Modern societies are simultaneously confronting demographic collapse and intensifying conflicts over gender equality, care, and labor. These tensions are often treated as policy trade-offs, yet they stem from a deeper ontological limitation: the reduction of human existence to functional structure (EāS). This paper introduces Universal Phase Crystallization Theory (UPCT) as a foundational shift toward defining existence as generative resonance (E=Ī¦R). By reframing freedom, equality, and ethics as dynamic conditions of generative participation, the apparent conflict between demographic policy and gender equality dissolves. What emerges is not a compromise, but a civilizational transition from function-centered systems to generation-centered structures. This work provides a unified theoretical framework for rethinking equality, care, and sustainability in the 21st century. Highlights Reveals that conflicts between demographic policy and gender equality originate from a shared S-centric ontology (EāS) Introduces UPCT (E = Ī¦R) as a unified framework for redefining existence, equality, and ethics Reconstructs equality as non-comparability of generative potential, beyond functional parity Demonstrates that demographic decline is a systemic failure of generative continuity (d(Ī¦R)/dt<0) Proposes a civilizational redesign based on generative resonance rather than labor-market optimization Summary & Main Arguments 1. Ontological Diagnosis of Modern CrisisThis paper begins by identifying a foundational contradiction in modern societies: the persistent conflict between demographic sustainability and gender equality. Rather than interpreting this as a policy failure, the paper argues that the root cause lies in an implicit ontological assumptionānamely, that human existence is reducible to structural or functional representation (EāS). Within this framework, individuals are treated as economic actors, legal units, or measurable entities, leading to systemic tensions when biological, relational, and generative dimensions cannot be fully captured. 2. Historical Saturation of Functional EqualityTracing the evolution of equality from formal legal equality to distributive and identity-based equality, the paper demonstrates that modern equality has progressively intensified its reliance on functional comparability. While these developments have been historically emancipatory, they culminate in a paradox: the more equality is pursued through structural comparison, the more differences (biological, genetic, relational) become sources of conflict. This results in a zero-sum system that ultimately fragments social cohesion. 3. UPCT as Ontological ReframingTo resolve this impasse, the paper introduces Universal Phase Crystallization Theory (UPCT), which redefines existence as generative resonance (E=Ī¦R). Here, Ī¦ represents generative potential, and R relational resonance. Structure (S) is not the essence of existence but a temporary crystallization within a dynamic generative cycle (Ī¦āRāSāĪ¦ā²). This reframing shifts the analytical focus from static structure to dynamic process. 4. Redefinition of Core ValuesWithin the UPCT framework, the foundational concepts of modern philosophy are reinterpreted. Freedom becomes participation in generative processes rather than choice within structures. Equality is redefined as the non-comparability of generative potential rather than functional uniformity. Ethics is formalized as the sustainability condition (d(Ī¦R)/dtā„0), transforming it from normative prescription to systemic viability condition. 5. Policy Implications and Civilizational TransitionFinally, the paper applies this framework to the conflict between demographic policy and gender equality. It demonstrates that the conflict dissolves when both are reinterpreted through generative conditions rather than structural distribution. Policy interventionsāsuch as reducing structural burdens, restoring relational infrastructure, elevating care, and redesigning timeāare reframed as foundational requirements for sustaining generative resonance. This marks not a policy adjustment, but a civilizational transition. Contributions 1. Ontological Regrounding of Social TheoryThis paper provides a fundamental ontological critique of modern social theory by identifying the implicit equation EāS as the root of contemporary contradictions. By introducing E=Ī¦R, it establishes a new foundation that integrates process, relation, and generation into the definition of existence. 2. Unified Framework Across DisciplinesThe study bridges philosophy, economics, gender studies, and systems theory by offering a single conceptual framework capable of explaining demographic decline, care crises, and equality conflicts. This integration moves beyond fragmented disciplinary approaches. 3. Redefinition of Equality and EthicsA major theoretical contribution is the redefinition of equality as non-comparability and ethics as a dynamic sustainability condition. This resolves long-standing tensions between fairness, difference, and viability, offering a new paradigm for justice theory. 4. Reinterpretation of Feminist and Critical ThoughtRather than rejecting feminist, Marxist, or care-based critiques, the paper demonstrates how these traditions can be sublated within UPCT. It preserves their insights while extending them beyond structural limitations, avoiding both reductionism and opposition. 5. Civilizational Design FrameworkThe paper advances a practical theoretical model for societal redesign. By translating UPCT into policy principlesāstructural reduction, relational recovery, generative elevation, and temporal redesignāit provides a concrete pathway for transitioning toward a generative society. Authorās Related Works UPCT Foundational Theoretical Works Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT): A Generative Relational Ontology of Existence, Stability, and Emergence.https://doi.org/10.5281/zenodo.19065461 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT): A Unified Generative Theory of Time, Life, and Civilization.https://doi.org/10.5281/zenodo.18653237 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT) Phase I: A Unified Resolution of Quantum Paradoxes via Temporal Sampling.https://doi.org/10.5281/zenodo.18230537 Ohumi, K. (2026). Universal Phase Crystallization Theory (UPCT) Phase II: A Phase Transition Law for Generative Systems under Measurement Optimization.https://doi.org/10.5281/zenodo.18408708 Ohumi, K. (2026). Universal Phase-Crystallization Theory (UPCT) I: Generative Time and Relational Space.https://doi.org/10.5281/zenodo.18979001 Ohumi, K. (2026). From Machine Civilization to Generative Civilization: Universal Phase-Crystallization Theory and the Generative Structure of Reality.https://doi.org/10.5281/zenodo.18935934 Ohumi, K. (2026). UPCT Existential Core: A Generative Ontology for Post-Functional Civilization. https://doi.org/10.5281/zenodo.19146516 Ohumi, K. (2026). A Generative-Relational Ontology of Sustained Existence: UPCT. https://doi.org/10.5281/zenodo.19469785 UPCT Ontology and Civilizational Philosophy Ohumi, K. (2026). Existence as Generativity: Desire, Structure, and the Dynamics of Civilizational Transition in Universal Phase Crystallization Theory. https://doi.org/10.5281/zenodo.19198157 Ohumi, K. (2026). From Having to Being: Toward a Generativity-Centered Ontology in the Age of Artificial Intelligence.https://doi.org/10.5281/zenodo.18829129 Ohumi, K. (2026). The Declaration of Life-OS: An Ontological Turn Toward a Generative Civilizational Spiral.https://doi.org/10.5281/zenodo.18645582 Ohumi, K. (2026). From Proof to Resonance: A Ī¦-Ontology of Existence, Labor, Education, and Economic Life.https://doi.org/10.5281/zenodo.18515955 Ohumi, K. (2026). Returning to the Source of Philosophy: Affirmation of Life as the Life-OS and a Radical Point of Departure.https://doi.org/10.5281/zenodo.18529485 Ohumi, K. (2026). Dialectics as a Relational Logic of Life: From Linear Ascent to Spiral Circulation.https://doi.org/10.5281/zenodo.18522371 Ohumi, K. (2026). Does Color Exist? Overcoming the Ontological-Epistemological Confusion Through Generative Phase Transition: An Application of Universal Phase Crystallization Theory (UPCT). https://doi.org/10.5281/zenodo.19105125 Ohumi, K. (2026). From Color to Sound: Human Cognitive Limits Between Ontology and epistemology and the Generative Resolution of UPCT. https://doi.org/10.5281/zenodo.19110346 Ohumi, K. (2026). Toward a Generative Theory of Human Motivation: Participation, Existence, and the Fundamental Drive. https://doi.org/10.5281/zenodo.19286911 Ohumi, K. (2026). What is Desire? The Transition from the "Machine OS" to the "Life OS" in the History of Human Thought. https://doi.org/10.5281/zenodo.19327281 Ohumi, K. (2026). The Ontology of Resonance Beyond Generative Supremacy: The First Principle of "Existence = Generation = Resonance" and the Mandalic Hierarchy of the Life OS. https://doi.org/10.5281/zenodo.19334259 Ohumi, K. (2026). Life as Generative Resonance: An Ontological Essay on Happiness, Wealth, and the Recovery of Human Generativity. https://doi.org/10.5281/zenodo.19394468 Ohumi, K. (2026). Co-Generative Intelligence: A Relational Framework for HumanāAI Collaboration Beyond Optimization. https://doi.org/10.5281/zenodo.19659573 Ohumi, K. (2026). The Equation of Knowledge Dynamics: A GenerativeāRelationalāStructural Field Theory of Intelligence and Civilization. https://doi.org/10.5281/zenodo.19707187 Ohumi, K. (2026). The Meta-principle of Generation and the End of Ideology: Dismantling Structural Illusions and Redefining the Ontology of Value via the Equation E = Ī¦R. https://doi.org/10.5281/zenodo.19724468 UPCT Science and Physics Foundations Ohumi, K. (2025). A Sampling-Theoretic Reinterpretation of Quantum Uncertainty and Wave Function Collapse.https://doi.org/10.5281/zenodo.1800
Insurance governance fundamentally concerns the legitimate authority to price risk, allocate capital, and absorb correlated losses. Traditional models centralize this authority in credentialed institutions, while existing decentralized finance (DeFi) protocols either replicate centralized control or treat critical actuarial parameters as exogenous inputs. This paper resolves the decentralized insurance governance trilemma by proposing the first complete theoretical framework built on a three-tier architecture of capital-backed parameter markets, extending and completing the Risk Coin framework (Yu, 2025). The architecture transforms actuarial assumptions into endogenously discovered economic variables. At the Risk-Pool Tier, insurers commit capital to bid on parameters of an MBBEFD exposure curve. Policyholders signal their private risk assessments by declaring a coverage limit and a total premium payment; from these, an implied retention multiplier is derived after market pricing. At the Catastrophe-Pool Tier, reinsurers likewise commit capital to bid on parameters for pricing optional excess-of-loss reinsurance. The core governance innovation is a recursive design: a Catastrophe Pool operates identically to a Risk Pool, with Risk Pools as its policyholders, thereby unifying the governance logic of primary and reinsurance markets under the same capital-backed mechanism. The ecosystem is anchored by the Risk-Coin-Fund Tier, which centralizes all capitalāincluding the base risk premiums (corresponding to actuarial costs), policyholdersā voluntary supra-actuarial contributions, and professional investment reinsurance capitalāand serves as the ultimate residual risk bearer. A core innovation is the consistent application of Risk Coin as a dual-purpose claim on the collective capital pool. RC tokens are issued to participants based on two principles: (1) as compensation for voluntary capital investment (the portion of declared premiums exceeding actuarial cost), and (2) as payment for risk-bearing, with the latter quantified by the market-determined exposure curve šŗ(ā ). This creates a unified incentive system where capital commitment confers pricing authority, and better risk management preserves RC value. We formalize the mechanism and prove its core properties: incentive-compatible parameter bidding, asymptotic information aggregation, dynamic stability of parameter markets, systemic resilience via an exponential solvency guarantee for diversifiable risks, and regulatory non-intrusiveness. A pivotal theoretical extension is the introduction of a protocol-native capital coverage ratio (Ļ), which emerges from the recursive markets. This ratio is governed by a target (štarget) and serves as a real-time, transparent solvency signal. It demonstrates how decentralized systems can achieve endogenous stability while providing a direct interface for financial oversight, effectively bridging cryptographic economics with prudential regulatory frameworks. The framework demonstrates that legitimate pricing authority and financial stability can emerge organically from cryptographic economics where tokens serve not as speculative instruments but as verifiable, economically-founded claims on underwriting capacity and capital pool ownership. By solving the governance trilemma through recursive capital-backed parameter markets, a unified RC-based capital accounting system, and a native regulatory interface, this work provides more than a new insurance mechanismāit offers a blueprint for stable, transparent, incentive-aligned, and regulatorily-compatible financial ecosystems that reimagine the foundations of risk-sharing and financial governance.
This paper presents a model in which risk-averse individuals can purchase insurance via traditional indemnity contracts or Decentralized Finance (DeFi) smart contract-based instruments. The model incorporates key features of DeFi insurance, including parametric payouts, basis risk arising from imperfect loss verification and pooled collateralization involving the risk of liquidity shortfalls. We characterize optimal insurance choices as a function of pricing, payout correlation and risk preferences. Numerical results show that DeFi insurance can complement or replace traditional coverage, improving welfare when basis and default risks are moderate or pricing advantages are substantial. The analysis reveals how DeFi-specific frictions shape insurance demand and provides insight into how DeFi instruments may shift market structure and expand the set of attainable risk transfer outcomes.
Decentralized Finance (DeFi) lending protocols currently rely on fixed collateralization ratios, leading to inefficiencies such as over-collateralization, frequent liquidations, and suboptimal capital utilization. This paper proposes a novel framework integrating machine learning (ML) with DeFi lending protocols to dynamically adjust collateral requirements in realtime based on borrower behavior, market volatility, and on-chain data. By analyzing historical loan performance, social sentiment, and macroeconomic indicators, the ML model optimizes collateral ratios to minimize liquidations while maintaining protocol security. We simulate the model using data from major DeFi platforms (e.g., Aave, Compound) and demonstrate a 30-50
Aim/Purpose To explore the potential of Federated Machine Learning (FML) in developing predictive models while ensuring data privacy and security. Background The rise of data-driven technologies has led to an increased focus on privacy concerns associated with centralized data storage. FML offers a decentralized approach, allowing organizations to collaboratively train models without sharing sensitive data (McMahan et al., 2017). Methodology This study employs a FML framework, utilizing local model training on decentralized datasets, followed by aggregation of model updates to create a global model. Privacy-preserving techniques, such as differential privacy, are also implemented (Dwork & Roth, 2014). Contribution This research contributes to the field of machine learning by demonstrating the efficacy of FML in predictive modeling, highlighting its potential for secure and privacy-conscious applications. Findings The study indicates that FML can effectively enhance model performance while maintaining the privacy of individual data sources. Recommendations for Practitioners Practitioners are encouraged to adopt FML techniques in applications requiring high data security, particularly in sectors such as healthcare and finance. Recommendation for Researchers Future research should explore advanced aggregation methods and evaluate the scalability of FML in diverse settings. Impact on Society The findings of this research have implications for the broader application of machine learning in sensitive areas, promoting data privacy while harnessing the power of collaborative intelligence. Future Research Further investigations should focus on the robustness of FML against adversarial attacks and its applicability in real-world scenarios.
The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the marketās unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the modelās effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.
Non-fungible tokens (NFTs) are digital assets that represent ownership of a particular item or can represent in-game items or virtual real estate. They are exclusive and limited in quantity, and their ability to be modified and controlled is what makes digital assets so valuable. The objective of the initiative is to develop interactive and immersive gaming experiences by utilizing the unique capabilities of NFTs. This concept is made possible through the use of smart contracts, which decentralize the ownership of NFTs and increase their desirability. The endeavor entails the creation of two online games employing NFTs. āObstacle Assaultā is a side-scrolling game in which players guide a character through obstacles and adversaries to reach the level&s;s conclusion. The game will use NFTs to depict weapons, armor, and power-ups that can be purchased and used to improve the player&s;s character. āTurtle Sidestepā is a puzzle game in which players must guide a turtle through a succession of obstacles in order to reach the level&s;s conclusion. NFTs will be used to depict virtual real estate in this game, allowing players to purchase and own specific locations within the game world.
Abstract This paper studies the optimal stopping problem under the largeāpopulation framework. In particular, two classes of optimal stopping problems are formulated by taking into account the relative performance criteria . It is remarkable that the relative performance criteria, also understood by the Joneses preference , habit formation utility , or relative wealth concern in economics and finance, play an important role in explaining various decision behaviors such as price bubbles. By introducing such criteria in largeāpopulation setting, a given agent can compare his individual stopping rule with the average behaviors of its cohort. The associated meanāfield games are formulated in order to derive the decentralized stopping rules. The related consistency conditions are characterized via some coupled equation system and the āNash equilibrium properties are also verified. In addition, some inverse meanāfield optimal stopping problem is also introduced and discussed.
When entering into a tontine, the value of the tontine for the participant highly depends on its composition (e.g. the age of the participants, the amount invested by each of them already invested in the tontine). However, participants subscribe to the scheme without any knowledge of either the composition of the tontine, or, in some cases, its exact payout scheme. Herein, we quantify the value of this information using certainty equivalents in the expected utility setting and a measure for model risk that allows us to obtain bounds on the tontine value subject to uncertainty in certain characteristics. We then propose a smart contract that offers full disclosure of information in a tontine. We discuss the practical implementation of such a tontine and present some new risks that could arise.
We review the recent developments in dynamic inventory models with financial flow considerations. The focus is on the literature that introduces cash flow dynamics into the classic inventory models that do not explicitly consider the interactions between physical (or material) and financial flows. These augmented models serve two important purposes. First, they help understand the impact of financial flows on inventory dynamics and decisions. Second, with the connection to the classic inventory models, one can leverage the extant results to derive the optimal control policy or to evaluate/optimize the performance of any given type of policy and reveal insights. We summarize models for both single-stage and multi-stage inventory systems, and discuss the implications and applications to decentralized systems within a broader topic of supply chain finance.
Lili Matic, Natalie Packham, Wolfgang Karl HƤrdle
The cryptocurrency market is volatile, non-stationary and non-continuous. Together with liquid derivatives markets, this poses a unique opportunity to study risk management, especially the hedging of options, in a turbulent market. We study the hedge behaviour and effectiveness for the class of affine jump diffusion models and infinite activity Levy processes. First, market data is calibrated to stochastic volatility inspired (SVI)-implied volatility surfaces to price options. To cover a wide range of market dynamics, we generate Monte Carlo price paths using an SVCJ model (stochastic volatility with correlated jumps), a close-to-actual-market GARCH-filtered kernel density estimation as well as a historical backtest. In all three settings, options are dynamically hedged with Delta, Delta-Gamma, Delta-Vega and Minimum Variance strategies. Including a wide range of market models allows to understand the trade-off in the hedge performance between complete, but overly parsimonious models, and more complex, but incomplete models. The calibration results reveal a strong indication for stochastic volatility, low jump frequency and evidence of infinite activity. Short-dated options are less sensitive to volatility or Gamma hedges. For longer-dated options, tail risk is consistently reduced by multiple-instrument hedges, in particular by employing complete market models with stochastic volatility.
In this dissertation we study a variety of continuous-time Markov chains (CTMCs) and present new formulas that can be used to find the stationary distribution and the Laplace transforms of the transition functions. Our first set of results involve a level-dependent Quasi-Birth-Death (QBD) processes. We study the distribution of the state and the associated running maximum level at a fixed time t. We present new expressions for the Laplace transforms of the transition functions containing this information. This work involves making use of a collection of R-matrices often found in matrix analytic literature. We also show how our methods can be used to study the joint distribution of the running minimum level and state of a level-dependent Markov process of M/G/1-type. Our next set of results are based on a homogeneous QBD proccess. These results involve first computing a new class of R and G-matrices that can be used to find the Laplace transforms of the transition functions associated with a homogeneous QBD process with finitely many levels. Our final set of results are based on two CTMCs studied in G\"obel et al. \cite{GobelKeelerKrzesinskiTaylor}, which were created to model the interactions between a small pool of miners and a larger collection of miners within the Bitcoin network. We use the random-product technique, introduced by Buckingham and Fralix \cite{BuckinghamFralix2015}, to find the stationary distribution of this model when all miners are honest and when the small pool of miners implement the Selfish Mining strategy introduced by Eyal and Sirer in \cite{EyalSirer}. We also study the Laplace transforms of the transition functions associated with these CTMCs and other performance measures such as the expected time it takes for a "fork" in the blockchain to be resolved.
Author JiÅĆ MĆ”lek acknowledges the financial support of Czech Science Foundation with grant GAÄR 18-05244S āInnovative Approaches to Credit Risk Managementā and Institutional support IP 100040/1020. Author Tran van Quang is grateful for the financial support of grant GAÄR 18-05244S āInnovative Approaches to Credit Risk Managementā of Czech Science Foundation.
In the era of diminishing power from US dollar and increasing competition among world currencies, Bitcoin, as a completely new concept as a medium of exchange, has received increasing attentions over the world. Nowadays, Bitcoin also becomes an investment vehicle, which carries attractive opportunities but also significant risks for the investment community. In this paper, we have compared the empirical performance of a newly-developed heavy-tailed distribution, the normal reciprocal inverse Gaussian (NRIG), with the most popular heavy-tailed distribution, the Studentās t distribution, under the GARCH framework in fitting the daily Bitcoin exchange rate returns. Our results indicate the heavy-tailed distribution has better performance in capture the daily Bitcoin exchange rate returns dynamics than the standard normal distribution. Our results also show the older fashioned Studentās t distribution still performs better than the new heavy-tailed distribution.