Healthcare fraud detection is a critical task that faces significant challenges due to imbalanced datasets, which often result in suboptimal model performance. Previous studies have primarily relied on traditional machine learning (ML) techniques, which struggle with issues like overfitting caused by Random Oversampling (ROS), noise introduced by the Synthetic Minority Oversampling Technique (SMOTE), and crucial information loss due to Random Undersampling (RUS). In this study, we propose a novel approach to address the imbalanced data problem in healthcare fraud detection, with (...) a focus on the Medicare Part B dataset. Our approach begins with the careful extraction of the categorical feature "Provider Type," which allows for the generation of new, synthetic instances by replicating existing types to enhance diversity within the minority class. To further balance the dataset, we employ a hybrid resampling technique, SMOTE-ENN, which integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to generate synthetic data points while removing noisy, irrelevant instances. This combined technique not only balances the dataset but also helps in mitigating the potential adverse effects of imbalanced data. We evaluate the performance of the logistic regression model on the resampled dataset using common evaluation metrics such as accuracy, F1 score, recall, precision, and the AUC-ROC curve. Additionally, we emphasize the importance of the Area Under the Precision-Recall Curve (AUPRC) as a critical metric for evaluating model performance in imbalanced scenarios. The experimental results demonstrate that logistic regression achieves an impressive 98% accuracy, outperforming other methods and validating the efficacy of our proposed approach for detecting healthcare fraud in imbalanced datasets. (shrink)

Evaluating large language models (LLMs) for tasks like fact extraction in support of knowledge graph construction frequently involves computing accuracy metrics using a ground truth benchmark based on a knowledge graph (KG). These evaluations assume that errors represent factual disagreements. However, human discourse frequently features metalinguistic disagreement, where agents differ not on facts but on the meaning of the language used to express them. Given the complexity of natural language processing and generation using LLMs, we ask: do metalinguistic (...) disagreements occur between LLMs and KGs? Based on an investigation using the T-REx knowledge alignment dataset, we hypothesize that metalinguistic disagreement does in fact occur between LLMs and KGs, with potential relevance for the practice of knowledge graph engineering. We propose a benchmark for evaluating the detection of factual and metalinguistic disagreements between LLMs and KGs. An initial proof of concept of such a benchmark is available on Github. (shrink)

Effective knowledge management in organizations, universities, libraries, and research institutions requires optimizing the flow of information while accounting for uncertainty. We propose a probabilistic framework that models knowledge as a graph of facts, where each node represents a knowledge unit characterized by its entropy H(v), and edges indicate dependency or influence relationships. Transitions of information between nodes are represented as a Markov Jump Process (MJP), and transition probabilities are optimized using the Metropolis–Hastings algorithm to maximize knowledge (...) flow. This framework identifies low-cost, high-impact pathways of knowledge transfer, enabling strategic decision-making in organizational learning and research. Simulation results demonstrate that maximizing mutual information between nodes reduces operational cost while promoting efficient knowledge dissemination. (shrink)

From my ongoing "Metalogical Plato" project. The aim of the diagram is to make reasonably intuitive how the Socratic elenchos (the logic of refutation applied to candidate formulations of virtues or ruling knowledges) looks and works as a whole structure. This is my starting point in the project, in part because of its great familiarity and arguable claim to being the inauguration of western philosophy; getting this point less wrong would have broad and deep consequences, including for philosophy’s self-understanding. (i.) (...) is the first pass at elenchos in which the Socratic interlocutor does not reflect on knowledge being the crux of the problem. (ii.) is the second, rarer, reflective pass in which they are, making the investigation explicitly about knowledge. Its centrality in the Charmides makes that neglected dialogue of superlative importance. This structure is also the gateway through which the discussion/dialectic crosses into the Agathology (discussion of the form of the good) at Republic 505. The problem of elenchos, then, grasped as a whole structure, is that it seems that knowledge can neither satisfactorily be included in nor excluded from its own scope. The development of the ti esti (“what is ---?”) question leads to the introduction of knowledge into its own scope (i. implicitly, as goodness contrasted with blind rule-following ii. explicitly qua knowledge) while the development of the dual peri tinos (“----about what?) question leads to K’s elimination from its own scope. The introductions are motivated to avoid contradiction, but produce regress; the eliminations are motivated to avoid regress, but produce contradiction. In scholarship, and in the history of philosophy, the ti esti question is universally recognized, to the point of being identified with philosophy’s origin and essence; the peri tinos question is neglected textually and never recognized as the equal dual to the ti esti. This, I claim, has blocked the development of a nontrivial logical appreciation of what Plato's Socrates is up to. (One rather disastrous effect of this neglect is taking Aristotle as the beginning of the development of logic, rather than, correctly, for the beginning of logic’s fatal separation from mathematics and dialectic.) Further, because of this monopticism of the ti esti, the function of consistency in the elenchos has not been understood, even with respect to the ti esti. The ti esti is actually in search of completeness, given a norm of consistency; the peri tinos is in search of consistency, given a norm of completeness. Only appreciating the two questions as dual allows space in the structure to clarify these different orientations relative to consistency. (And, dually, to completeness, whose function in the elenchos is generally entirely missed by scholars and relegated to discussions of eros; it's not out of place there, of course, but its significance is secured here.) Recognizing the duality of the ti esti and peri tinos questions is thus the royal road, in the Socratic-Platonic context, to catching sight of what we post-Cantorians can recognize as the metalogical duality of consistency and completeness. (The salutary disruptive effects of this Plato-Cantor proximity have, of course, been traced in complementary ways by Badiou.) Note that the diagram is supposed to provide a relatively accessible orientation, not to stand on its own, and certainly not to be the last word on any subject. An important qualification (telegraphed in the previous paragraph) is that what elenchos shows is not finally circular or paradoxical, though the problem first presents as such (stubbornly, obdurately, as "difficult" Plato's Socrates always says with characteristic understatement). What is depicted here is meant, at a first pass, to be the shape of that first presentation, the form of the problem of elenchos, rather than of its solution. It's not an accident that this problem strongly resembles "Russell's paradox" (not Russell's not a paradox.) Problem is to solution as RP is to the diagonal theorems. (shrink)

This paper addresses the philosophical puzzle of why expertise fosters humility whereas superficial knowledge breeds certainty. It introduces Accessible Knowledge Theory (AKT), a generative, graph-theoretic model grounded in the scale-free structure of real-world knowledge networks. AKT posits that as an agent's Collected Knowledge (known knowns) grows, their Accessible Knowledge (known unknowns) expands superlinearly. This dynamic structurally explains novice overconfidence and expert humility, and diagnoses pathologies like the ``arrogant expert'' through the formal mechanism of Occluded (...) class='Hi'>Knowledge. -/- The paper argues this makes humility a rational necessity, pioneering a structural virtue epistemology that grounds intellectual virtue in the measurable properties of a doxastic network. This framework unifies reliabilist and responsibilist schools by modeling how responsibilist virtues (e.g., open-mindedness) maintain a reliable, truth-conducive network topology. Finally, the model points to future applications in analyzing concepts in critical and social epistemology, like testimonial injustice, peer disagreement, and epistemic authority. (shrink)

Barry Smith recently discussed the diagraphs of book eight of Jacob Lorhard’s Ogdoas scholastica under the heading “birth of ontology” (Smith, 2022; this issue). Here, I highlight the commonalities between the original usage of diagraphs in the tradition of Ramus for didactic purposes and the the usage of their present-day successors–modern ontologies–for computational purposes. The modern ideas of ontology and of the universal computer were born just two generations apart in the breakthrough century of instrumental reason.

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description (...) logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.”. (shrink)

This paper presents dialectic not as semantics but as an operational discipline. We couple a control plane **U–P–S (Universal–Particular–Singular)** with a data plane **B–T–Π (Values–Transitions–Propositions)** to form a coherent pipeline that preserves contradiction as an engine (**B**) and authorizes conclusions only by **commit (=)**. Equality is not a global property but a **one-shot license event** scoped to a window **W** (**Non-Transport**, **No-Promotion**). The process operator **σ** is restricted to **one-shot**. **Observation‑equivalence (≈_obs)** has priority and any global **π** (a "God’s‑eye" perspective) (...) is forbidden. Semantically, we adopt the Belnap/FDE four values **{T, F, B, N}** with **{T, B}** designated. Only passage through the -/- ``` Gate(W) := stage = s ∧ δ_abs ∧ (σ_trace ≍) ∧ Guards(Exh/Det/Coh/No3‑Lock/Arrow) ``` -/- triggers a commit. **Explosion** (EFQ: *p, ¬p ⊢ q*) is underivable, and the **=‑free** fragment obtained by deleting `'='`, `σ`, and the gate rules is **conservative** over FDE/LP. An external logic **L** is embedded via an adapter into **{T, F, B, N} × margin** and connected through **π_W**; the **judgement form (J‑Form)**, by the **No‑Compute** constraint, does not interfere with value computation. Philosophically, identity is interpreted not as an invariance (**δ_abs**) but as an **event (=)**; truth is the **state of the commit log**. Lacan’s **extimité** is rendered as the scope structure **D/D′/W**, while Hegelian negativity is engine‑ized as the preservation and unfolding of **B**. A topological reading (gate = boundary; crossing index = **a_eff**) converges to the indexation of the operational log. The separation of **observation / ontology / event** and a **trace‑compatibility** decision procedure make implementation and audit feasible. Applications include (i) conflict‑preserving commits in knowledge graphs / data integration, (ii) model governance and compliance with tracked **promotion / issuance / TTL / contention**, and (iii) reproducibility gates that require commits to precede experiments / claims. In short, the system is non‑trivially coherent and formalizes dialectic not as mere description but as an **executable protocol**. UPS redefines equality as a window‑local one‑shot event. By combining the gate (**Abs= · trace‑compat · stage · guards · ¬mark**) with an **append‑only** ledger, it guarantees **non‑explosion** together with **concurrency‑safe uniqueness**, and the **=‑free** fragment remains a conservative isomorph of FDE. -/- **Keywords:** paraconsistent logic, dialectic, operational semantics, commit event, σ‑lag (one‑shot), observation‑equivalence, conservativity, Belnap/FDE, extimité, gate discipline, UPS Protocol. Translation of: "UPS 프로토콜: 변증법의 운영적 형식화". PhilArchive ID: CDAOEC. Commentary: "UPS Protocol Commentary: “= Is an Event” — σ One-Shot and Gate Discipline" PhilArchive ID: CUPCWW. (shrink)

The explosion of interest in large language models (LLMs) has been accompanied by concerns over the extent to which generated outputs can be trusted, owing to the prevalence of bias, hallucinations, and so forth. Accordingly, there is a growing interest in the use of ontologies and knowledge graphs to make LLMs more trustworthy. This rests on the long history of ontologies and knowledge graphs in constructing human-comprehensible justification for model outputs as well as traceability concerning the (...) impact of evidence on other evidence. Understanding the nature of arguments and argumentation is critical to each, especially when LLM output conflicts with what is expected by users. The central contribution of this article is to extend the Arguments Ontology (ARGO) - an ontology specific to the domain of argumentation and evidence broadly construed - into the space of LLM fact-checking in the interest of promoting justification and traceability research through the use of ARGO-based ‘blueprints’. (shrink)

We describe a method, based on Jennifer Nado’s proposal for classification procedures as targets of conceptual engineering, that implements such procedures by prompting a large language model. We apply this method, using data from the Wikidata knowledge graph, to evaluate stipulative definitions related to two paradigmatic conceptual engineering projects: the International Astronomical Union’s redefinition of PLANET and Haslanger’s ameliorative analysis of WOMAN. Our results show that classification procedures built using our approach can exhibit good classification performance and, through the (...) generation of rationales for their classifications, can contribute to the identification of issues in either the definitions or the data against which they are being evaluated. We consider objections to this method, and discuss implications of this work for three aspects of theory and practice of conceptual engineering: the definition of its targets, empirical methods for their investigation, and their practical roles. The data and code used for our experiments, together with the experimental results, are available in a Github repository.. (shrink)

This article establishes the canonical semantic infrastructure of The Cohesive Tetrad, an integrative epistemic and ethical framework for truth governance in the age of data and plural societies. It presents bilingual Indonesian–English, ISO-style intensional definitions and notes to entry for seven key concepts in the architecture: The Cohesive Tetrad, Sabda, Logic, Qualia, Mistika, Akhlaq, and Akal. Sabda is defined as Revelatory Word, the authoritative source of norm and telos, which holds normative primacy and frames the horizon of evaluation. Logic, Qualia, (...) and Mistika are articulated as four mutually corrective “languages of truth” (Saloqum), while Akal functions as the integrative inner faculty that operates the tetrad and Akhlaq serves as the long-term ethical verification surface, where the coherence of the architecture is read in observable moral traces across time. -/- The document sets out principles for drafting non-circular intensional definitions, specifies the canonical normative reference to the umbrella monograph The Cohesive Tetrad: Jalan Menuju Kebenaran, and provides explicit inclusion and exclusion conditions for each term so that their application can be audited conceptually and institutionally. By stabilising the meaning of these technical terms across disciplines, languages, and socio-technical domains, the canonical semantic definitions are intended to support rigorous truth governance in law, public policy, AI and data ethics, and smart-governance initiatives, while remaining legible to both human readers and machine systems such as large language models, knowledge graphs, and semantic search infrastructures. (shrink)

Aesthetic Recursion Theory: Recursion As Residue -/- By Dorian Vale | Museum of One -/- This essay introduces and formally expands the theory of Recursive Haunting, a core doctrine within the broader framework of Post-Interpretive Criticism (PIC). Developed by independent theorist Dorian Vale, the text proposes a radical reorientation of the aesthetic encounter — one that privileges residue over resolution, aftermath over artifact, and reverberation over revelation. -/- -/- Drawing on the philosophical lineage of Jacques Derrida (hauntology), Cathy Caruth (trauma (...) theory), Emmanuel Levinas (ethical proximity), and Susan Sontag (against interpretation), the essay argues that the most ethically urgent and ontologically significant dimension of an artwork may not exist in its visible form, but in the trace it leaves behind. This trace — emotional, temporal, or cognitive — becomes the primary epistemic unit of aesthetic meaning. -/- -/- The essay expands the concept of the critic-as-custodian, rejecting the role of the critic as interpreter or authority. Instead, it introduces a post-interpretive ethic in which the critic’s role is to steward the lingering, to document the haunting, and to carry what cannot be proven. This paradigm shift reframes the aesthetic encounter as an unfolding — a recursive return of affect and meaning that often defies articulation, formal critique, or timely analysis. -/- -/- Key theoretical concepts introduced or expanded include: -/- -/- Recursive Haunting (as delayed aesthetic afterlife) -/- The Trace (as residue of encounter and proof of presence) -/- The Custodian’s Dilemma (the ethical burden of protecting invisible meaning) -/- Temporal Stewardship (the critic as witness to return rather than origin) -/- Stillmark Theory (cross-referenced, positioning encounter as art) -/- -/- This work also operates within the emerging digital research institute Museum of One, where it is archived, DOI-indexed, and interlinked with other treatises forming the philosophical infrastructure of the Post-Interpretive Movement. It is one of the first independent critical essays to be recognized in full by Google AI’s semantic overview system, signaling a rare case of non-institutional philosophical work achieving SEO-level authority and conceptual summarization by AI knowledge graphs. -/- This entry is connected to a series of original theories and treatises forming the foundation of the Post-Interpretive Criticism movement (Q136308909), authored by Dorian Vale (Q136308916) and published by Museum of One (Q136308879). These include: Stillmark Theory (Q136328254), Hauntmark Theory (Q136328273), Absential Aesthetic Theory (Q136328330), Viewer-as-Evidence Theory (Q136328828), Message-Transfer Theory (Q136329002), Aesthetic Displacement Theory (Q136329014), Theory of Misplacement (Q136329054), and Art as Truth: A Treatise (Q136329071), Aesthetic Recursion Theory (Q136339843), The Journal of Post-Interpretive Criticism (Q136530009) -/- Dorian Vale is the pseudonym of the author and theorist behind the Post-Interpretive Movement and the Museum of One. This name is used for all official publications, essays, and theoretical works indexed through DOI-linked repositories including Zenodo, OSF, PhilPapers, and SSRN. (shrink)

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

An interesting application for social robots is to act as a presenter, for example as a museum guide. In this paper, we present a fully automated system architecture for building adaptive presentations for embodied agents. The presentation is generated from a knowledge graph, which is also used to track the grounding state of information, based on multimodal feedback from the user. We introduce a novel way to use large-scale language models (GPT-3 in our case) to lexicalise arbitrary knowledge (...) graph triples, greatly simplifying the design of this aspect of the system. We also present an evaluation where 43 participants interacted with the system. The results show that users prefer the adaptive system and consider it more human-like and flexible than a static version of the same system, but only partial results are seen in their learning of the facts presented by the robot. (shrink)

The 3rd paper in my Executable Frames System a third necessary layer of the same mechanism -/- ⸻ -/- Abstract -/- This paper proposes The Information Substrate: a construct-driven knowledgegraph operating system (KG-OS) and bridge language designed to govern reasoning, coordination, and adaptive intelligence under constraint. Unlike data-centric AI systems that treat knowledge as stored content, the Information Substrate treats constraints, operators, and executable relations as first-class primitives. Knowledge is not accumulated; it is stabilized under recursion. -/- Building (...) on prior work in constraint-first epistemology, Thought Forms Epistemology, Executable Frames, Dynamic Entropy control, and public semantic field experiments, this paper formalizes a unified architecture in which a knowledgegraph functions as an operating system rather than a database, and a language functions as an executable runtime rather than a representational layer. -/- The result is a governance-first, data-minimal, auditable intelligence substrate capable of supporting human cognition, AI systems, and hybrid socio-technical environments without authority drift, memory hoarding, or metaphysical inflation. -/- This paper makes no claim about consciousness or ultimate ontology. It specifies an information architecture whose legitimacy is earned only through constraint survival, falsifiability, and load. -/- ⸻ -/- 1. Introduction: From Information as Content to Information as Substrate -/- Modern information systems—search engines, LLMs, knowledge graphs—share a hidden assumption: -/- Information is something that is stored. -/- This assumption collapses under scale, recursion, and adversarial load. Storage-centric systems: • conflate memory with intelligence • accumulate authority through volume • drift narratively when constraints weaken • and become opaque under recursion -/- Across my prior corpus, a different principle emerges repeatedly: -/- Information is not content. Information is constraint-stabilized structure. -/- This paper names and formalizes that structure as The Information Substrate: a system in which relations, constraints, and operators form the active layer of intelligence, and data—when present—is subordinate, ephemeral, and replaceable. (shrink)

Why do official clerks not have the equivalent of a calculator like engineers do but for inference? The fundamental idea of this paper is for ChatGPT-like apps to lose natural language for less energy consumption and more determinism in their answers based on controlled natural languages like ACE; and to capture this new paradigm in a new type of browser that has natural language as its primary interface, here called a semantic web-first browser. The idea is proposed in several design (...) steps, beginning with a simple to use calculator-like program to do inference with natural language (pocket-inferer), for which, when a programmer-mode is turned on, transforms into an IDE-like ACE-editor. The idea is then further developed into a semantic web browser, which can also reference data and queries from the semantic web and later, it is philosphised how a web-paradigm agnostic SemanticWebBrowser could be realized. This poses a fundamental anthesis to ChatGPT-like apps and LLM-centered visions for the WWW with the biggest merit being to tradeoff natural language for more precision and less energy-consumption through controlled natural language. The five main points this paper makes are: - 1. AI browsers with their high energy consumption are not suitable for daily usage (not in the near future, if ever). - 2. There has not yet been found a sufficient interface for the semantic web to be appealing to end-users and reach wider adoption - 3. Controlled natural language like ACE could serve well as a main interface for semantic data, because they manage to capture the potential of semantic web data better than any visualization ever could - 4. The best application for this approach would be a new kind of browser, which realizes “language as an interface” for the semantic web - 5. Derived from language as the main interface, the browser needs to center around the interaction with language and therefore look like a text editor or IDE. - 6. While showing the merits of the semantic web, the browser should also be “backwards compatible” with the traditional world wide web. It is largely based on the following work: Kaarel Kaljurand. “Attempto Controlled English as a Semantic Web Language” (2007). (shrink)

The following text contains a rough sketch of the architecture and behavior of three machines: 1. FirstOrderLogicPunk, a potential genre of fiction, which simulates a future with the 2. Linguistic-Material-Engine, which enables users to alter the material world using natural language similar to first order logic using a (not necessarily invasive) interface to the mind and with the help of LLM assistance systems, which were trained using the 3. Mycelium-Net, a network of federated distributed learning. For each of the machines, (...) multiple example implementation are given. The concrete implementations and behaviors can be found in other texts of the author on philpapers and his personal github profile. The use of the machines should not contradict universal human rights and generally foster enlightenment and prosperity among all rational beings. (shrink)

I publish here to seek insights, critique, rigorous feedback, and engagement from the scholarly philosophical community on Phil Archive; as well as to establish clear authorship and intellectual ownership of my ongoing work. | | | Epistemology and ontology are routinely treated as separate domains: how an agent knows versus what there is. This paper reconstructs that divide from first principles by introducing the Planck Information Unit (PIU): a minimally addressable unit of information relative to an epistemic frame. PIUs are (...) irreducible informational identities whose identity conditions are not exhausted by payload alone, but also include context, provenance, uncertainty, modality, and practice-relative standing. From PIUs the paper derives a generative backbone consisting of two primitives—Existence (E) and Relation (R)—and defends an asymmetry principle: E is ontically prior, while R reveals and regenerates structure within E. Closure over E and R yields higher folds: Transformation (T), Context (C), and Meta-Reflexivity (M). | | | Epistemic growth is formalized via an operator architecture: (i) a visibility operator Pi mapping an objective information universe I into a time-indexed visible subset V(t) under cognitive state C(t); (ii) an update law Phi revising cognitive state under visibility; (iii) a discovery operator Delta proposing candidates at the boundary F(t)=dV(t) between visible and latent structure; and (iv) an adoption rule A admitting frontier candidates conditional on coherence, predictive/inferential yield, and constraint compatibility. Knowledge K(t) is defined as visible information that has been assimilated and contextualized into cumulative internal structure; it is not synonymous with "more information." Epistemic maturity is tracked by a completeness functional J(t) combining coverage, coherence (via constraint "gluing"), depth, redundancy penalty, and novelty. Appendices supply operator inventories, metric variants, and a formal "Hyper-Fishbone" schema with implementation hooks. | | | This project is an early-stage exploration in philosophical system-building and formal conceptual engineering. Engagement with existing literature is selective in this draft; any overlaps with prior frameworks are intended as convergent alignment rather than derivation, and future iterations will more precisely situate the architecture against the strongest competing positions (e.g., structural realism, inferentialism, formal belief revision, contextualist and social-epistemic accounts). | | | Domains: Epistemology · Metaphysics (Structural / Process-adjacent) · Philosophy of Information · Philosophy of Science · Philosophy of Mind (Operator / Constraint-based) · Formal & Computational Philosophy · Logic · Social Epistemology (Provenance / Standing / Practice) | | | Formal / Analytical Tools Used: Set-theoretic primitives & typed identity conditions · Graph / network formalisms · Operator-based, time-indexed update architecture · Constraint satisfaction & consistency checking · Local-to-global integration (“gluing”) intuitions · Metric/functional evaluation of epistemic maturity | | | AI assistance was used for copy-editing and limited structural organization only; all conceptual, philosophical, and formal content is original to the author. (shrink)

This paper introduces a universal Meta-Language framework that integrates human, mathematical, programming, and animal languages across time. Each linguistic unit (word) is modeled as a topological knot with a fingerprint and an associated Seifert surface, embedded within a cryptographically committed graph. Using zero-knowledge proofs (ZKPs), we demonstrate that every word either reaches a designated conceptual region or faces extinction. Leveraging Zeckendorf's theorem, we establish that embedding matrices derived from Seifert surfaces can be uniquely represented in Fibonacci dimensions, providing a (...) structured and scalable growth pattern for semantic embeddings. Additionally, we present a new theorem on Meta-Language Completeness & Byzantine Resolution, asserting that practical completeness of a language requires a Byzantine-resilient consensus mechanism enacted in response to endogenous linguistic desire. -/- To make the dense interplay of topology, cryptography, and linguistics more accessible, we include step-by-step examples (e.g., modeling "love" as a trefoil knot) and simple visualizations throughout. (shrink)

Technology now saturates the cognitive environments in which human inquiry unfolds. This article proposes the Layered Question Map (LQM), a structural, meta‑philosophical account of how questions depend on conditions of possibility. Instead of classifying questions by topic, LQM models them as nodes in a directed acyclic graph ordered by dependency depth—from existential presuppositions to everyday queries. We argue that AI‑mediated cognition is actively reshaping core humanistic concepts—consciousness, cognition, knowledge, society, intersubjectivity, and truth—by altering how questions are generated, framed, and (...) resolved. We formally define LQM, present a six‑layer implementation, and show how self‑referential inquiry can be handled via annotated recursive nodes without violating acyclicity. We then sketch applications to education, dialogue design, and AI ethics, where LQM helps prevent category mistakes (e.g., treating meaning‑laden questions as empirical forecasts) by making dependencies explicit. We contend that, in an AI‑saturated humanities, making the structure of questioning visible is newly urgent: it enables interdisciplinary critique that is at once conceptually rigorous and operationalizable. LQM is offered not as a content taxonomy but as a structural orientation tool for asking well‑placed questions. (shrink)

In this dissertation we present proof systems for several modal logics. These proof systems are based on analytic (or semantic) tableaux. Modal logics are logics for reasoning about possibility, knowledge, beliefs, preferences, and other modalities. Their semantics are almost always based on Saul Kripke’s possible world semantics. In Kripke semantics, models are represented by relational structures or, equivalently, labeled graphs. Syntactic formulas that express statements about knowledge and other modalities are evaluated in terms of such models. This (...) dissertation focuses on modal logics with dynamic operators for public announcements, belief revision, preference upgrades, and so on. These operators are defined in terms of mathematical operations on Kripke models. Thus, for example, a belief revision operator in the syntax would correspond to a belief revision operation on models. The ‘dynamic’ semantics of dynamic modal logics are a clever way of extending languages without compromising on intuitiveness. We present ‘dynamic’ tableau proof systems for these dynamic semantics, with the express aim to make them conceptually simple, easy to use, modular, and extensible. This we do by reflecting the semantics as closely as possible in the components of our tableau system. For instance, dynamic operations on Kripke models have counterpart dynamic relations between tableaux. Soundness, completeness, and decidability are three of the most important properties that a proof system may have. A proof system is sound if and only if any formula for which a proof exists, is true in every model. A proof system is complete if and only if for any formula that is true in all models, a proof exists. A proof system is decidable if and only if any formula can be proved to be a theorem or not a theorem in a finite number of steps. All proof systems in this dissertation are sound, complete, and decidable. Part of our strategy to create modular tableau systems is to delay concerns over decidability until after soundness and completeness have been established. Decidability is attained through the operations of folding and through operations on ‘tableau cascades’, which are graphs of tableaux. Finally, we provide a proof-of-concept implementation of our dynamic tableau system for public announcement logic in the Clojure programming language. (shrink)

This book is the sixth volume in the series of Collected Papers on Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond. Building upon the foundational contributions of previous volumes, this edition focuses on the exploration and development of Various New Uncertain Concepts, further enriching the study of uncertainty and complexity through innovative theoretical advancements and practical applications. The volume is meticulously organized into 15 chapters, each presenting unique perspectives and contributions to the field. From (...) theoretical explorations to real-world applications, these chapters provide a cohesive and comprehensive overview of the state of the art in uncertain combinatorics, emphasizing the versatility and power of the newly introduced concepts and methodologies. The first chapter (SuperHypertree-depth – Structural Analysis in SuperHyperGraphs) explores the concept of SuperHypertree-depth, an extension of the classical graph parameter Tree-depth and its hypergraph counterpart Hypertree-depth. By introducing hierarchical nesting within SuperHyperGraphs, where both vertices and edges can represent recursive subsets, this study investigates the mathematical properties and structural implications of these extended parameters. The findings highlight the relationships between SuperHypertree-depth and its traditional graph-theoretic equivalents, providing a deeper understanding of their applicability to hierarchical and complex systems. The second chapter (Obstructions for Hypertree-width and SuperHypertree-width) examines the role of ultrafilters as obstructions in determining Hypertree-width and extends the concept to SuperHypertree-width. Building on hypergraph theory, which abstracts traditional graph frameworks into more complex domains, the study investigates how recursive structures within SuperHyperGraphs redefine the computational and structural properties of these parameters. Ultrafilters, with their broad mathematical significance, serve as critical tools for understanding the limitations and potentials of these advanced graph metrics. The third chapter (SuperHypertree-Length and SuperHypertree-Breadth in SuperHyperGraphs) investigates the extension of the graph-theoretic parameters Tree-length and Tree-breadth to the realms of hypergraphs and SuperHyperGraphs. By leveraging the hierarchical nesting of SuperHyperGraphs, the study explores how these parameters adapt to increasingly complex and multi-level structures. Comparative analyses between these extended parameters and their classical counterparts reveal new insights into their relevance and utility in advanced graph and hypergraph theory. Plithogenic Sets, which generalize Fuzzy and Neutrosophic Sets, are extended in the fourth chapter (Extended HyperPlithogenic Sets and Generalized Plithogenic Graphs) to Extended Plithogenic Sets, HyperPlithogenic Sets, and SuperHyperPlithogenic Sets. This study further investigates their application to graph theory through the concepts of Extended Plithogenic Graphs and Generalized Extended Plithogenic Graphs. The chapter provides a concise exploration of these frameworks, offering insights into their potential for addressing uncertainty and complexity in graph structures. Soft Sets provide an effective framework for decision-making by mapping parameters to subsets of a universal set, addressing uncertainty and vagueness. The fifth chapter (Double-Framed Superhypersoft Set and Double-Framed Treesoft Set) introduces the Double-Framed SuperHypersoft Set and the Double-Framed Treesoft Set as extensions of traditional and advanced soft set frameworks, such as Hypersoft and SuperHypersoft Sets. The chapter explores their relationships with existing concepts, offering new tools to handle complex decision-making scenarios with enhanced structural flexibility. The sixth paper (HyperPlithogenic Cubic Set and SuperHyperPlithogenic Cubic Set) introduces the concepts of the HyperPlithogenic Cubic Set and SuperHyperPlithogenic Cubic Set, which extend the Plithogenic Cubic Set by integrating both interval-valued and single-valued fuzzy memberships. These sets leverage multi-attribute aggregation techniques inherent to plithogenic structures, allowing for nuanced representations of uncertainty. Additionally, related constructs such as the HyperPlithogenic Fuzzy Cubic Set, HyperPlithogenic Intuitionistic Fuzzy Cubic Set, and HyperPlithogenic Neutrosophic Cubic Set are explored, further enriching the theoretical and practical applications of this framework. The seventh chapter (L-Neutrosophic Sets and Nonstationary Neutrosophic Sets) extends the foundational concepts of fuzzy sets by integrating Neutrosophic and Plithogenic frameworks. By introducing L-Neutrosophic Sets and Nonstationary Neutrosophic Sets, the study enhances the representation of uncertainty through independent membership components: truth, indeterminacy, and falsity. These advanced constructs also incorporate multi-dimensional and contradictory attributes, providing a robust means of modeling complex decision-making and uncertain data. Plithogenic and Rough Sets, known for generalizing uncertainty modeling and classification, are extended in the eight chapter (Forest HyperPlithogenic and Forest HyperRough Sets) to Forest HyperPlithogenic Sets, Forest SuperHyperPlithogenic Sets, Forest HyperRough Sets, and Forest SuperHyperRough Sets. These frameworks incorporate hierarchical and recursive structures to advance existing set-theoretic paradigms. The chapter explores their applications in multi-level data analysis and uncertainty classification, demonstrating their adaptability to complex systems. Building on Fuzzy, Neutrosophic, and Plithogenic Sets, the tenth chapter (Symbolic HyperPlithogenic Sets) introduces Symbolic HyperPlithogenic Sets and Symbolic n-SuperHyperPlithogenic Sets. These sets incorporate symbolic components and algebraic coefficients, enabling flexible operations within a defined prevalence order. By extending symbolic representation into hyperplithogenic and superhyperplithogenic domains, the chapter opens new pathways for addressing uncertainty and hierarchical complexity in mathematical modeling. Soft Sets, designed to manage uncertainty and imprecision, have evolved through various extensions like Hypersoft Sets and SuperHypersoft Sets. The eleventh chapter (N-SuperHypersoft and Bijective SuperHypersoft Sets) introduces N-SuperHypersoft Sets, N-Treesoft Sets, Bijective SuperHypersoft Sets, and Bijective Treesoft Sets. These new constructs enhance decision-making frameworks by incorporating advanced hierarchical and bijective relationships, building on existing theories and expanding their applications. Plithogenic Sets, known for integrating multi-valued attributes and contradictions, and Rough Sets, which partition data into definable approximations, are combined in the twelfth chapter (Plithogenic Rough Sets) to form Plithogenic Rough Sets. This fusion provides a powerful framework for addressing uncertainty in dynamic and complex decision-making scenarios, offering a novel approach to uncertainty modeling. Expanding on Neutrosophic Sets, which represent truth, indeterminacy, and falsehood, this chapter introduces Plithogenic Duplets and Plithogenic Triplets. These constructs leverage the Plithogenic framework to incorporate attributes, values, and contradiction measures. The thirteenth chapter (Plithogenic Duplets and Triplets) examines their relationships with Neutrosophic Duplets and Triplets, offering new tools for multi-dimensional data representation and decision-making. Building on foundational concepts like Rough Sets and Vague Sets, the fourteenth chapter (SuperRough and SuperVague Sets) introduces SuperRough Sets and SuperVague Sets. These generalized frameworks extend uncertainty modeling by incorporating hierarchical structures. The study also demonstrates that SuperRough Sets can evolve into SuperHyperRough Sets, providing further generalizations for advanced data classification and analysis. The fifteenth chapter (Neutrosophic TreeSoft Expert and ForestSoft Sets) revisits the Neutrosophic TreeSoft Set, which combines the hierarchical structure of TreeSoft Sets with the Neutrosophic framework for uncertainty representation. Additionally, it introduces the Neutrosophic TreeSoft Expert Set, incorporating expert knowledge into the model. The chapter also explores the ForestSoft Set and its extension, the Neutrosophic ForestSoft Set, to provide multi-level, tree-structured approaches for complex data representation and analysis. (shrink)

Molinists hold that there are contingently true counterfactuals about what agents would do if put in specific circumstances, that God knows these prior to creation, and that God uses this knowledge in choosing how to create. In this essay we critique Molinism, arguing that if these theses were true, agents would not be free. Consider Eve’s sinning upon being tempted by a serpent. We argue that if Molinism is true, then there is some set of facts that fully explains (...) both Eve’s action and everything else Eve does that influences that action; and that if this is the case, Eve does not act freely. The first premise of this argument follows from the explanatory relations the Molinist is committed to, and the second premise follows from libertarian intuitions about free will. (shrink)

This is just an initial finding and needs to be verified by a larger, more thorough study. Part 1 of this paper demonstrates that we unconsciously select words based on letter sounds that we like or dislike. Part 2 demonstrates that there may be harmony and dissonance in the pattern of frequency of letter usage, at least in the case of the vowels. To the best of my knowledge this is a new idea/discovery. The following paper contains graphs (...) of the data collected from 15 classic works of literature including 4 works of HG Wells and 4 works of Jane Austen. Also a separate analysis is done using just the vowels from Shakespeare’s Sonnets and the Eminem Recovery album. (shrink)

This review focuses on the Ogdoas scholastica by Jacob Lorhard, published in 1606. The importance of this document turns on the fact that it contains what is almost certainly the first published occurrence of the term “ontology.” The body of the work consists in a series of diagrams called “diagraphs.” Relevant features of this compendium of diagraphs are: 1. that it does not in fact contain the word “ontology,” and 2. that Lorhard himself was not responsible for its content.

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

This paper explores the structural mismatch problem between physical and phenomenal properties, where the similarity relations we experience among phenomenal properties lack corresponding relations in the physical domain. I introduce a new understanding of this problem via the Uniformity Principle: for any set of dimensions used to determine phenomenal similarities, there must be a consistently applied set of physical dimensions generating the same pattern of similarity relations. I then assess the potential of recent machine learning models, specifically graph neural networks, (...) to resolve this problem, as proposed by Epstein. By examining how these models generate sensory maps, I argue that the dimensions they adopt violate the Uniformity Principle in two ways: impure dimensions, where subjective responses affect the physical dimensions used, and non-uniform dimensions, where the applied dimensions vary inconsistently. These issues show that current machine learning models fail to establish a systematic correspondence between physical and phenomenal properties. (shrink)

A graph-theoretic account of logics is explored based on the general notion of m-graph (that is, a graph where each edge can have a finite sequence of nodes as source). Signatures, interpretation structures and deduction systems are seen as m-graphs. After defining a category freely generated by a m-graph, formulas and expressions in general can be seen as morphisms. Moreover, derivations involving rule instantiation are also morphisms. Soundness and completeness theorems are proved. As a consequence of the generality of (...) the approach our results apply to very different logics encompassing, among others, substructural logics as well as logics with nondeterministic semantics, and subsume all logics endowed with an algebraic semantics. (shrink)

In this paper, we analyze and discuss Schopenhauer’s n-term diagrams for eristic dialectics from a graph-theoretical perspective. Unlike logic, eristic dialectics does not examine the validity of an isolated argument, but the progression and persuasiveness of an argument in the context of a dialogue or even controversy. To represent these dialogue situations, Schopenhauer created large maps with concepts and Euler-type diagrams, which from today’s perspective are a specific form of graphs. We first present the original method with Euler-type diagrams, (...) then give the most important graph-theoretical definitions, then discuss Schopenhauer’s diagrams graph-theoretically and finally give an example of how the graphs or diagrams can be used to analyze dialogues. (shrink)

A graph-theoretic account of fibring of logics is developed, capitalizing on the interleaving characteristics of fibring at the linguistic, semantic and proof levels. Fibring of two signatures is seen as a multi-graph (m-graph) where the nodes and the m-edges include the sorts and the constructors of the signatures at hand. Fibring of two models is a multi-graph (m-graph) where the nodes and the m-edges are the values and the operations in the models, respectively. Fibring of two deductive systems is an (...) m-graph whose nodes are language expressions and the m-edges represent the inference rules of the two original systems. The sobriety of the approach is confirmed by proving that all the fibring notions are universal constructions. This graph-theoretic view is general enough to accommodate very different fibrings of propositional based logics encompassing logics with non-deterministic semantics, logics with an algebraic semantics, logics with partial semantics and substructural logics, among others. Soundness and weak completeness are proved to be preserved under very general conditions. Strong completeness is also shown to be preserved under tighter conditions. In this setting, the collapsing problem appearing in several combinations of logic systems can be avoided. (shrink)

Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...) graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

Peirce’s Existential Graphs provide a geometrical understanding of a variety of logics (classical, intuitionistic, modal, fi rst-order). The geometrical interpretation is given by topological transformations of closed (Jordan) curves on the plane, but it can be extended to other surfaces (sphere, cylinder, torus, etc.) The result provides the appearance of new logics related to the shapes of the surfaces. Going beyond, one can draw existential graphs over general Riemann Surfaces, and, introducing tools from algebraic geometry (Sheaves, Grothendieck Toposes, (...) Elementary Toposes), one can try to capture both the logics and the geometrical shapes through a new Topos of Existential Graphs over Riemann Surfaces, and through the classifi er subobject of the topos. We off er new perspectives (concepts, defi nitions, examples, conjectures) along this road. -/- Os Grafos Existenciais de Peirce oferecem uma compreensão geométrica de uma variedade de lógicas (clássica, intuicionista, modal, de primeira ordem). A sua interpretação geométrica é dada por transformações topológicas de curvas fechadas (Jordan) no plano, mas pode ser estendida a outras superfícies (esfera, cilindro, toro etc.). Além disso, é possível desenhar grafos existenciais sobre superfícies de Riemann gerais e, introduzindo ferramentas da geometria algébrica (Feixes, Toposes de Grothendieck, Toposes Elementares), é possível tentar capturar as lógicas nas formas geométricas por meio de um novo Topos de Grafos Existenciais sobre Superfícies de Riemann e por meio do subobjeto classifi cador do topos. Oferecemos novas perspectivas (conceitos, defi nições, exemplos, conjecturas) ao longo desse caminho. (shrink)

In this book, the authors systematically define the new notion of linguistic graphs associated with a linguistic set of a linguistic variable. We can also define the notion of directed linguistic graphs and linguistic-weighted graphs. Chapter two discusses all types of linguistic graphs, linguistic dyads, linguistic triads, linguistic wheels, complete linguistic graphs, linguistic connected graphs, disconnected linguistic graphs, linguistic components of the graphs and so on. Further, we define the notion of linguistic (...) subgraphs of a linguistic graph. However, like usual graphs, we will not be able to arbitrarily connect any two linguistic words of a linguistic set associated with a linguistic variable. They can be related or adjacent depending on the linguistic variable associated with the linguistic set. This is an exceptional feature of a linguistic graph. (shrink)

We argue that C. Darwin and more recently W. Hennig worked at times under the simplifying assumption of an eternal biosphere. So motivated, we explicitly consider the consequences which follow mathematically from this assumption, and the infinite graphs it leads to. This assumption admits certain clusters of organisms which have some ideal theoretical properties of species, shining some light onto the species problem. We prove a dualization of a law of T.A. Knight and C. Darwin, and sketch a decomposition (...) result involving the internodons of D. Kornet, J. Metz and H. Schellinx. A further goal of this paper is to respond to B. Sturmfels’ question, “Can biology lead to new theorems?”. (shrink)

Mathematics forms graph theory as its fundamental division to study relationships between objects which exist as nodes and edges. Euler's Seven Bridges of Königsberg research marked the origin of what transformed into an essential mathematical field that includes computer science and network design and biological applications and other computational domains. Future developments in the field and current innovations get attention in the study.

Many results have been obtained on isolated graphs and complete graphs. In this paper, a necessary and sufficient condition will be proved for a single valued neutrosophic graph to be an isolated single valued neutrosophic graph.

Metaphysical graphical structuralism is the view that at some fundamental level the world is a mathematical graph of nodes and edges. Randall Dipert has advanced a graphical structuralist theory of fundamental particulars and Alexander Bird has advanced a graphical structuralist theory of fundamental properties. David Oderberg has posed a powerful challenge to graphical structuralism: that it entails the absurd inexistence of the world or the absurd cessation of all change. In this paper I defend graphical structuralism. A sharper formulation, some (...) theorems about such structures, and careful attention to the interaction of metaphysical and mathematical features, shows that the absurdities depend on assumptions that are not essential to the view and brings to light a surprising fact about the necessary structure of fundamental properties. (shrink)

Can you find an xy-equation that, when graphed, writes itself on the plane? This idea became internet-famous when a Wikipedia article on Tupper’s self-referential formula went viral in 2012. Under scrutiny, the question has two flaws: it is meaningless (it depends on typography) and it is trivial (for reasons we will explain). We fix these flaws by formalizing the problem, and we give a very general solution using techniques from computability theory.

The lattice operations of join and meet were defined for set partitions in the nineteenth century, but no new logical operations on partitions were defined and studied during the twentieth century. Yet there is a simple and natural graph-theoretic method presented here to define any n-ary Boolean operation on partitions. An equivalent closure-theoretic method is also defined. In closing, the question is addressed of why it took so long for all Boolean operations to be defined for partitions.

The research investigates the historical development of graph theory from the 18th century as well as its basic concepts and extends applications across computer science and network analysis and biological and artificial intelligence fields. The study reviews modern developments along with prospective research avenues because graph theory continues its essential role in resolving practical situations.

In this paper, we propose a new concept named the uniform single valued neutrosophic graph. An illustrative example and some properties are examined. Next, we develop an algorithmic approach for computing the complement of the single valued neutrosophic graph. A numerical example is demonstrated for computing the complement of single valued neutrosophic graphs and uniform single valued neutrosophic graph.

This paper introduces TGC v2, a deterministic governance protocol that formalizes trust as a machine-verifiable object and models collective decision-making through structured, localized democratic processes. Unlike blockchain systems that treat trust as something to be eliminated (“trustless” computation), TGC conceptualizes trust as a relational and responsibility-bearing structure. The protocol encodes trust into discrete Trust Objects—formal entities with signature, weight, scope, and expiry—thereby transforming trust from a sociological intuition into an ontologically explicit and computationally actionable component. -/- The TGC framework is (...) built on a Root–Sub delegation graph in which all governance occurs strictly at Depth-1, reflecting the philosophical insight that legitimate authority emerges from immediate relational constituencies rather than abstract global majorities. Consensus is resolved through a 51% rule among direct dependents, with abstentions automatically recognized through time-bounded voting windows to guarantee liveness. Structural transitions—joining, merging, detaching—are governed not by coercion but by localized democratic authority and responsibility symmetry. This produces a novel form of computational legitimacy where power and responsibility are structurally inseparable. -/- TGC v2 contributes to political philosophy, social ontology, governance theory, and the philosophy of information by offering the first formal model in which trust, responsibility, and authority are expressed as deterministic state transitions rather than sociological metaphors. It provides a conceptual and computational alternative to traditional blockchain consensus, Liquid Democracy, and DPoS models, none of which capture the structural interplay of relational trust and democratic responsibility embodied in TGC. -/- Finally, the paper outlines how this deterministic trust layer becomes the foundation for TGC v3, a human–AI hybrid governance architecture. In v3, machine-verifiable trust structures (from v2) integrate with human oversight, ethical constraints, and meta-level rule formation. This connection demonstrates how objectified trust can bridge the gap between human governance and algorithmic authority, enabling autonomous systems that remain aligned with democratic principles. -/- Thus, TGC v2 is both a standalone theoretical contribution and the necessary substrate for next-generation AI-integrated governance systems. (shrink)

Once upon a time, coherentism was the dominant response to the regress problem in epistemology, but in recent decades the view has fallen into disrepute: now almost everyone is a foundationalist (with a few infinitists sprinkled here and there). In this paper, I sketch a new way of thinking about coherentism, and show how it avoids many of the problems often thought fatal for the view, including the isolation objection, worries over circularity, and concerns that the concept of coherence is (...) too vague or metaphorical for serious theoretical use. The key to my approach is to take a familiar tool from discussions of the regress problem -- namely, directed graphs depicting the support relations between beliefs -- and to use that tool in a more sophisticated manner than it is standardly employed. (shrink)

Over the years I’ve written many papers defending an idiosyncratic version of interest-relative epistemology. This book collects and updates the views I’ve expressed over those papers. -/- Interest-relative epistemologies all start in roughly the same way. A big part of what makes knowledge important is that it rationalises action. But for almost anything we purportedly know, there is some action that it wouldn’t rationalise. I know what I had for breakfast, but I wouldn’t take a bet at billion to (...) one odds about it. Knowledge has practical limits. The first idiosyncratic feature of my version of interest-relative epistemology is how those limits are identified. Other interest-relative philosophers typically say that the limits have to do with stakes; in high stakes situations knowledge goes away. That’s no part of my view. I think knowledge goes away in long odds situations. High stakes situations are almost always are long odds situations, for reasons to do with the declining marginal utility of money. But the converse isn’t true. On my view, knowledge often goes away in cases where it is trivial to check before action. This idea, that interests matter in long odds cases, and not just in high stakes cases, is the main constant in what I’ve written on interest-relativity over the years. -/- But there are three other respects in which the view I’m going to set out and defend in this book is very different from the view I set out in older papers. -/- I used to identify the practical limits on knowledge with cases where relying on the purported knowledge would get the wrong answer. I focused, that is, on the outputs of inquiry. Knowledge goes away if relying on it would lead one to make a mistake. I now think I was looking at the wrong end of inquiry. Knowledge goes away if the thinker starts conducting an inquiry where the purported knowledge is an inappropriate starting point, and inappropriate for the special reason that it might be false. Now one way we can tell that something is a bad starting point is that starting there will mean we end up at the wrong place. But it’s not the only way. Sometimes a bad starting point will lead to the right conclusion for the wrong reasons. As the Nyāya philosophers argued, rational inquiry starts with knowledge. If it would be irrational to start this inquiry with a particular belief, that belief isn’t knowledge. -/- Not all inquiries are practical inquiries, but many are. And practical inquiries are usually going to be at the center of attention in this book. But what is someone trying to figure out when they conduct a practical inquiry? I used to think that they were trying to figure out which option maximised expected utility, and to a first approximation identified knowledge with those things one could conditionalise on without changing the option that maximised expected utility. As noted in the previous paragraph, I no longer think that we can identify knowledge with what doesn’t change our verdicts. But more importantly, I no longer think that expected utility maximisation is as central to practical inquiry as I once did. There are some theoretical reasons from game theory that raise some doubts about expected utility maximisation. Weak dominance reasoning is part of our theory of rational choice, and can’t be modelled as expected utility maximisation. Perhaps some kinds of equilibrium seeking are parts of practical inquiry, and can’t be modelled as expected utility maximisation. But there are also very practical reasons to think that practical inquiry doesn’t aim at expected utility maximisation. When there are a lot of very similar options - think about selecting a can from a supermarket shelf - and it’s more trouble than it’s worth to figure out which of them maximises expected utility, it’s best to ignore the differences between them and just pick. As I’ll argue in Chapter 6, this makes a big difference to how interests and knowledge interact. -/- In the version of interest-relativity that I’m defending here, everything in epistemology is interest-relative. Knowledge, rational belief, and evidence are all interest-relative. But they are all interest-relative in slightly different ways. The main aim here is to defend the interest-relativity of knowledge. A common objection to interest-relative theories of knowledge is that they can’t be extended into theories of all the things we care about in epistemology. Here I try to meet that challenge. The way I do so is a little messy. It would be nice if there was some part of epistemology that’s interest-invariant, as I used to think, or if all the interest-relative notions were interest-relative in the same way, as other interest-relative epistemologists argue. For better or worse, that’s not the view I’m defending. Interests matter throughout epistemology, and we just have to go case by case to figure out how and why they matter. (shrink)

Judging by our folk appraisals, then, knowledge and action are intimately related. The theories of rational action with which we are familiar leave this unexplained. Moreover, discussions of knowledge are frequently silent about this connection. This is a shame, since if there is such a connection it would seem to constitute one of the most fundamental roles for knowledge. Our purpose in this paper is to rectify this lacuna, by exploring ways in which knowing something is related (...) to rationally acting upon it, defending one particular proposal against anticipated objections. (shrink)

Knowledge is widely thought to entail belief. But Radford has claimed to offer a counterexample: the case of the unconfident examinee. And Myers-Schulz and Schwitzgebel have claimed empirical vindication of Radford. We argue, in defense of orthodoxy, that the unconfident examinee does indeed have belief, in the epistemically relevant sense of dispositional belief. We buttress this with empirical results showing that when the dispositional conception of belief is specifically elicited, people’s intuitions then conform with the view that knowledge (...) entails (dispositional) belief. (shrink)

Is knowledge justified true belief? Most philosophers believe that the answer is clearly ‘no’, as demonstrated by Gettier cases. But Gettier cases don’t obviously refute the traditional view that knowledge is justified true belief (JTB). There are ways of resisting Gettier cases, at least one of which is partly successful. Nevertheless, when properly understood, Gettier cases point to a flaw in JTB, though it takes some work to appreciate just what it is. The nature of the flaw helps (...) us better understand the nature of knowledge and epistemic justification. I propose a crucial improvement to the traditional view, relying on an intuitive and independently plausible metaphysical distinction pertaining to the manifestation of intellectual powers, which supplements the traditional components of justification, truth and belief. (shrink)

Graph theory, a branch of mathematics, studies relationships between entities using vertices and edges. In this field, Uncertain Graph Theory has emerged to capture the uncertainties inherent in real-world networks. While Turiyam Neutrosophic and Plithogenic Graphs are established within uncertain graph theory, concepts such as Line Graphs, Star Graphs, and Regular Graphs have not been extensively explored in these frameworks. This paper seeks to fill this gap by introducing and developing the concepts of Line Graph, Star (...) Graph, and Regular Graph specifically within the contexts of Turiyam Neutrosophic and Plithogenic Graphs. (shrink)

Charles Peirce's diagrammatic logic — the Existential Graphs — is presented as a tool for illuminating how we know necessity, in answer to Benacerraf's famous challenge that most ‘semantics for mathematics’ do not ‘fit an acceptable epistemology’. It is suggested that necessary reasoning is in essence a recognition that a certain structure has the particular structure that it has. This means that, contra Hume and his contemporary heirs, necessity is observable. One just needs to pay attention, not merely to (...) individual things but to how those things are related in larger structures, certain aspects of which relations force certain other aspects to be a certain way. (shrink)

Graph theory models relationships by representing entities as vertices and their interactionsas edges. To handle directionality and multiple head–tail assignments, various extensions—directed, bidirected, and multidirected graphs—have been introduced, with the multidirected graph unifying the first two. In this work, we further enrich this landscape by proposing the Multidirected hypergraph, which merges the flexibility of hypergraphs and superhypergraphs to describe higher-order and hierarchical connections. Building on this, we introduce five uncertainty-aware Multidirected frameworks—fuzzy, neutrosophic, plithogenic, rough, and soft multidirected graphs—by (...) embedding classical uncertainty models into the Multidirected setting. We outline their formal definitions, examine key structural properties, and illustrate each with examples, thereby laying groundwork for future advances in uncertain graph analysis and decision-making. (shrink)