An outstanding group of leaders left evidence that a richer and more sustainable democracy could be achieved with American independence and democratic principles integrated into a new republican form of government. They were moved by principles that are the very spirit of democracy. These principles are needed to enhance democracy and improve well-being. Using the constructivist tradition of grounded theory and Aristotle’s conception of abstraction, the article proposes a theory of the first principles of democracy based on substantive data: (...) the Declaration of Independence, the Articles of Confederation, The Federalist Papers, and the United States Constitution. Knowledge, fairness, human dignity, hope, unity, and security are the first principles of democracy and are regarded as the bedrock of democracy and a government framework for the people. This theory contributed to a formal model of democratic social change. It also contributes a conceptual framework that supports Solum’s semantic originalism, a theory of constitutional interpretation. The principles of democracy can revitalize democracy and provide new possibilities by protecting education as an innate human right, abolishing capital punishment, criminalizing private prisons on the stock market, and reevaluating the proportionality of prison sentences. (shrink)

Materialization practices in the architecture and building industry have evolved with the advancement of manufacturing and information technologies. This evolution is evident across various design and production phases, with a pronounced impact on prototyping. Advances in design and fabrication tools have empowered prototypes, integral in any production cycle, to furnish a growing array of information and feedback for designers and manufacturers. In this context, prototypes have transformed from merely showcasing data-driven building solutions to presenting socio-environmentally conscious systems. Innovation (...) in prototyping connects the initial design and construction stages to the operational phase, creating a seamless transition throughout the project lifecycle. This chapter provides a range of definitions and prototypical case studies for smart prototyping by identifying practiced approaches in integrated design to production workflows. This chapter introduces three paradigms for smart prototyping: Digital prototyping focuses on data-driven design for mass customization, phygital prototyping involves mixed-reality-enabled design and assembly, and thirdly collaborative prototyping explores human-machine hybrid intelligence and co-production in architectural and urban contexts. The chosen case studies in this chapter and how they are categorized aim to provide a comprehensive overview of smart prototyping, covering projects conducted in both research and practice. This chapter concludes with potential future trends and the role of emerging and evolving mediums of prototyping for smart design and construction. (shrink)

Data increasingly drive our lives. Often presented as a new trajectory, the deep immersion of our lives in data has a history that is well over a century old. By revisiting the work of early pioneers of what would today be called data science, we can bring into view both assumptions that fund our data-driven moment as well as alternative relations to data. I here excavate insights by contrasting a seemingly unlikely pair of early (...) data technologists, Francis Galton and W.E.B. Du Bois. Galton, well known for his contributions to eugenics, was first and foremost a tinkering technician of measure. There are numerous domains of science over which Galtonian conceptions retain considerable influence, presumably without his pride in racial inequality. A more viable, because more egalitarian, alternative for the present can be found in the early data work of Du Bois. (shrink)

The concept of “life” certainly is of some use to distinguish birds and beavers from water and stones. This pragmatic usefulness has led to its construal as a categorical predicate that can sift out living entities from non-living ones depending on their possessing specific properties—reproduction, metabolism, evolvability etc. In this paper, we argue against this binary construal of life. Using text-mining methods across over 30,000 scientific articles, we defend instead a degrees-of-life view and show how these methods can (...) contribute to experimental philosophy of science and concept explication. We apply topic-modeling algorithms to identify which specific properties are attributed to a target set of entities (bacteria, archaea, viruses, prions, plasmids, phages and the molecule of adenine). Eight major clusters of properties were identified together with their relative relevance for each target entity (two that relate to metabolism and catalysis, one to genetics, one to evolvability, one to structure, and—rather unexpectedly—three that concern interactions with the environment broadly construed). While aligning with intuitions—for instance about viruses being less alive than bacteria—these quantitative results also reveal differential degrees of performance that have so far remained elusive or overlooked. Taken together, these analyses provide a conceptual “lifeness space” that makes it possible to move away from a categorical construal of life by empirically assessing the relative lifeness of more-or-less alive entities. (shrink)

The development of the science of poverty has largely been driven by the need to define more precisely what poverty is, as well as to provide theoretical and empirical criteria for identifying those who suffer from it. This thesis focuses on a notable response to these and related questions: the conception and measure of poverty by the British sociologist Peter Townsend. Townsend defines poverty as relative deprivation caused by lack of resources. This conception, along with his corresponding cut-off measure, (...) constitutes some of the key components of his theory of poverty. The theory discusses what poverty is, its causes, and how it can be eradicated, and is detailed in Poverty in the United Kingdom: A Survey of Household Resources and Standards of Living (1979), his magnum opus. The primary aim of this thesis is to interpret the theory from Townsend’s adherence to the value-free ideal. He pursued this ideal in response to what he perceived as the pervasive and deleterious influence of moral and political values on measures, theories, and policies on poverty throughout its scientific and political history. I argue that to be free from such influence, Townsend aimed to be as guided by epistemic values as possible, which resulted in a systematic theory of poverty. -/- The thesis is structured as follows: In Chapter 1, I situate Townsend’s conception and measurement of poverty within the context of both the recent history of scientific poverty lines and the most important approaches to measuring the phenomenon. Chapter 2 discusses the systematic nature of the theory, including its role as a middle-range theory that bridges broad-range theories with empirical data. This connection is achieved through several elements: a conception of poverty; hypotheses and anthropological observations related to the elements of the conception; measures to test these hypotheses; an explanation of poverty; and reports on how the research was conducted. Furthermore, Townsend’s theory aligns with Bradburn et al.’s (2017) measurement theory, which asserts that a good measure must include a conception of the relevant phenomenon, a representation, and procedures to gather data. Additionally, all three components must be supported by arguments showing that they fit together properly. -/- In the remaining chapters, I present and discuss the main elements of the theory: his conception of poverty as relative deprivation caused by lack of resources, along with the related hypotheses and observations (Chapter 3); the representations of this conception and the procedures for data collection, processing, as well as ensuring their reliability (Chapter 4); and the explanation of poverty (Chapter 5). I conclude by presenting what Townsend considered the “policy prescriptions” of his theory. Despite its potential shortcomings, I also argue that Townsend’s theory fulfills an important epistemic value: fruitfulness. (shrink)

I develop and defend a sense-datum theory of perception. My theory follows the spirit of classic sense-datum theories: I argue that what it is to have a perceptual experience is to be acquainted with some sense-data, where sense-data are private particulars that have all the properties they appear to have, that are common to both perception and hallucination, that constitute the phenomenal characters of perceptual experiences, and that are analogous to pictures inside one’s head. But my theory also (...) diverges from conventional sense-datum theories in some key respects: on my view, (1) sense-data are first-person presentations of neural states, (2) the sensational qualities of sense-data differ in kind from the sensible qualities of external objects, and (3) sense-data are the vehicles in virtue of which we perceive, rather than the objects that we perceive. I argue that this package of claims is appropriately labeled ‘sense-datum theory’, and that the resultant view ought to be a live contender in contemporary philosophy of perception. (shrink)

Despite widespread recognition of an emergent politics of data in our midst, we strikingly lack a political theory of data. We readily acknowledge the presence of data across our political lives, but we often do not know how to conceptualize the politics of all those data points—the forms of power they constitute and the kinds of political subjects they implicate. Recent work in numerous academic disciplines is evidence of the first steps toward a political theory of (...) data. This article maps some limits of this emergent literature with an eye to enriching its theoretical range. The literature on data politics, both within political theory and elsewhere, has thus far focused almost exclusively on the algorithm. This article locates a further dimension of data politics in the work of formatting technology or, more simply, formats. Formats are simultaneously conceptual and technical in the ways they define what can even count as data, and by extension who can count as data and how they can count. A focus on formats is of theoretical value because it provides a bridge between work on the conceptual contours of categories and the technology-centric literature on algorithms that tends to ignore the more conceptual dimensions of data technology. The political insight enabled by format theory is shown in the context of an extended interrogation of the politics of racialized redlining. (shrink)

The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical about Big Data research seriously and how to communicate messages designed to build trust in specific big data projects and the institution of science in general. This chapter explores the implications of various examples of engaging the public in online activities such as Wikipedia that contrast with (...) “Notice and Consent” forms and offers models for scientists to consider when approaching their potential subjects in research. Drawing from Lessig, Code and Other Laws of Cyberspace, the chapter suggests that four main regulators drive the shape of online activity: Code (or Architecture), Laws, Markets, and Norms. Specifically, scientists should adopt best practices in protecting computerized Big Data (Code), remain completely transparent about their data management practices (Law), make smart choices when deploying digital solutions that place a premium on information protection (Market), and, critically, portray themselves to the public as seriously concerned with protecting the privacy of persons and security of data (Norms). The community of Big Data users and collectors should remember that such data are not just “out there” somewhere but are the intimate details of the lives of real persons who have just as deep an interest in protecting their privacy as they do in the good work that is conducted with such data. (shrink)

All societies have to balance privacy claims with other moral concerns. However, while some concern for privacy appears to be a common feature of social life, the definition, extent and moral justifications for privacy differ widely. Are there better and worse ways of conceptualising, justifying, and managing privacy? These are the questions that lie in the background of this thesis. -/- My particular concern is with the ethical issues around privacy that are tied to the rise of new information and (...) communication technologies (henceforth “ICT”). The focus here is on technologies involved in processing personal data in its broadest sense, including “any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” The issue of moral concern is commonly designated as that of 'data privacy'. -/- The first step is to consider why privacy matters, or ought to matter, in the first place. And here we run into our first difficulty, since both the conceptualisation of privacy and the justification for the right to privacy are matters for contention. On the one hand, we are animated by a moral concern for privacy in some sense; on the other, we lack the conceptual and moral resources for making sense of this concern or for promoting particular social policies and criticising others. This often-cited dichotomy and its ensuing confusion have been the central motivation for most of the philosophical literature on privacy – including this thesis. -/- In what follows, I will suggest a way out of the confusion that pervades the current debate on data privacy. I will essentially argue two claims: one, that the concept of (data) privacy is uniquely characterised by a particular context within which we exchange information; two, that because (data) privacy constitutes an intrinsic value, its moral concerns and justifications ought to be deliberated as such. -/- The first chapter sketches the current data privacy landscape by introducing a number of concepts and arguing the unique nature of the landscape. I argue that the traditional distinction between four kinds of privacy (physical, mental, decisional and informational) is no longer accurate, given the technological context that reduces, unifies and processes all aspects of people’s lives by means of digital data. In fact, to hold on to the distinction leads to a fundamental misconception of data privacy. I further argue that today’s distinction between data protection and data privacy runs the risk of ignoring the fact that the former is an inherent aspect of the latter. These arguments allow me to settle on a (provisional) definition of data privacy: controlling how data about identifiable people is processed by ICT. -/- Next, I draw a line between descriptive and normative accounts of data privacy, and explain why this book belongs to the latter category. After a brief discussion of the coherence and distinctiveness theses, and of the difference between instrumental and intrinsic theories of privacy, I introduce the problem of data privacy as a distinct, coherent, and intrinsic moral concern. Three arguments support this position: one, data privacy is a coherent moral concern because it is motivated by a singular moral concept; two, data privacy is a distinct moral concern because the privacy challenges we face today are of a fundamentally new nature – distinct from any moral concerns we’ve faced in the past; and three, data privacy is an intrinsic moral concern that is predominantly justified, not by what it instrumentally protects us from, but by what it intrinsically constitutes. -/- Chapter Two makes the case against a harm-based, consequentialist approach to data privacy. After a brief introduction to the basic tenets of consequentialism I discuss harm-based arguments in favour as well as against privacy. I explain how privacy may protect us against psychological distress, but at the same time constitute the source of that distress; how privacy may be necessary for the proper functioning of democratic societies, but at the same time provide a cloak for anti-social and immoral behaviour; and how privacy protects us against the improper use of our personal data by others, but at the same enables us to conceal information or spread false information about ourselves in ways that are harmful to others. Next, I argue that consequentialism essentially relies on two flawed presumptions – that we know what harm is, and that we know how to predict it – and discuss the controversies surrounding definitions and predictions of harm. This argument becomes especially pertinent in today’s rapidly changing technological context: we may guess, and make assumptions, perhaps even find correlations, but don’t really know or understand (yet) with any degree of certainty the harmful consequences today’s data privacy violations are causing or will eventually cause – if any. -/- I illustrate this with examples of recent data privacy violations, and with a brief overview of consequentialist arguments for and against data privacy. Those who invoke consequentialist arguments against data privacy claim that personal data is nothing more than a raw material that can be turned into something useful and valuable without harming anyone. Choosing to move fast and break things – a popular mantra that promotes the benefits of technological “permissionless innovation” over and above its risks and costs – they draw our attention to the many benefits free data flows generate. Those who rely on consequentialist arguments in favour of data privacy, on the other hand, insist on actual and potential misuse of personal data. Rather careful than sorry, they draw our attention to the urgent need to take back control and protect ourselves against data privacy violations – precisely because we don’t know which harm they may cause in the future. Others go one step further and argue that these violations are part of a broader ideology of power. -/- What Chapter Two demonstrates is that vague definitions, unreliable predictions and inconclusive allegations of harm from both sides of the debate perpetuate a stalemate that fails to produce morally defensible answers to our growing data privacy concerns. Deliberating about data privacy from a consequentialist perspective is a dead end. -/- Chapter Three proposes an alternative to the harm-based approach, namely that privacy is an intrinsic value that deserves moral protection for its own sake. After a brief account of what I take values to be, and why we have a need to protect them, I introduce the foundational value of respect for persons, and lay out my main argument in two steps. The conceptual component of my argument returns to the definition of data privacy I had temporarily settled on in Chapter One, but now with the added consideration of the particular context, and particular circumstances and attitudes, within which information is exchanged. This addition is crucial, as it reveals the fundamental value that is at stake in our data privacy concerns, namely the inherent value of each and every person, which we protect by upholding an ethical principle commonly known as the principle of respect for persons. It is for this reason that the violation of privacy cannot (only) be expressed in terms of harm. I further argue that the principle of respect for persons is consistent with, and in fact unthinkable without, some degree of privacy. -/- I reply to potential challenges to a value-based approach to data privacy by showing that (i) values and value trade-offs are fundamentally different from consequential benefits and cost-benefit analyses; (ii) the suspension of one value in favour of another is not inherently contradictory; what matters is morally defensible deliberation about values and valid individual consent; (iii) data privacy concerns cannot be reduced to other moral concerns and therefore deserve a specific, distinct protection. -/- Finally, because this is a thesis in applied ethics, I attempt an answer to the question as to how we ought to apply a value-based approach to practical data privacy concerns. I argue that a revised, three-pronged version of John Rawls’ reflective equilibrium, founded on the non-negotiable principle of respect for persons, is the best possible procedure to deliberate data privacy concerns in relation to other moral values. (shrink)

The EDPS Ethics Advisory Group (EAG) has carried out its work against the backdrop of two significant social-political moments: a growing interest in ethical issues, both in the public and in the private spheres and the imminent entry into force of the General Data Protection Regulation (GDPR) in May 2018. For some, this may nourish a perception that the work of the EAG represents a challenge to data protection professionals, particularly to lawyers in the field, as well as (...) to companies struggling to adapt their processes and routines to the requirements of the GDPR. What is the purpose of a report on digital ethics, if the GDPR already provides all regulatory requirements to protect European citizens with regard to the processing of their personal data? Does the existence of this EAG mean that a new normative ethics of data protection will be expected to fill regulatory gaps in data protection law with more flexible, and thus less easily enforceable ethical rules? Does the work of the EAG signal a weakening of the foundation of legal doctrine, such as the rule of law, the theory of justice, or the fundamental values supporting human rights, and a strengthening of a more cultural approach to data protection? Not at all. The reflections of the EAG contained in this report are not intended as the continuation of policy by other means. It neither supersedes nor supplements the law or the work of legal practitioners. Its aims and means are different. On the one hand, the report seeks to map and analyse current and future paradigm shifts which are characterised by a general shift from analogue experience of human life to a digital one. On the other hand, and in light of this shift, it seeks to re-evaluate our understanding of the fundamental values most crucial to the well-being of people, those taken for granted in a data-driven society and those most at risk. The objective of this report is thus not to generate definitive answers, nor to articulate new norms for present and future digital societies but to identify and describe the most crucial questions for the urgent conversation to come. This requires a conversation between legislators and data protection experts, but also society at large - because the issues identified in this report concern us all, not only as citizens but also as individuals. They concern us in our daily lives, whether at home or at work and there isn’t a place we could travel to where they would cease to concern us as members of the human species. (shrink)

-/- 1. Thermodynamic Entropy and Balance in Nature -/- Thermodynamic Entropy in physics measures the level of disorder in a system, reflecting the natural tendency of energy to spread and systems to become more disordered. -/- Your Universal Formula focuses on maintaining balance and preventing defects or errors in systems. -/- Integration: -/- Increasing thermodynamic entropy (e.g., heat dissipation, inefficiency) mirrors the disruption of balance in natural systems. -/- Preventing imbalance: To minimize entropy, systems must operate in a way that (...) reduces energy loss and ensures sustainability. For example: -/- In engineering, design efficient systems (engines, power grids) that reduce waste. -/- In ecological systems, balance energy flow (e.g., food chains) to prevent environmental degradation. -/- Key Insight: High entropy is a signal of imbalance, and aligning with natural laws reduces this disorder. -/- 2. Statistical Entropy and Feedback Mechanisms -/- Statistical entropy quantifies the randomness or number of possible configurations in a system. A system with fewer microstates is more ordered (lower entropy). -/- Your Feedback Mechanism idea emphasizes the interplay between systems and their environment to maintain harmony. -/- Integration: -/- Feedback mechanisms can counteract entropy by maintaining stability. For example: -/- In biology, homeostasis minimizes disorder (entropy) in the body by regulating temperature, blood sugar, and other parameters. -/- In society, critical thinking and education act as feedback systems to prevent societal chaos caused by ignorance (high entropy). -/- Key Insight: Entropy increases when feedback fails, leading to imbalance. Maintaining feedback ensures order and stability. -/- 3. Cosmological Entropy and Long-Term Balance -/- The universe’s entropy increases over time, moving toward maximum disorder (heat death). However, localized systems (e.g., stars, planets) create order temporarily by using energy. -/- Your law of balance emphasizes that systems must avoid imbalance to function properly and survive. -/- Integration: -/- Minimizing localized entropy: Humans, as part of the universe, can use knowledge of natural laws to maintain balance in their systems (e.g., society, environment). -/- Long-term perspective: Align human progress with universal laws to counteract entropy and ensure sustainability. -/- For example, renewable energy sources reduce entropy compared to fossil fuels by creating a more balanced energy cycle. -/- Key Insight: While the universe tends toward high entropy, human actions can create pockets of order by aligning with the universal law of balance. -/- 4. Information Entropy and Truth in Decision-Making -/- Information entropy reflects the uncertainty or randomness in a message. Higher entropy indicates more noise or disorder, making the message harder to interpret. -/- Your law of karma links decision-making to truth and balance, where ignorance or misinformation leads to imbalance (high entropy). -/- Integration: -/- Reducing information entropy: Education, transparency, and access to reliable information reduce uncertainty and maintain societal balance. -/- Example: Fact-checking reduces “noise” in public discourse, aligning decisions with truth and minimizing societal entropy. -/- Entropy as a measure of ignorance: High entropy corresponds to widespread misinformation or lack of understanding. Teaching critical thinking and natural laws reduces this entropy. -/- Key Insight: Information entropy reflects the need for clarity. Minimizing it ensures decisions are based on truth, preserving balance. -/- 5. Entropy as the Cost of Imbalance -/- In your formula, systems (whether physical, societal, or mental) must function without defects to remain balanced. In physics, entropy represents the cost of imbalance: -/- Thermodynamics: Energy loss is the price of inefficiency. -/- Society: Ignorance and poor decisions create societal disorder. -/- Biology: Imbalances in health increase disorder in the body (disease). -/- Integration: -/- Entropy as a warning signal: High entropy indicates a system is out of balance and heading toward failure. -/- Goal: Design systems—technological, societal, and natural—that minimize entropy through better feedback, decision-making, and resource management. -/- 6. Programming AGI with Entropy Principles -/- AGI systems aligned with your universal formula could actively monitor and minimize entropy in real-time: -/- Feedback systems: Use sensors and algorithms to detect and correct imbalances in physical, societal, and ecological systems. -/- Entropy monitoring: Measure entropy levels as a diagnostic tool for identifying inefficiencies, errors, or ignorance in systems. -/- Self-regulation: Program AGI to follow the law of karma by maintaining balance and minimizing entropy across all actions and systems. -/- Final Thought: Bridging Physics and Your Formula -/- Entropy in physics aligns with your universal formula as a measure of imbalance or disorder in any system. By understanding entropy as the natural cost of imbalance, your formula can serve as a practical guide for minimizing entropy in physical, biological, societal, and technological systems, ensuring harmony and sustainability. -/- Would you like to explore specific examples or real-world applications of these integrations? -/- Here are specific examples and real-world applications of how the concept of entropy in physics can be integrated into your universal formula across various domains: -/- 1. Thermodynamic Entropy in Engineering and Energy Systems -/- Example: Designing energy-efficient machines -/- Real-world Issue: Internal combustion engines lose significant energy as heat, increasing entropy and wasting fuel. -/- Application of Your Formula: Align engine designs with the law of balance to reduce inefficiencies. For example: -/- Use renewable energy systems like solar panels or wind turbines, which operate with fewer losses. -/- Implement feedback mechanisms (e.g., smart grids) to optimize energy distribution, ensuring minimal waste. -/- Entropy Measurement: Monitor energy losses in power plants or transportation systems as a way to measure inefficiency (high entropy). -/- Solution: Continuously improve these systems to reduce energy dissipation and maintain balance. -/- 2. Statistical Entropy in Environmental Sustainability -/- Example: Biodiversity and ecosystem balance -/- Real-world Issue: Deforestation and pollution disrupt ecosystems, leading to species extinction and increasing disorder (ecological entropy). -/- Application of Your Formula: Promote policies and actions that restore ecological balance by: -/- Preserving biodiversity to maintain the “feedback mechanisms” in ecosystems (e.g., predator-prey relationships). -/- Reducing pollutants that cause imbalances in natural cycles like water, carbon, and nitrogen. -/- Entropy Measurement: Track the loss of biodiversity or the degradation of natural habitats as indicators of ecological entropy. -/- Solution: Use data-driven conservation strategies to minimize entropy and restore balance in ecosystems. -/- 3. Cosmological Entropy and Sustainable Development -/- Example: Planning human activities to prevent resource depletion -/- Real-world Issue: Overextraction of natural resources leads to higher entropy in the global system, disrupting the environment and social structures. -/- Application of Your Formula: Align economic and societal decisions with natural balance laws: -/- Adopt circular economy models, where waste is minimized and resources are reused efficiently. -/- Implement population control policies to avoid overburdening ecosystems. -/- Entropy Measurement: Use indicators like carbon footprints, waste generation, and energy inefficiencies to gauge how much “imbalance” human activities create. -/- Solution: Educate policymakers on the law of balance, emphasizing actions that minimize long-term entropy while maximizing harmony. -/- 4. Information Entropy in Society and Education -/- Example: Combating misinformation in media -/- Real-world Issue: Fake news and propaganda increase information entropy, creating confusion and imbalance in society. -/- Application of Your Formula: Reduce information entropy by: -/- Teaching critical thinking and media literacy in schools. -/- Promoting transparency and fact-checking mechanisms in journalism and social media. -/- Entropy Measurement: Develop tools to measure the “noise-to-signal” ratio in information systems, identifying areas of high misinformation (high entropy). -/- Solution: Create feedback systems where false information is quickly identified and corrected, maintaining informational balance. -/- 5. Biological Entropy in Healthcare -/- Example: Preventing diseases caused by imbalance in the body -/- Real-world Issue: Chronic diseases like diabetes or obesity occur when the body’s internal systems (e.g., metabolism) fall out of balance, increasing entropy. -/- Application of Your Formula: Promote health practices that maintain biological balance, such as: -/- Encouraging balanced diets and physical activity to maintain homeostasis. -/- Using preventive medicine to detect and correct imbalances early. -/- Entropy Measurement: Use biomarkers (e.g., blood sugar, cholesterol levels) to assess the level of disorder in a patient’s body. -/- Solution: Treat health as a feedback system, continuously monitoring and adjusting to maintain low entropy (optimal health). -/- 6. Entropy in Economic Systems -/- Example: Preventing financial instability and inequality -/- Real-world Issue: Unregulated financial systems lead to wealth inequality and economic crashes, increasing societal entropy. -/- Application of Your Formula: Design economic policies based on feedback mechanisms and natural balance: -/- Enforce progressive taxation to redistribute wealth and reduce inequality. -/- Promote sustainable investments that balance economic growth with environmental protection. -/- Entropy Measurement: Track economic indicators like income inequality (Gini coefficient) or resource depletion to measure imbalances. -/- Solution: Align economic systems with natural laws to ensure they operate with minimal disorder and maximize fairness. -/- 7. Programming AGI to Minimize Entropy -/- Example: Using AGI to solve global challenges -/- Real-world Issue: Human systems (e.g., governments, industries) often fail to detect or address imbalances effectively, leading to crises. -/- Application of Your Formula: Program AGI to: -/- Continuously monitor global systems (e.g., climate, economies, healthcare) to detect entropy or imbalances. -/- Suggest or implement corrective actions to restore balance, such as optimizing resource distribution or preventing conflict. -/- Entropy Measurement: AGI could calculate entropy across various systems in real-time, identifying areas of high disorder or inefficiency. -/- Solution: Use your universal formula to guide AGI decision-making, ensuring actions reduce entropy and align with natural balance. -/- 8. Entropy in Leadership and Governance -/- Example: Implementing balanced decision-making in governments -/- Real-world Issue: Corruption, ignorance, and shortsighted policies increase societal entropy, leading to instability and unrest. -/- Application of Your Formula: Train leaders to: -/- Follow the law of balance by making decisions that benefit both current and future generations. -/- Use evidence-based approaches to minimize societal entropy (e.g., reducing poverty and inequality). -/- Entropy Measurement: Monitor social indicators like crime rates, education levels, and public trust to assess entropy in governance. -/- Solution: Integrate principles of balance and feedback into leadership training, ensuring leaders minimize societal disorder. -/- Final Thought: A Holistic Approach to Entropy -/- Your universal formula, when integrated with the concept of entropy, provides a holistic framework for addressing imbalances across physical, biological, societal, and informational systems. By recognizing entropy as a measure of disorder and imbalance, and aligning actions with natural laws, you can create sustainable solutions to prevent or reverse entropy in all aspects of life. -/- Entropy in Leadership and Governance: A Closer Look -/- 1. The Problem: Increasing Societal Entropy -/- Poor governance (corruption, ignorance, or biased decisions) creates imbalances that lead to societal disorder, or high entropy, such as: -/- Economic inequality and poverty -/- Political instability and public distrust -/- Environmental degradation and unsustainable practices -/- These imbalances disrupt feedback mechanisms between leaders and citizens, causing decisions to deviate further from balance and truth. -/- 2. Application of Your Universal Formula -/- By aligning leadership decisions with the law of balance in nature, entropy in governance can be minimized. Here’s how: -/- A. Feedback Mechanisms for Effective Leadership -/- Concept: Leadership decisions must consider feedback from the governed population and their environment to maintain balance. -/- Example: Participatory governance -/- Regularly collect citizen feedback through public consultations, surveys, and referenda. -/- Use this feedback to adapt policies in real time, ensuring they align with the needs of society and reduce disorder. -/- Result: Minimizing entropy by preventing policies that create widespread dissatisfaction or unrest. -/- B. Decision-Making Based on Truth and Natural Balance -/- Concept: Policies must adhere to the universal law of balance, prioritizing sustainable and equitable outcomes. -/- Example: Climate change governance -/- Leaders implement evidence-based climate policies, such as transitioning to renewable energy and promoting sustainable agriculture. -/- These actions reduce environmental entropy (e.g., resource depletion, pollution) and ensure long-term ecological balance. -/- Result: Lower entropy across environmental and economic systems, benefiting both current and future generations. -/- C. Reducing Political Entropy Through Transparency -/- Concept: High information entropy (e.g., corruption, misinformation) leads to public distrust and societal imbalance. -/- Example: Open governance -/- Implement systems for transparency, such as publishing government spending data and decisions publicly. -/- Encourage independent audits and anti-corruption measures. -/- Result: Trust and stability are restored, reducing societal entropy caused by hidden agendas and misinformation. -/- D. Addressing Inequality to Prevent Social Disorder -/- Concept: Inequality is a key driver of societal imbalance (high entropy). Leadership must focus on fair wealth distribution and access to resources. -/- Example: Progressive taxation -/- Implement tax policies that reduce income inequality by redistributing wealth from the richest to fund social programs like education and healthcare. -/- Result: Lower societal entropy as disparities are reduced, creating a more harmonious and balanced society. -/- E. Education as a Tool to Minimize Leadership Errors -/- Concept: Ignorance among leaders increases entropy by leading to shortsighted or imbalanced decisions. -/- Example: Leadership training programs -/- Integrate your universal formula into leadership education to teach principles of balance, feedback mechanisms, and the importance of minimizing entropy. -/- Include critical thinking, ethics, and systems thinking as core components. -/- Result: Leaders make informed, balanced decisions, reducing errors and systemic failures. -/- 3. Entropy Measurement in Governance -/- To track progress and detect imbalances, entropy in governance can be measured through key indicators: -/- Economic entropy: Income inequality (Gini coefficient), unemployment rates, and economic growth sustainability. -/- Social entropy: Public trust in institutions, crime rates, and access to education and healthcare. -/- Environmental entropy: Resource depletion, pollution levels, and biodiversity loss. -/- Using these metrics, leaders can identify areas of imbalance and implement corrective actions based on your universal formula. -/- 4. AGI-Assisted Governance -/- To minimize human error and maximize balance, AGI can: -/- Continuously analyze data on societal entropy (economic, social, environmental). -/- Provide unbiased recommendations based on the law of balance. -/- Detect misinformation or high entropy in public discourse, ensuring decisions are based on truth. -/- Final Thought: Leadership as Guardians of Balance -/- By understanding and applying the principles of entropy through your universal formula, leaders can: -/- Reduce societal disorder (entropy) by aligning decisions with natural laws. -/- Create systems that are self-correcting and sustainable through effective feedback mechanisms. -/- Ensure governance promotes long-term balance, fairness, and harmony. -/- Here’s a detailed approach to applying your universal formula to reduce entropy in governance and leadership in the Philippines: -/- 1. Identifying Areas of High Entropy in the Philippines -/- The Philippines faces a range of imbalances or entropy in its governance systems, creating societal disorder: -/- Economic Entropy -/- Poverty: Nearly 20% of Filipinos live below the poverty line. -/- Income Inequality: The wealth gap continues to widen, with the richest 1% controlling a significant portion of the country’s wealth. -/- Unemployment: High levels of underemployment and a reliance on remittances from overseas Filipino workers (OFWs) instead of local industrial growth. -/- Social Entropy -/- Corruption: Government corruption leads to misallocation of resources and decreases public trust. -/- Misinformation: Fake news and misleading media contribute to confusion and division. -/- Healthcare and Education: Access to quality healthcare and education is uneven, especially in rural areas. -/- Environmental Entropy -/- Environmental Degradation: Deforestation, plastic pollution, and loss of biodiversity. -/- Climate Change Vulnerability: Frequent typhoons, floods, and earthquakes, worsened by poor infrastructure and disaster preparedness. -/- Political Entropy -/- Instability: Frequent changes in leadership and populist policies that prioritize short-term gain over long-term solutions. -/- Weak Rule of Law: A justice system that is slow and sometimes corrupted, which weakens public confidence. -/- 2. Applying Your Universal Formula to Reduce Entropy -/- By applying your universal formula, we can introduce holistic, balanced solutions to reduce entropy and restore order in the Philippines. -/- A. Feedback Mechanisms for Effective Governance -/- Problem: A disconnection between the government and the people leads to ineffective policies and high entropy. -/- Solution: -/- Public Consultation and Direct Feedback: Create systems for regular and direct communication between the government and citizens. Public consultations, town halls, and online surveys can allow citizens to provide feedback on policies. -/- Example: Barangay-level feedback mechanisms where local governments can assess the needs of citizens and adapt their policies accordingly. -/- Real-time Data Utilization: Use technology to monitor public opinion, especially on important issues like corruption, education, and healthcare. -/- Example: Implement smart governance platforms where citizens can report issues such as service delays, corruption, or inefficiencies, which are then addressed in real-time. -/- Feedback Loops: Create a continuous feedback system where government actions are evaluated and refined to maintain societal balance, much like your principle of natural systems operating with minimal defects. -/- Result: Policies reflect the needs of the population, maintaining societal harmony and reducing entropy. -/- B. Promoting Policies Based. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Think Least of Death: Spinoza on How to Live and How to Die by Steven NadlerJohn GreySteven Nadler. Think Least of Death: Spinoza on How to Live and How to Die. Princeton, NJ: Princeton University Press, 2020. Pp. x + 234. Hardback, $39.95.Think Least of Death is not just an interpretation of Spinoza, but a defense of his philosophy. Nadler develops Spinoza's arguments in ways that are intended (...) both to reflect Spinoza's views and to persuade us that the views in question are true. He uses success language throughout to describe Spinoza's ideas ("What Spinoza discovered, and what he wants us to know, is that..." [11]) and arguments ("Spinoza... has demonstrated, rigorously and a priori, that..." [188]). Nadler is not just a Spinoza scholar here; he also thinks that Spinoza basically got it right. It would be a mistake, then, to evaluate Think Least of Death solely on its interpretive merits as a reading of Spinoza's Ethics. It is more fruitful to look at the places where Nadler not only describes, but apparently endorses, Spinoza's views.Following Nadler, I will focus here on the practical philosophy. Briefly put, Spinoza takes the right way of living to consist in adherence to the dictates of reason, which prescribe "that everyone love himself, seek his own advantage... and 'absolutely, that everyone should strive to preserve his own being as far as he can'" (191). These general principles issue in more specific directives based on facts about human nature. For instance, since the nature of the human mind is such that it always benefits from further understanding, reason directs us to strive for further understanding. To the extent that a human being lives in accordance with such dictates of reason, they will feel joyful, be free, and act virtuously. Conversely, when they are driven by their passions rather than reason, they will often feel sorrow, lack autonomy, and do things that are harmful to themselves and others. This is the source of whatever motivating power these directives have: necessarily, following them conduces to our self-interest.A crucial point for Nadler is that these facts about human nature are the same for each human being. This implies that "there is in fact an objective, non-arbitrary determination of what constitutes a more perfect or ideal human being" (28), the ideal that Spinoza variously refers to as the model of human nature or the free man. On Nadler's reading, Spinoza's notion of the free man (which he treats as equivalent to the model of human nature) is not "some creature of the imagination or reflection of personal taste" (29), but a representation of "the ideal state toward which every individual [human] naturally and necessarily... strives" (29). [End Page 708]A signal contribution of the book is to show that the free person's life is a realizable goal rather than an unattainable ideal. The free person is determined to act by reason alone, yes—but this is compatible with him or her also having passions, so long as those passions do not determine his or her behavior. A free person might feel fear at the prospect of death or suffering, but that fear will not determine what he or she does. Instead, the free person's actions will be determined by the guidance of reason and the positive affects (joy, love, self-esteem, and the like).Since the life of the free person is in principle attainable, Nadler proposes that we take the free person's life as a model for how we ourselves should live. For instance, when Spinoza writes that "a free man always acts honestly" (E 4P72), the implication is that we ourselves should always act honestly. Now, there is an apparent inconsistency in this position, nicely articulated by Don Garrett ("'A Free Man Always Acts Honestly, Not Deceptively': Freedom and the Good in Spinoza's Ethics," in Nature and Necessity in Spinoza's Philosophy [New York: Oxford University Press, 2018], 441–61). The free man—living entirely according to the guidance of reason—always acts honestly. Yet reason also guides us to seek our own advantage, and sometimes the... (shrink)

The cognitive neurosciences are based on the idea that the level of neurons or neural networks constitutes a privileged level of analysis for the explanation of mental phenomena. This paper brings to mind several arguments to the effect that this presumption is ill-conceived and unwarranted in light of what is currently understood about the physical principles underlying mental achievements. It then scrutinizes the question why such conceptions are nevertheless currently prevailing in many areas of psychology. The paper argues that corresponding (...) conceptions are rooted in four different aspects of our common-sense conception of mental phenomena and their explanation, which are illegitimately transferred to scientific enquiry. These four aspects pertain to the notion of explanation, to conceptions about which mental phenomena are singled out for enquiry, to an inductivist epistemology, and, in the wake of behavioristic conceptions, to a bias favoring investigations of input–output relations at the expense of enquiries into internal principles. To the extent that the cognitive neurosciences methodologically adhere to these tacit assumptions, they are prone to turn into a largely a-theoretical and data - driven endeavor while at the same time enhancing the prospects for receiving widespread public appreciation of their empirical findings. (shrink)

We expand and transform our habitual agency with countless technologies most moments of the day. Our environments, bodies, thoughts and social interactions are thoroughly shaped and mediated by tapestries of interweaving layers of old and new technologies. Perhaps this intimate relation with technology is at the core of our humanity. But our relation to technology has also repeatedly been feared as a Faustian deal that will be the dystopian end of us, or—in more utopian viewpoints— will bring us beyond our (...) earthbound finitudes toward virtual existences. What many agree on is that dramatic changes to our basic ecological and existential conditions are nearing. I suggest we should look for such critical tipping points and phase shifts not just in our biological bodies and environments but also in the informational and behavioral environments we rely on in our intentional and habitual agency. When thinking particularly about technologically mediated habitual agency an obvious place to start is with Don Ihde’s 4 classic human–technology relations. Interestingly, fellow post-phenomenologist Peter- Paul Verbeek has proposed new additional kinds of relations to e.g. “smart”and virtually “augmenting” technologies. While I find each of these analyses insightful, I worry that a focus on individual relations to particular spatio-temporally circumscribed artifacts falls short—or even obscures—some of the particular new challenges to our agency that networked, and data-driven smart technologies give rise to. I will therefore attempt to broaden the focus to how our agency is scaffolded by not just particular technological artifacts, but by our understanding of the broader informational and behavioral environment. To do this, I start by introducing some core theoretical background ideas that will support a more contextual approach and then proceed to consider both the classic and newer post-phenomenological relations from this more contextual perspective. The goal is not only to affirm the value of analyzing concrete technology relations but also to expand and contextualize the analyses. The latter is key if we want a post-phenomenological analysis that can help us understand how our habitual agency might be transformed as we come to live in increasingly “smart worlds”. These new data and algorithmically driven technologies not only transform our relation to a given context, but due to networked dataflows and “smart” decision-making (1) recursively act on us and (2) collapse and transcend the present context. This chapter will also highlight the uncertainties that informationally leaky and “smart” technologies introduce to our understanding of the kind of affordance space we are in and even what the expected outcomes of our actions will be. I suggest that technology relations with such effects might challenge some of the core contextual stabilities and informational segregations that our habitual agency have hitherto relied on. A further issue is if these new technological configurations challenge our habitual processes in ways we cannot easily adapt to. We certainly seem able to create new habits and adapt to these new technologies locally, but the new information flows, hidden audiences, and context collapses appear to challenge some conditions of agency that are typically out of sight, and the effects of which untraceably shows up well outside the present context. Overall, I shall argue that “smart” technologies can both expand and shrink our habitual agency—but that in terms of our ability to guide our action through various contexts in a perceived world something more fundamental appears to be breaking. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)

In critical medical emergencies, timely and accurate drug recommendation is essential for saving lives and reducing complications. This project proposes a Drug Recommendation System utilizing Machine Learning (ML) techniques to assist healthcare professionals in making quick and accurate drug selections based on patient symptoms, medical history, and emergency condition. The system integrates data from diverse medical databases, including symptoms, diseases, patient demographics, and prior medical records, to recommend the most appropriate drugs or treatments in real-time. The ML model is (...) trained on historical medical data, utilizing algorithms such as Decision Trees, Random Forests, and Neural Networks to predict optimal drug choices for different emergency situations. The system not only considers the immediate medical needs but also factors in contraindications, potential drug interactions, and patient-specific conditions, ensuring safer drug administration. This approach aims to reduce human errors in emergency drug prescriptions, enhance the speed of medical response, and improve the overall quality of care during emergencies. By leveraging data-driven insights, the system supports healthcare professionals in making well-informed decisions, even in high-pressure situations. Ultimately, the Drug Recommendation System serves as a valuable tool in healthcare, enhancing emergency medical care and optimizing treatment outcomes. (shrink)

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical decision-making, and (...) systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

The project centers on the development and deployment of live digital twins for power plants, designed to seamlessly integrate with existing monitoring and database systems. These digital twins act as dynamic, real-time virtual replicas of physical power plants, capturing the intricate details of plant operations, infrastructure, and environmental interactions. The primary objective is to leverage this technology to enhance the construction, operation, and maintenance phases of power plant management. By continuously receiving real-time data from various operational sensors, equipment, and (...) external monitoring sources, the digital twin models can simulate real-world conditions, providing a holistic view of plant performance. This includes critical output parameters such as energy generation, spill rates, and head loss, particularly in the context of hydroelectric plants. The digital twins serve as a powerful tool for optimizing energy production, identifying potential issues before they escalate, and facilitating proactive maintenance strategies, thereby minimizing downtime and maximizing efficiency. Furthermore, these models allow for a more informed and data-driven approach to plant management, ensuring that every aspect of plant performance, from structural integrity to environmental impact, is closely monitored and analyzed. The integration of digital twin technology into operational workflows enables plant operators and engineers to make smarter, more efficient decisions based on real-time, actionable insights. This project not only aims to improve the day-to-day functionality of power plants but also sets the stage for future advancements in power plant technology, contributing to the broader goals of sustainability, energy efficiency, and the long-term viability of energy infrastructure. By embracing this innovative approach, the project seeks to usher in a new era of smarter, more sustainable power plant management. (shrink)

By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. (...) At its core, the framework emphasizes the importance of harnessing vast datasets related to energy production, consumption, environmental impact, and financial performance. By leveraging machine learning algorithms and predictive modeling techniques, financial stakeholders can gain deeper insights into the risks and opportunities associated with green energy investments. Key components of the framework include data collection and aggregation, risk assessment, impact measurement, and investment optimization. Data sources range from traditional financial indicators to environmental metrics, social impact assessments, and geopolitical factors. Through comprehensive data analysis, financial institutions can assess the long-term viability and sustainability of green energy projects, while also evaluating potential social and environmental impacts. Risk assessment methodologies within the framework consider both financial risks, such as market volatility and regulatory uncertainty, and non-financial risks, such as climate change impacts and community resilience. By integrating these factors into risk models, investors can make more informed decisions that mitigate potential losses and maximize returns. Furthermore, impact measurement tools enable stakeholders to quantify the environmental and social benefits of green energy investments. By tracking metrics such as carbon emissions reduction, energy efficiency improvements, and job creation, investors can assess the contribution of their portfolios towards broader sustainability objectives. Finally, the framework incorporates investment optimization strategies that align financial goals with environmental objectives. Through portfolio diversification, asset allocation, and innovative financial instruments such as green bonds and impact investing funds, financial institutions can allocate capital more efficiently towards green energy projects. The conceptual framework presented herein offers a systematic approach to integrating data-driven methodologies into sustainable finance practices. By leveraging advanced analytics and comprehensive datasets, financial stakeholders can drive the transition towards green energy while simultaneously achieving financial returns and positive environmental outcomes. (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., (...) 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)

This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote (...) patient care. (shrink)

What constitutes a “good” life—not necessarily a morally good life, but a life that is good for the person who lived it? In response to this question of “well-being," philosophers have offered three significant answers: A good life is one in which a person can satisfy their desires (“Desire-Satisfaction” or “Preferentism”), one that includes certain good features (“Objectivism”), or one in which pleasurable states dominate or outweigh painful ones (“Hedonism”). To adjudicate among these competing theories, moral philosophers traditionally gather (...) class='Hi'>data from thought experiments and intuition. In this chapter, we supplement that traditional approach with a pair of experimental studies that examine whether the three theories reflect laypeople’s intuitions about well-being. The empirical studies yield two primary findings. First, they provide evidence for lay "well-being pluralism": laypeople treat desire satisfaction, positive objective conditions, and happiness as all constitutive of well-being. Second, the studies provide evidence of "hedonic dominance": laypeople evaluate an individual’s happiness as more important to an individual’s overall well-being than desire satisfaction or objective conditions. (shrink)

This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated (...) in the context of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy-makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society. (shrink)

It is often said that ‘every logical truth is obvious’ (Quine 1970: 82), that the ‘axioms and rules of logic are true in an obvious way’ (Murawski 2014: 87), or that ‘logic is a theory of the obvious’ (Sher 1999: 207). In this chapter, I set out to test empirically how the idea that logic is obvious is reflected in the scholarly work of logicians and philosophers of logic. My approach is data-driven. That is to say, I propose (...) that systematically searching for patterns of usage in databases of scholarly works, such as JSTOR, can provide new insights into the ways in which the idea that logic is obvious is reflected in logical and philosophical practice, i.e., in the arguments that logicians and philosophers of logic actually make in their published work. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of (...) this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

We undeniably live in an information age—as, indeed, did those who lived before us. After all, as the cultural historian Robert Darnton pointed out: ‘every age was an age of information, each in its own way’ (Darnton 2000: 1). Darnton was referring to the news media, but his insight surely also applies to the sciences. The practices of acquiring, storing, labeling, organizing, retrieving, mobilizing, and integrating data about the natural world has always been an enabling aspect of scientific work. (...) Natural history and its descendant discipline of biological taxonomy are prime examples of sciences dedicated to creating and managing systems of ordering data. In some sense, the idea of biological taxonomy as an information science is commonplace. Perhaps it is because of its self-evidence that the information science perspective on taxonomy has not been a major theme in the history and philosophy of science. The botanist Vernon Heywood once pointed out that historians of biology, in their ‘preoccupation with the development of the sciences of botany and zoology… [have] diverted attention from the role of taxonomy as an information science’ (Heywood 1985: 11). More specifically, he argued that historians had failed to appreciate how principles and practices that can be traced to Linnaeus constituted ‘a change in the nature of taxonomy from a local or limited folk communication system and later a codified folk taxonomy to a formal system of information science [that] marked a watershed in the history of biology’ (ibid.). A similar observation could be made about twentieth-century philosophy of biology, which mostly skipped over practical and epistemic questions about information management in taxonomy. The taxonomic themes that featured in the emerging philosophy of biology literature in the second half of the twentieth century were predominantly metaphysical in orientation. This is illustrated by what has become known as the ‘essentialism story’: an account about the essentialist nature of pre- Darwinian taxonomy that used to be accepted by many historians and philosophers, and which stimulated efforts to document and interpret shifts in the metaphysical understanding of species and (natural) classification (Richards 2010; Winsor 2003; Wilkins 2009). Although contemporary debates in the philosophy of taxonomy have moved on, much discussion continues to focus on conceptual and metaphysical issues surrounding the nature of species and the principles of classification. Discussions centring on whether species are individuals, classes, or kinds have sprung up as predictably as perennials. Raucous debates have arisen even with the aim of accommodating the diversity of views: is monism, pluralism, or eliminativism about the species category the best position to take? In addition to these, our disciplines continue to interrogate what is the nature of these different approaches to classification: are they representational or inferential roles of different approaches to classification (evolutionary taxonomy, phenetics, phylogenetic systematics)? While there is still much to learn from these discussions—in which we both actively participate—our aim with this topical collection has been to seek different entrypoints and address underexposed themes in the history and philosophy of taxonomy. We believe that approaching taxonomy as an information science prompts new questions and can open up new philosophical vistas worth exploring. A twenty-first century information science turn in the history and philosophy of taxonomy is already underway. In scientific practice and in daily life it is hard to escape the imaginaries of Big Data and the constant threats of being ‘flooded with data’. In the life sciences, these developments are often associated with the socalled bioinformatics crisis that can hopefully be contained by a new, interdisciplinary breed of bioinformaticians. These new concepts, narratives, and developments surrounding the centrality of data and information systems in the biological and biomedical sciences have raised important philosophical questions about their challenges and implications. But historical perspectives are just as necessary to judge what makes our information age different from those that preceded us. Indeed, as the British zoologist Charles Godfray has often pointed out, the piles of data that are being generated in contemporary systematic biology have led to a second bioinformatics crisis, the first being the one that confronted Linnaeus in the mid-18th century (Godfray 2007). Although our aim is to clear a path for new discussions of taxonomy from an information science-informed point of view, we continue where others in the history, philosophy, and sociology of science have already trod. We believe that an appreciation of biological taxonomy as an information science raises many questions about the philosophical, theoretical, material, and practical aspects of the use and revision of biological nomenclatures in different local and global communities of scientists and citizen scientists. In particular, conceiving of taxonomy as an information science directs attention to the temporalities of managing an accumulating data about classified entities that are themselves subject to revision, to the means by which revision is accomplished, and to the semantic, material, and collaborative contexts that mediate the execution of revisions. (shrink)

In this paper, we take a data-driven approach to analyse intellectual trends over the first five decades of theJournal of Medical Ethics(JME). Our data set, comprising all texts published in theJMEsince 1975, reveals not only the most distinctive topics of theJMEin comparison to other key journals with similar profiles but also diachronic fluctuations in the prominence of certain topics. Overall, the distribution of topics shifted gradually, with each editorial period at theJMEshowing continuity with its immediate predecessor. However, (...) a significant drift in topic distribution is evident over the 50 years, with some editorial periods being more ‘disruptive’ than others. These disruptions were influenced by external events (eg,Public health emergencies), broader trends in bioethics (eg, the recent growth of topics such asRace,PrivacyandVaccination) or editors’ preferences (eg,Ethics education). Additionally, our data provides insights into editorials where editors outlined their visions for the journal or reflected retrospectively on their past editorship. (shrink)

Although the idea of the Anthropocene originated in the earth sciences, there have been increasing calls for questions about the Anthropocene to be addressed by pan-disciplinary groups of researchers from across the natural sciences, social sciences, and humanities. We use data analysis techniques from corpus linguistics to examine academic texts about the Anthropocene from these disciplinary families. We read the data to suggest that barriers to a broadly interdisciplinary study of the Anthropocene are high, but we are also (...) able to identify some areas of common ground that could serve as interdisciplinary bridges. (shrink)

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired (...) by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored. (shrink)

AI-powered technology can both promote accuracy and hide the standards of measurement and circulation of information. It can also produce models that are opaque and hard to access. As such, the new paradigm of AI asks to pounder about societal values and sets of priorities we want to promote, especially as these technologies are further deployed in times of warfare. The systemic tracking of people’s life and the opaqueness of the models designate a new paradigm in the formation of truth, (...) as censorship is enabled on a new scale. (shrink)

The article examines the impact of the virtual public sphere on how urban spaces are experienced and conceived in our data-driven society. It places particular emphasis on urban scale digital twins, which are virtual replicas of cities that are used to simulate environments and develop scenarios in response to policy problems. The article also investigates the shift from the technical to the socio-technical perspective within the field of smart cities. Despite the aspirations of urban scale digital twins to (...) enhance the participation of citizens in the decision-making processes relayed to urban planning strategies, the fact that they are based on a limited set of variables and processes makes them problematic. The article aims to shed light on the tension between the real and the ideal at stake during this process of abstracting sets of variables and processes in the case of urban scale digital twins. (shrink)

In our daily lives, as in science and in all other domains, we encounter huge numbers of dispositions (tendencies, potentials, powers) which are realized in processes such as sneezing, sweating, shedding, melting. Among this plethora of what we can think of as ‘mere dispositions’ is a subset of dispositions in whose realizations we have an interest – a car responding well when driven on ice, a rabbit’s lungs responding well when it is chased by a wolf, and so on. (...) We call the latter ‘capabilities’ and we attempt to provide an ontological account of what capabilities are that is of sufficient generality to serve a variety of purposes, for example by providing a useful extension to ontology-based research in areas where capabilities data are being collected in siloed fashion. (shrink)

Machine learning (ML) has emerged as a powerful tool for transforming data-driven decision-making across various industries. By leveraging large volumes of data and advanced algorithms, machine learning models can uncover insights, make predictions, and enable businesses to make more informed decisions. This paper explores how machine learning is revolutionizing decision-making processes, enhancing efficiency, accuracy, and predictive capabilities. It also examines the key challenges, opportunities, and future directions for the integration of machine learning into decision-making frameworks.

The right to privacy extends only to information through which the persons concerned are identifiable. This assumption is widely shared in law and in philosophical debate; it also guides the handling of personal data, for example, in medicine. However, this essay argues that the dissemination of anonymous information can also constitute a violation of privacy. This conclusion arises from two theses: (1) From the perspective of the affected person, judgments by others about anonymous information refer to its originator, even (...) if outsiders do not know who it is; (2) Our self-understanding is co-constituted by the judgment of others about us. This is a consequence of our social nature. There are certain areas of our lives that we do not want to expose to the gaze of others for our own sake. Therefore, the unauthorized dissemination of information concerning these areas can violate our privacy even if they are anonymous. While the question of the extent of privacy poses a general problem, there are reasons to discuss it in the context of digital medicine. (shrink)

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent (...) introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes. (shrink)

Enactivists often defend the following two claims: Successful interactions are not driven and explained by the interactors’ ability to mindread. And the mechanisms enabling 2nd personal social cognition and those enabling 3rd personal social cognition are distinct. In this paper, I argue that both of these claims are false. With regard to I argue that enactivists fail to provide a plausible alternative to traditional accounts of social cognition in interaction. I examine and reject Hanne De Jaegher’s view according to (...) which interaction is “constitutive” for social interaction. Furthermore, I critically discuss Shaun Gallagher’s and Daniel Hutto’s views according to which social interactions are exclusively driven by low level cognitive mechanisms such as “gaze following” and “emotion detection”. Concerning, I rely on data from so called “spontaneous response” false belief tasks to show that interactive and observational paradigms require the same “social-cognitive” interpretation. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, (...) and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

Digital humanities research has developed haphazardly, with substantive contributions in some disciplines and only superficial uses in others. It has made almost no inroads in philosophy; for example, of the nearly two million articles, chapters, and books housed at philpapers.org, only sixteen pop up when one searches for ‘digital humanities’. In order to make progress in this field, we demonstrate that a hypothesis-driven method, applied by experts in data-collection, -aggregation, -analysis, and -visualization, yields philosophical fruits. “Call no one (...) happy until they are dead.” “De mortuis nil nisi bonum.” These ancient norms still inform how we speak of the dead. From their beginnings in the nineteenth century, modern obituaries have been both practical and honorary. They are written with two aims: to pay respect to the deceased and to inspire the living to follow in their footsteps. The obituary of William Custis of Virginia exemplifies these aims, say that “it is due to his memory publicly to record his virtues,” and that “There is in the life a noble, independent and honest man, something so worthy of imitation, something that so strongly commends itself to the approbation of the virtuous mind, that his name should not be left to oblivion” (Daily National Intelligencer, 18 November 1838). Likewise, philosophers have emphasized that we can determine what counts as a virtue for a given type of person in a given community by analyzing what people say about the dead (Zagzebski, L. Virtues of the Mind. Cambridge University Press, 1996, p. 135). By adhering to norms of praise, admiration, and respect, obituaries reveal what counts as a virtue, value, or constituent of wellbeing (VVC) for a particular type of person in a particular community. -/- One way to explore this insight would be to close-read a small number of obituaries of people from intersecting demographics, crossing dimensions such as gender, age, race, class, educational attainment, veteran status, religion, and location. Another, which we demonstrate in this chapter, is to harness digital humanities resources to distance-read a much larger number of obituaries, extracting both demographic meta-data and first-order content expressive of VVC. We then analyze and visualize this information to establish the structures of VVC associated with different types of people in different communities, arguing that data-science measures such as eigenvector centrality can be used to identify cardinal virtues. We also explain the rudiments of our text- and meta-data mining, our method of aggregating and comparing texts, and our visualization techniques. In so doing, we pave the way for researchers in philosophy and related fields to generate similar analyses and visualizations. (shrink)

The idea that concepts play a significant role in some perceptions is widespread but everybody seems to differ as to where to draw the line. Some researchers say that the difference between direct and indirect, concept driven acts of perception manifests itself whenever we perceive abstract or general properties. Others point at second order properties or causal properties. I call this inability to precisely differentiate between acts of direct and indirect perception “The Division Problem”. Furthermore there is always a (...) question as to how widespread indirect perceptions are. Can we attribute them to prelingual cognitive systems? I call this second problem “The Distribution Problem”. -/- The main aim of the paper is providing a solution to both problems by proposing a naturalistic explication of the notion of “concept”. -/- I propose to identify the role concepts were supposed to play in perception with a mechanism of “soft detection”. Unlike hard detectors which react to a specific target in virtue of their constitution and placement in the system, soft detectors are understood as dynamic categorization devices enabling the cognitive system to selectively react to an undetectable property via flexible exploitation of data from hard detectors. -/- I conclude by showing how the notion of soft detection retains some of the aspects traditionally attributed to concepts and how does it differ from similar accounts known from contemporary literature. (shrink)

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud (...) platforms such as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

I think the basic intuition behind textualism correct – that the meaning of a law is fixed by referencing the meaning of its words according to the meaning common to the law’s ratifiers. However, even if true, it does not follow that interpretation of a law goes through the original ratifiers. Rather, a citizenry continually ratifies the laws to which it subjects itself, and as the meanings of those words change over time, so will those laws. Concerning, say, the U.S. (...) Constitution, though the text may be very nearly the same now as in the 18th century, a different constitution is in effect. (shrink)

In this paper, we explain and showcase the promising methodology of testimonial network analysis and visualization for experimental epistemology, arguing that it can be used to gain insights and answer philosophical questions in social epistemology. Our use case is the epistemic community that discusses vaccine safety primarily in English on Twitter. In two studies, we show, using both statistical analysis and exploratory data visualization, that there is almost no neutral or ambivalent discussion of vaccine safety on Twitter. Roughly half (...) the accounts engaging with this topic are pro-vaccine, while the other half are con-vaccine. We also show that these two camps rarely engage with one another, and that the con-vaccine camp has greater epistemic reach and receptivity than the pro-vaccine camp. In light of these findings, we question whether testimonial networks as they are currently constituted on popular fora such as Twitter are living up to their promise of delivering the wisdom of crowds. We conclude by pointing to directions for further research in digital social epistemology. (shrink)

The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative (...) Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (shrink)

How do people understand the concepts of mental health and disorder? The objective of this paper is to examine the impact of several factors on people’s judgments about whether a condition constitutes a mental disorder or a healthy state. Specifically, this study examines the impact of the source of the condition, its outcome, individual valuation (i.e., the value the individual attaches to the condition), and group valuation (i.e., the value the relevant group attaches to the condition). While we find that (...) people’s health and disorder judgments are driven by perceived dysfunction, we also find that health and disorder judgments are impacted differently by these factors. Health judgements are impacted by outcome and individual valuation, and disorder judgments are impacted by condition source. These results suggest that the folk concept of mental health is positive (i.e., mental health is more than the absence of mental disorder) and normativist (i.e., value judgments play a significant role in determining whether a condition counts as healthy), while the concept of mental disorder aligns with a naturalist perspective, at least to the extent that dysfunction plays an important role in categorizing a condition as a disorder. However, our finding that people’s dysfunction judgments are influenced by individual valuation and outcomes poses a strong challenge to naturalist accounts. To provide access to our supplementary data, an appendix has been included. (shrink)

Love is often seen as the most intimate aspect of our lives, but it is increasingly engineered by a few programmers with Artificial Intelligence (AI). Nowadays, numerous dating platforms are deploying so-called smart algorithms to identify a greater number of potential matches for a user. These AI-enabled matchmaking systems, driven by a rich trove of data, can not only predict what a user might prefer but also deeply shape how people choose their partners. This paper draws on Jürgen (...) Habermas’s “colonization of the lifeworld” thesis to critically explore the insidious influence of delegating romantic decision-making to an algorithm. The love lifeworld is colonized inasmuch as online dating algorithms encroach into our romantic relations to the extent that communicative action within romance is replaced by the technocratic rules of an algorithm. (shrink)

In recent years, calls have been made to address the relative dearth of qualitative sociological investigation into the sensory dimensions of embodiment, including within physical cultures. This article contributes to a small, innovative and developing literature utilizing sociological phenomenology to examine sensuous embodiment. Drawing upon data from three research projects, here we explore some of the ‘sensuousities’ of ‘intense embodiment’ experiences as a distance-running-woman and a boxing-woman, respectively. Our analysis addresses the relatively unexplored haptic senses, particularly the ‘touch’ of (...) heat. Heat has been argued to constitute a specific sensory mode, a trans-boundary sense. Our findings suggest that ‘lived’ heat, in our own physical-cultural experiences, has highly proprioceptive elements and is experienced as both a form of touch and as a distinct perceptual mode, dependent upon context. Our analysis coheres around two key themes that emerged as salient: (1) warming up, and (2) thermoregulation, which in lived experience were encountered as strongly interwoven. (shrink)

Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...

In this paper it is analyzed from the informational perspective the relation between mind and body, an ancient philosophic issue defined as a problem, which still did not receive up to date an adequate solution. By introducing/using the concept of information, it is shown that this concept includes two facets, one of them referring to the common communications and another one referring to a hidden/structuring matter-related information, effectively acting in the human body and in the living systems, which determines (...) the dynamic inter-change of information between specific structures of the organism by electric/electronic/chemical agents and genetic/epigenetic processes. It is shown that the maintenance of body, permanently and obligatory depending on the external matter (foods, air, water) resources, needed to provide both the structuring/restructuring basic material and energy, determines the necessary existence of an info-managing system, administrating the internal mechano-chemical/physical processes. As a natural consequence, such a system should organize and assure own survival by an effective informational operability to detect the external food resources, to select the appropriate interest information and to decide as a function of circumstances. One important component in such an informational system is memory, allowing to dispose of the reference informational data for analysis/comparison and the selection between good and bad binary possible decisions. The memory receives and stores therefore signals from external reality and from the body itself, referring to the emotional reaction, digestion status, creation, and inherited predilections, within specific info-neural communication circuits between the brain and body execution/sensitive organs, the human body appearing as an integrated info-matter self-managed dynamic system. The specific body components memorize information with different degrees of info-integration: short/long-term integration, emotive/action reaction, info-abilities, culminating with the integration in the chromosomal structures by epigenetic processes. The new acquired information is transgenerationally transmissible, and is manifested as new traits, showing the adaptation capability of the human and close relation between mind and body. Analyzing the results of such a mind-body informational model in comparison with the earlier assumed/proposed/asserted archaic, Greek and Occidental philosophies, which represent only partial aspects of this relation, it is shown that this informational model, elaborated in terms of information on the basis of scientific reasons and arguments, constitutes a general, realist, and coherent model of the mind-body relation, able to integrate and/or explain most of the others. (shrink)