are widely recognized as valuable public health resources and therapeutic landscapes. However, limited research has examined how specific coastal interactions that foster close connections with nature influence health outcomes. This study investigates the relationship between the frequency of engaging in high-nature-interaction coastal activities―e.g., beach walking, wildlife spotting, water sports, mountain biking, spending time on the beach, beach sports, watching the sunset, seagoing, and shell collecting― and health outcomes among visitors to the Belgian coast. Using a dataset of 1,939 (...) responses, we analyze how the frequencies of these activities over the past year are associated with self-reported mental and general health. Grounded in the Granular Interaction Thinking Theory (GITT) and employing Bayesian GITT Framework analytics, the study identifies distinct patterns in these associations. While frequent engagement in activities, e.g., beach walking and beach sports, is positively linked to both mental and general health, spending time on the beach and shell collecting are unexpectedly associated with negative health outcomes. Watching the sunset is negatively associated with general health, while mountain biking and water sports show negative associations with mental health. In contrast, wildlife spotting exhibits a positive association with mental well-being. These findings highlight the complex interplay of environmental factors, visitor perceptions, and biophysical-psychological interactions in shaping health outcomes. The study emphasizes the need for targeted coastal management strategies to enhance the therapeutic benefits of blue spaces and calls for further research to elucidate the mechanisms underlying these associations. (shrink)

The article summarizes the software tool on astrophysical analysis with multi-wavelength space telescope data. It recaps the evidence analysis conducted on the Kerr-Newman black hole (KNBH). It was written prior to the article Research on the Kerr-Newman Black Hole in M82 Confirms Black Hole and White Hole Juxtapose not soon after the experiment. The conducted analysis suggested Hawking radiation is caused by the movement of ergosurfaces of the BH and serves as the primal evidence for black (...) hole and white hole juxtapose. A later data exploration was conducted with the radiation trails in the multi-wavelength data. The evidences produced corroborates with Yale professor Priyamvada Natarajan's black hole seeds theory. It implies that the electromagnetic dynamic of fusion and fission temperature determines the pressure of surface tension on the macro particles, and the mass density of the BH is determined by the electromagnetic tension between the outer ergosurface and inner ergosurface. The ring singularity of the BH and the Penrose-Hawking singularity of the BH determine the spin and gravitational singularity of the BH. Inside the ergosurfaces, the BH singularities' backward inflow causes the vicinity's rate on feeding the BH. It makes the active galactic nuclei (AGN) a semi-closed system. The density pressure is released on threshold. During the mass exchange observed by energy momentum upon the AGN's open, the macroparticle composition of the BH changes with the galactic event. This causes the expansion rate of the galaxy and contraction rate of the BH. AGN types and their relative motions determine the expansion rate of the cosmic universe in a generalized quantitative thinking. The article concludes that BH spin is caused by the asymmetric motions of AGN in the BH system. A set of the nuclear resonance caused by BH and white hole (WH) oscillation is processed with the same set of data. (shrink)

Art and Culture are very important aspects of humanity. However, due to their abstract nature, attempts to quantify the value of such fields have been the challenges for the scientific community. Recently, a new work of Vuong et al. (2019) presents an approach that sheds light on the possibility of applying Bayesian networks analysis to clarify the connection between architecture, for example, the design of the house façade and cultural evolution in Vietnamese city in the early 20th century.

Rapid urbanization in Vietnam significantly impacts the environment and human well-being. Public parks are crucial for enhancing social and environmental sustainability in urban areas, yet their establishment and expansion require substantial funding. This study investigates the factors influencing Vietnamese urban residents’ willingness to donate to planting projects in public parks, utilizing the Bayesian Mindsponge Framework (BMF), which combines Mindsponge Theory’s informational entropy-based notion of value with Bayesian analysis. Analyzing data from 535 residents in major Vietnamese cities, (...) we found that while men visit public parks more frequently, they are less willing to donate to planting projects. Older residents visit parks more often and are more willing to donate, while higher-income individuals show greater willingness to donate. Most motivations—such as relaxation, physical activities, nature enjoyment, socializing, and family time—are positively linked to park visit frequency and indirectly to donation willingness. However, only relaxation and enjoyment of nature are directly associated with willingness to donate. Interestingly, higher education levels and visiting parks for children’s educational activities are associated with lower donation willingness. These findings hint at the possible existence of the free-riding problem, gender inequality, ineffective environmental education, and lack of awareness of public parks’ natural value, as well as opportunities to cultivate an eco-surplus culture through nature interactions in public parks. (shrink)

Data cleaning and preprocessing are fundamental steps in the data analysis pipeline. These processes involve transforming raw data into a usable format by identifying and rectifying inconsistencies, errors, and missing values. Given the importance of data quality in achieving accurate and reliable analytical results, understanding the best practices for these stages is crucial. This paper outlines key techniques for data cleaning and preprocessing, including handling missing data, detecting and managing outliers, data normalization, (...) encoding categorical variables, and dealing with noisy data. Additionally, it explores the importance of these practices in ensuring robust and insightful analysis. (shrink)

Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (CO), (...) sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃), across different geographic locations. The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. (shrink)

We argue that concerns about double-counting—using the same evidence both to calibrate or tune climate models and also to confirm or verify that the models are adequate—deserve more careful scrutiny in climate modelling circles. It is widely held that double-counting is bad and that separate data must be used for calibration and confirmation. We show that this is far from obviously true, and that climate scientists may be confusing their targets. Our analysis turns on a Bayesian/relative-likelihood approach (...) to incremental confirmation. According to this approach, double-counting is entirely proper. We go on to discuss plausible difficulties with calibrating climate models, and we distinguish more and less ambitious notions of confirmation. Strong claims of confirmation may not, in many cases, be warranted, but it would be a mistake to regard double-counting as the culprit. 1 Introduction2 Remarks about Models and Adequacy-for-Purpose3 Evidence for Calibration Can Also Yield Comparative Confirmation3.1 Double-counting I3.2 Double-counting II4 Climate Science Examples: Comparative Confirmation in Practice4.1 Confirmation due to better and worse best fits4.2 Confirmation due to more and less plausible forcings values5 Old Evidence6 Doubts about the Relevance of Past Data7 Non-comparative Confirmation and Catch-Alls8 Climate Science Example: Non-comparative Confirmation and Catch-Alls in Practice9 Concluding Remarks. (shrink)

Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets. Association rule mining is a type of data mining process, which is indented to determine interesting associations between items and to establish a set of association rules whose support is greater than a specific threshold. The classical association rules can only be extracted from (...) binary data where an item exists in a transaction, but it fails to deal effectively with quantitative attributes, through decreasing the quality of generated association rules due to sharp boundary problems. In order to overcome the drawbacks of classical association rule mining, we propose in this research a new neutrosophic association rule algorithm. The algorithm uses a new approach for generating association rules by dealing with membership, indeterminacy, and non-membership functions of items, conducting to an efficient decision-making system by considering all vague association rules. To prove the validity of the method, we compare the fuzzy mining and the neutrosophic mining. The results show that the proposed approach increases the number of generated association rules. (shrink)

Five years ago, on May 24, 2019, the computer program ‘bayesvl’ was officially published on R under the name “bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with ‘Stan’”. This program (or package) was developed by two founders of the SM3D Portal, Vuong Quan Hoang and La Viet Phuong, to improve the productivity of conducting social research. The package was designed with a pedagogical orientation, supporting users in familiarizing themselves with Bayesian statistical methods, MCMC (...) simulation, and visualization of technical diagnoses and results. (shrink)

Artificial intelligence (AI) has been incorporated into financial data analysis at a rapid pace, resulting in the creation of extremely complex models that can process large volumes of data and make important choices like credit scoring, fraud detection, and stock price projections. But these models' complexity—particularly deep learning and ensemble methods—often leads to a lack of transparency, which makes it challenging for stakeholders to comprehend the decision-making process. This opacity has the potential to erode public confidence in (...) AI systems, especially in the financial industry where choices can have big financial repercussions. With an emphasis on financial data analysis, this study explores different approaches to visualizing the decision-making processes of complicated artificial intelligence systems. We investigate various methods of interpretability such as heatmaps, decision trees, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and feature importance metrics. These techniques give financial professionals a greater understanding of and confidence in AI-driven judgments by providing means to improve the transparency and comprehensibility of AI systems. The trade-offs between interpretability and model accuracy, the difficulties with bias and fairness in financial AI, and the significance of maintaining security and privacy in visualization techniques are also covered in the study. Finally, we suggest a paradigm for strengthening the trustworthiness of AI in finance, balancing the requirement for accurate forecasts with openness and ethical considerations. (shrink)

This study analyses behavioral psychological facilitators and barriers to using different carsharing business models. It identifies the most preferable carsharing business models for different trip purposes as well as the main motivators for using it. Users of carsharing services (N=1,121) in German cities completed a questionnaire distributed by five operators representing three different business models: freefloating (FF), round-trip station-based (RTSB), and peer-to-peer (P2P). All analyses are performed from a Bayesian perspective and further discussion of the statistical analyses is included. (...) The main results indicate that there are different preferences for carsharing business models depending on the trip purpose, with a trade-off between free-floating and round-trip station-based business models. The peer-to-peer business model stood out for short holiday trips. Age, educational level, and income affected the probability of selecting different carsharing operators. Users of FF and RTSB differ regarding driving habits and trust in the services. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of (...) inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

Appropriate measurement scales are fundamental in data analysis, allowing researchers to categorise, select appropriate statistical methods, and analyse and interpret their data accurately. The nominal scale is one such measurement scale in behavioural sciences, which is crucial in organising data into distinct categories. This paper provides an overview of the nominal measurement scale in research data analysis. It explains the characteristics and role of the nominal scale in organising data into distinct categories. The (...) paper discusses methods of collecting nominal scale data, including surveys and observations. It explores the use of the nominal scale in descriptive (such as frequency counts, measures of dispersion and central tendencies), and inferential statistics (such as point biserial correlation, independent t-test, analysis of variance, logistic regression, discriminant analysis, differential item functioning, chi-square test of independence, Kruskal-Walli’s test, and Mann-Whitney U Test). Each technique is explained with assumptions and application areas. In conclusion, the paper emphasises the significance of the nominal scale in data analysis and its contribution to various statistical techniques. It serves as a comprehensive guide for researchers and practitioners looking to understand and utilise the nominal measurement scale in their data analysis. (shrink)

The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...) results of the main empirical research carried out in recent years, in particular those conducted with a promising methodology: Digital Trace Data Analysis. (shrink)

The usefulness of the statistic known as the p-value, as a means of quantify-ing the strength of evidence for the presence of an effect from empirical data has long been questioned in the statistical community. In recent years there has been a notable increase in the awareness of both fundamental and practical limitations of the statistic within the target research fields, and especially biomedicine. In this article I analyse the recently published article which, in summary, argues that with a (...) better understand-ing and thus more appropriate use of the statistic, many of the aforementioned limitations can be addressed. In particular, I demonstrate that the (often implicit) premises of this counterargument are questionable, in some cases arguably inconsistent, and that therefore the counterargument provides little if any justification for the continued use of the p-value. Additionally, my analysis should help researchers seeking to interpret their empirical data by illustrating the nuanced nature and the multiplicity of statistical, methodological, and epistemological issues which must be considered in this process. (shrink)

Debunking arguments in ethics contend that our moral beliefs have dubious evolutionary, cultural, or psychological origins—hence concluding that we should doubt such beliefs. Debates about debunking are often couched in coarse-grained terms—about whether our moral beliefs are justified or not, for instance. In this paper, I propose a more detailed Bayesian analysis of debunking arguments, which proceeds in the fine-grained framework of rational confidence. Such analysis promises several payoffs: it highlights how debunking arguments don’t affect all agents, (...) but rather only those agents who updated on their intuitions using a specific range of evidentiary weights; it underscores how the debunkers shouldn’t conclude that we should reduce confidence beyond some threshold, but rather only that we should reduce confidence by some amount; and it proposes a method of integrating different kinds of evidence—about the kinds of epistemic flaws at play, about the different possible origins of our moral beliefs, about the background normative assumptions we’re entitled to make—in order to arrive at a rational moral credence in light of debunking. (shrink)

The problem addressed in this paper is “the main epistemic problem concerning science”, viz. “the explication of how we compare and evaluate theories [...] in the light of the available evidence” (van Fraassen, BC, 1983, Theory comparison and relevant Evidence. In J. Earman (Ed.), Testing scientific theories (pp. 27–42). Minneapolis: University of Minnesota Press). Sections 1– 3 contain the general plausibility-informativeness theory of theory assessment. In a nutshell, the message is (1) that there are two values a theory should exhibit: (...) truth and informativeness—measured respectively by a truth indicator and a strength indicator; (2) that these two values are conflicting in the sense that the former is a decreasing and the latter an increasing function of the logical strength of the theory to be assessed; and (3) that in assessing a given theory by the available data one should weigh between these two conflicting aspects in such a way that any surplus in informativeness succeeds, if the shortfall in plausibility is small enough. Particular accounts of this general theory arise by inserting particular strength indicators and truth indicators. In Section 4 the theory is spelt out for the Bayesian paradigm of subjective probabilities. It is then compared to incremental Bayesian confirmation theory. Section 4 closes by asking whether it is likely to be lovely. Section 5 discusses a few problems of confirmation theory in the light of the present approach. In particular, it is briefly indicated how the present account gives rise to a new analysis of Hempel’s conditions of adequacy for any relation of confirmation (Hempel, CG, 1945, Studies in the logic of comfirmation. Mind, 54, 1–26, 97–121.), differing from the one Carnap gave in § 87 of his Logical foundations of probability (1962, Chicago: University of Chicago Press). Section 6 adresses the question of justification any theory of theory assessment has to face: why should one stick to theories given high assessment values rather than to any other theories? The answer given by the Bayesian version of the account presented in section 4 is that one should accept theories given high assessment values, because, in the medium run, theory assessment almost surely takes one to the most informative among all true theories when presented separating data. The concluding section 7 continues the comparison between the present account and incremental Bayesian confirmation theory. (shrink)

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka etal. (2015, 2016a, 2016b, 2017) develop a procedure forcausal feature learning (CFL) in an effortto automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new (...) technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, (...) Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics Platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu. (shrink)

In this paper, the notion of degree of inconsistency is introduced as a tool to evaluate the sensitivity of the Full Bayesian Significance Test (FBST) value of evidence with respect to changes in the prior or reference density. For that, both the definition of the FBST, a possibilistic approach to hypothesis testing based on Bayesian probability procedures, and the use of bilattice structures, as introduced by Ginsberg and Fitting, in paraconsistent logics, are reviewed. The computational and theoretical advantages (...) of using the proposed degree of inconsistency based sensitivity evaluation as an alternative to traditional statistical power analysis is also discussed. (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. It is (...) inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)