Cha, Jinho, Long Hoang Pham, Thi Quynh Trang Vo, Jaeyoung Cho · 5 authors
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examine how adoption intensity alpha is structurally pinned at a boundary solution, invariant to variance and heterogeneity, while profitability and service outcomes are variance-fragile, eroding under volatility and heavy-tailed demand. A sharp threshold in the fixed cost parameter A3 triggers discontinuous adoption collapse (H1), variance shocks reduce profits monotonically but not adoption (H2), and additional results on readiness heterogeneity (H3), profit-service co-benefits (H4), and distributional robustness (H5) confirm the duality between stable adoption and fragile payoffs. External validity checks further establish convergence of sample average approximation at the canonical O(1/sqrt(N)) rate (H6). Empirical validation using S&P 500 returns and the MovieLens100K dataset corroborates the theoretical structure: bounded and heavy-tailed distributions fit better than Gaussian models, and profits diverge across volatility regimes even as adoption remains stable. Taken together, the results demonstrate that adoption choices are robust to uncertainty, but their financial consequences are highly fragile. For operations and finance, this duality underscores the need for risk-adjusted performance evaluation, option-theoretic modeling, and distributional stress testing in strategic investment and supply chain design.
In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.
This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally models such problems and propose two distinct problem reductions to standard MDPs. One problem reduction provides a novel, counter-intuitive perspective, and combining the two problem reductions enables significant improvements in dynamic programming algorithms such as value iteration. In the case of the LRA which size is parameterized by $κ$ (in Ethereum's case $κ$= 325), we reduce the computational complexity from $O(2^κκ^{2^{κ+2}})$ to $O(κ^4)$ (per iteration). This solution also provide the usual benefits from Dynamic Programming solutions: exponentially fast convergence toward the optimal solution is guaranteed. The dual perspective also simplifies policy extraction, making the approach well-suited for resource-constrained agents who can operate with very limited memory and computation once the problem has been solved. Furthermore, we generalize those results to a broader class of MDPs, enhancing their applicability. The framework is validated through two case studies: a fictional card game and the LRA on the Ethereum random seed consensus protocol. These applications demonstrate the framework's ability to solve large-scale problems effectively while offering actionable insights into optimal strategies. This work advances the study of MDPs and contributes to understanding security vulnerabilities in blockchain systems.
This paper presents a privacy-preserving intrusion detection architecture tailored for smart home environments, addressing the dual challenge of maintaining data confidentiality while enabling accurate anomaly detection. The proposed system replaces conventional raw data analysis with a proof-driven mechanism leveraging Zero-Knowledge Proofs (ZKPs). Behavioral patterns from smart devices such as motion sensors, door contacts, and environmental monitors are abstracted into cryptographic representations, which are then processed by a zk-SNARK-compatible machine learning model. Inference results are accompanied by cryptographic proofs verifying the correctness of each decision without disclosing the input data. A private blockchain layer, implemented using Ethereum smart contracts, records event hashes, proof metadata, and decision outcomes to ensure tamper-evident logging and automated response handling. Experimental simulations on synthetic home automation datasets demonstrate that the architecture achieves over 92% anomaly detection accuracy while ensuring zero exposure of raw sensor streams. The system also exhibits low-latency proof generation (~400 ms) and end-to-end response time under 1.2 seconds, confirming its suitability for real-time smart home applications.
Decentralized autonomous organizations (DAOs) represent an innovation in the design of organizations by creating blockchain-based human-machine systems that are governed based on the collective decisions of their participants. Although this new form of organizing promises to sustain participation and foster decentralized governance, many existing DAOs have failed to achieve the intended degrees of decentralization. This study aims to understand how DAOs can fulfill their potential for decentralization by empowering individuals to participate in governance. Using an abductive approach guided by the empowerment theory, this research identifies three key practices underpinning empowerment in DAOs: promoting autonomy, ensuring transparency, and fostering communication. A configurational approach is used to identify complementarities among these practices that lead to three distinct governance archetypes associated with varying degrees of decentralization. Based on fuzzy-set qualitative comparative analysis (fsQCA) of 30 DAO cases, we introduce “deliberative democracy” as a DAO governance archetype that allows for increasingly decentralized governance. Our findings demonstrate that, although a high degree of autonomy is needed to sustain decentralization, there needs to be sufficient communication among autonomous actors to facilitate the collective management of DAOs. These findings advance the understanding of decentralization in information systems research and highlight the governance mechanisms that foster decentralization in blockchain-based systems.
We present BATTLE for Bitcoin, a DoS-resilient dispute layer that secures optimistic bridges between Bitcoin and rollups or sidechains. Our design adapts the BATTLE tournament protocol to Bitcoin's UTXO model using BitVM-style FLEX components and garbled circuits with on-demand L1 security bonds. Disputes are resolved in logarithmic rounds while recycling rewards, keeping the honest asserter's minimum initial capital constant even under many permissionless challengers. The construction is fully contestable (challengers can supply higher-work counter-proofs) and relies only on standard timelocks and pre-signed transaction DAGs, without new opcodes. For $N$ operators, the protocol requires $O(N^2)$ pre-signed transactions, signatures, and message exchanges, yet remains practical at $N\!\gtrsim\!10^3$, enabling high decentralization.
Financial fraud has been growing exponentially in recent years. The rise of cryptocurrencies as an investment asset has simultaneously seen a parallel growth in cryptocurrency scams. To detect possible cryptocurrency fraud, and in particular market manipulation, previous research focused on the detection of changes in the network of trades; however, market manipulators are now trading across multiple cryptocurrency platforms, making their detection more difficult. Hence, it is important to consider the identification of changes across several trading networks or a `network of networks' over time. To this end, in this article, we propose a new change-point detection method in the network structure of tensor-variate data. This new method, labeled TenSeg, first employs a tensor decomposition, and second detects multiple change-points in the second-order (cross-covariance or network) structure of the decomposed data. It allows for change-point detection in the presence of frequent changes of possibly small magnitudes and is computationally fast. We apply our method to several simulated datasets and to a cryptocurrency dataset, which consists of network tensor-variate data from the Ethereum blockchain. We demonstrate that our approach substantially outperforms other state-of-the-art change-point techniques, and the detected change-points in the Ethereum data set coincide with changes across several trading networks or a `network of networks' over time. Finally, all the relevant \textsf{R} code implementing the method in the article are available on https://github.com/Anastasiou-Andreas/TenSeg.
The programmable and composable nature of smart contract protocols has enabled the emergence of novel market structures and asset classes that are architecturally frictional to implement in traditional financial paradigms. This fluidity has produced an understudied class of market dynamics, particularly in coupled markets where one market serves as an oracle for the other. In such market structures, purchases or liquidations through the intermediate asset create coupled price action between the intermediate and final assets; leading to basket inflation or deflation when denominated in the riskless asset. This paper examines the microstructure of this inflationary dynamic given two constant function market makers (CFMMs) as the intermediate market structures; attempting to quantify their contributions to the former relative to familiar pool metrics such as price drift, trade size, and market depth. Further, a concrete case study is developed, where both markets are constant product markets. The intention is to shed light on the market design process within such coupled environments.
Transaction Fee Mechanisms (TFMs) study auction design in the Blockchain context, and emphasize robustness against miner and user collusion, moreso than traditional auction theory. \cite{chung2023foundations} introduce the notion of a mechanism being $c$-Side-Contract-Proof ($c$-SCP), i.e., robust to a collusion of the miner and $c$ users. Later work \cite{chung2024collusion,welfareIncreasingCollusion} shows a gap between the $1$-SCP and $2$-SCP classes. We show that the class of $2$-SCP mechanisms equals that of any $c$-SCP with $c\geq 2$, under a relatively minor assumption of consistent tie-breaking. In essence, this implies that any mechanism vulnerable to collusion, is also vulnerable to a small collusion.
Abstract Stronger health systems are better equipped to withstand shocks and continue providing quality services as response measures are implemented. We conducted a systematic review to synthesize the understanding of the concept of health system resilience from various stakeholders in Africa, focusing on definitions and attributes of a resilient health system. We conducted a search for peer-reviewed articles and grey literature, filtered for Africa, from 1980 to 2023, using the SPIDER framework. We searched four databases: PubMed, the Bielefeld Academic Search Engine, the Cumulative Index to Nursing and Allied Health Literature, and Scopus, and reviewed the websites of the World Health Organization, Africa CDC, and Ministries of Health of African countries. Articles were selected based on set inclusion and exclusion criteria. Qualitative articles were appraised using the Critical Appraisal Skills Programme, and mixed-methods articles using the Mixed Methods Appraisal Tool. We mapped the distribution of included articles by country studied; categorized the articles based on reported shock, health system building block described; and identified the definition of health system resilience, and its attributes in each article. The search yielded 4,306 relevant records, fifty-five of which were included in the study. Studies were found from 48 of the 54 African countries. Up to 75% of the articles focused on COVID-19; others were on Ebola Virus Disease, cholera, and meningitis. Service delivery and health workforce were the most frequently studied health system building blocks. In defining or describing health system resilience, the adaptive capacity (39, 65%) was most frequently mentioned, followed by absorptive capacity (17, 28%), preparedness (3, 5%), and recovery (1, 2%). Identified attributes of a resilient health system were: community engagement and involvement; leadership and governance; collaborations and partnerships; human resources for health; health education and promotion; health information systems; health service delivery; decentralization and local governance; health infrastructure and logistics; preparedness; learning and adaptation; and innovation and financing. Our review reports four core capacities that define a resilient health system: preparedness, absorptive capacity, adaptive capacity, and recovery. Essential attributes encompass community engagement, health education and promotion, leadership and governance, surveillance and laboratory capacity, innovation, service delivery, and adaptability.
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering.
With the growth of digital financial systems, robust security and privacy have become a concern for financial institutions. Even though traditional machine learning models have shown to be effective in fraud detections, they often compromise user data by requiring centralized access to sensitive information. In IoT-enabled financial endpoints such as ATMs and POS Systems that regularly produce sensitive data that is sent over the network. Federated Learning (FL) offers a privacy-preserving, decentralized model training across institutions without sharing raw data. FL enables cross-silo collaboration among banks while also using cross-device learning on IoT endpoints. This survey explores the role of FL in enhancing financial security and introduces a novel classification of its applications based on regulatory and compliance exposure levels— ranging from low-exposure tasks such as collaborative portfolio optimization [16] to high-exposure tasks like real-time fraud detection [7], [8]. Unlike prior surveys, this work reviews FL’s practical use within financial systems, discussing its regulatory compliance and recent successes in fraud prevention and blockchainintegrated frameworks. However, FL’s deployment in finance is not without challenges. Data heterogeneity, adversarial attacks, and regulatory compliance make implementation far from easy. This survey reviews current defense mechanisms and discusses future directions, including blockchain integration, differential privacy, secure multi-party computation, and quantum-secure frameworks. Ultimately, this work aims to be a resource for researchers exploring FL’s potential to advance secure, privacycompliant financial systems.
Decentralized coordination and digital contracting are becoming critical in complex industrial ecosystems, yet existing approaches often rely on ad hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study develops a mechanism design framework for smart contract-based resource allocation that explicitly embeds efficiency and fairness in decentralized coordination. We establish the existence and uniqueness of contract equilibria, extending classical results in mechanism design, and introduce a decentralized price adjustment algorithm with provable convergence guarantees that can be implemented in real time. To evaluate performance, we combine extensive synthetic benchmarks with a proof-of-concept real-world dataset (MovieLens). The synthetic tests probe robustness under fee volatility, participation shocks, and dynamic demand, while the MovieLens case study illustrates how the mechanism can balance efficiency and fairness in realistic allocation environments. Results demonstrate that the proposed mechanism achieves substantial improvements in both efficiency and equity while remaining resilient to abrupt perturbations, confirming its stability beyond steady state analysis. The findings highlight broad managerial and policy relevance for supply chains, logistics, energy markets, healthcare resource allocation, and public infrastructure, where transparent and auditable coordination is increasingly critical. By combining theoretical rigor with empirical validation, the study shows how digital contracts can serve not only as technical artifacts but also as institutional instruments for transparency, accountability, and resilience in high-stakes resource allocation.
Effective supply chain management under high-variance demand requires models that jointly address demand uncertainty and digital contracting adoption. Existing research often simplifies demand variability or treats adoption as an exogenous decision, limiting relevance in e-commerce and humanitarian logistics. This study develops an optimization framework combining dynamic Negative Binomial (NB) demand modeling with endogenous smart contract adoption. The NB process incorporates autoregressive dynamics in success probability to capture overdispersion and temporal correlation. Simulation experiments using four real-world datasets, including Delhivery Logistics and the SCMS Global Health Delivery system, apply maximum likelihood estimation and grid search to optimize adoption intensity and order quantity. Across all datasets, the NB specification outperforms Poisson and Gaussian benchmarks, with overdispersion indices exceeding 1.5. Forecasting comparisons show that while ARIMA and Exponential Smoothing achieve similar point accuracy, the NB model provides superior stability under high variance. Scenario analysis reveals that when dispersion exceeds a critical threshold (r > 6), increasing smart contract adoption above 70% significantly enhances profitability and service levels. This framework offers actionable guidance for balancing inventory costs, service levels, and implementation expenses, highlighting the importance of aligning digital adoption strategies with empirically observed demand volatility.
Organisation scholars frequently refer to Franz Kafka to shed light on various dark sides of organisations, in particular the dysfunctional aspects of bureaucratic organisations commonly associated with the word “Kafkaesque.” In this essay, we take the hundredth anniversary of Franz Kafka’s death as an occasion to revisit his work and life as a source of imagination for organisation scholars. We start by introducing Kafka’s “office writings,” produced during his daytime job as an accident insurance lawyer for industrial workers and, so far, largely overlooked by organisation scholars, as well as facets of his biography. We then propose that a more comprehensive analysis of Kafka’s oeuvre offers organisation scholarship a unique perspective on two pressing, contemporary challenges of organising. First, Kafka’s work and life illuminate the inherent contingency and futility of organising in the face of uncertainty, while also highlighting its necessity. Second, his writing provides a nuanced understanding of enigmatic, inescapable organisations that resonate with today’s digital and algorithmic forms of organising. Through his work, we find examples of individual and organisational acts of resilience and resistance, including a leveraging of bureaucratic institutions to fight inequality and injustice. These themes directly speak to current debates on the role of organisation in times of crisis and disruption, marked by the erosion of democratic institutions, the rise of digital and algorithmic organizing, and ecological collapse.
Decentralized Autonomous Organizations (DAOs) aim to enable participatory governance, but in practice face challenges of voter apathy, concentration of voting power, and misaligned delegation. Existing delegation mechanisms often reinforce visibility biases, where a small set of highly ranked delegates accumulate disproportionate influence regardless of their alignment with the broader community. In this paper, we conduct an empirical study of delegation in DAO governance off-chain discussions from 14 DAO forums. We develop a methodology to link forum participants to on-chain addresses, extract governance interests using large language models, and compare these interests against delegates' historical behavior. Our analysis reveals that delegations are frequently misaligned with token holders' expressed priorities and that current ranking-based interfaces exacerbate power concentration. We argue that incorporating interest alignment into delegation processes could mitigate these imbalances and improve the representativeness of DAO decision-making. To support future research, we will release our dataset and code in a public repository.
This article analyzes the implementation of participatory budgeting as a democratic governance mechanism in the Decentralized Autonomous Municipal Government of Santa Ana de Cotacachi, Ecuador, during 2015-2020. Through a mixed methodological approach combining systematic documentary analysis of 147 official documents, 42 semi-structured interviews, participant observation in 12 deliberative sessions, and statistical analysis of management indicators, the institutionalization of the Cantonal Participation System and its multidimensional impact on local public management is examined. Results show that normative formalization through the 2016 Ordinance consolidated a two-decade participatory process, achieving mobilization of 8,347 unique citizens (21.3% of adult population) and execution of 312 priority projects with investment exceeding USD 8.4 million. Econometric analysis reveals significant correlation (r=0.76, p<0.001) between national transfers and participatory resources, evidencing structural fiscal vulnerability. Three critical success factors are identified: multilevel institutional articulation, accumulated social capital (186 active organizations), and contextualized methodological adaptation. However, structural challenges persist related to fiscal dependency (68% of revenue from transfers), power asymmetries in participation (73% male overrepresentation in leadership), and tensions between economic development and environmental sustainability. Comparative analysis with 15 Latin American experiences positions Cotacachi as a paradigmatic case of institutional innovation, albeit with scalability limitations. The study concludes that the model represents a significant contribution to democratization of subnational public management, requiring structural reforms in fiscal architecture and inclusion mechanisms to ensure sustainability.
Decentralized applications (dApps) in Decentralized Finance (DeFi) face a fundamental tension between regulatory compliance requirements like Know Your Customer (KYC) and maintaining decentralization and privacy. Existing permissioned DeFi solutions often fail to adequately protect private attributes of dApp users and introduce implicit trust assumptions, undermining the blockchain's decentralization. Addressing these limitations, this paper presents a novel synthesis of Self-Sovereign Identity (SSI), Zero-Knowledge Proofs (ZKPs), and Attribute-Based Access Control to enable privacy-preserving on-chain permissioning based on decentralized policy decisions. We provide a comprehensive framework for permissioned dApps that aligns decentralized trust, privacy, and transparency, harmonizing blockchain principles with regulatory compliance. Our framework supports multiple proof types (equality, range, membership, and time-dependent) with efficient proof generation through a commit-and-prove scheme that moves credential authenticity verification outside the ZKP circuit. Experimental evaluation of our KYC-compliant DeFi implementation shows considerable performance improvement for different proof types compared to baseline approaches. We advance the state-of-the-art through a holistic approach, flexible proof mechanisms addressing diverse real-world requirements, and optimized proof generation enabling practical deployment.
Xingxing Chen, Xiaohong Zhang, Shaojiang Zhong, Shuling Liu
Vehicular Ad Hoc Networks (VANETs) are now a pivotal component of Intelligent Transportation Systems. However, ensuring secure vehicle identity authentication and protecting user privacy remain two challenging issues in VANETs. Addressing these challenges, this paper seamlessly integrates blockchain technology with the InterPlanetary File System to realize a fully decentralized storage solution for identity verification information. Simultaneously, it employs zk-SNARK and elliptic curve cryptography to allow vehicle users to anonymously complete identity verification. Additionally, the lightweight identity authentication proof obtained after successful verification maintains credibility while reducing the computational and communication costs for both roadside units and vehicles. The security and performance analysis of the system show that the proposed scheme has significant advantages in both communication and computation compared with similar research, while also offering superior security and a broader range of functional attributes compared to existing competitive approaches.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) must reconcile an inherent tension. The Ethereum Virtual Machine (EVM) was designed for transparent step-by-step execution with dynamic control flow. Proving such execution in zero-knowledge, however, requires transforming it into algebraic circuit representations that encode computation as mathematical constraints. Existing surveys address zkEVMs at the level of implementations, cryptographic primitives, or Layer 2 deployment, leaving the constraint-system design that governs their cost largely unexamined. This survey provides the first constraint-level analysis of how five production zkEVM systems and three universal Zero-Knowledge Virtual Machines (zkVMs) resolve this tension through constraint engineering. We show that the degree of EVM compatibility, captured by the Type 1-4 spectrum, is the defining architectural decision that shapes all subsequent technical choices. We classify the design space along four architectural dimensions, namely arithmetization frameworks, dispatch strategies, semantic rewrites, and recursion approaches. Examining the mechanisms within each dimension, we identify the technical factors and trade-offs that drive each choice. The analysis reveals that all five surveyed production zkEVMs adopt PLONKish arithmetization. The zkVMs instead rely on the Algebraic Intermediate Representation (AIR), which suits uniform state machines. A single trade-off between EVM compatibility and constraint cost underlies these choices. The most Ethereum-equivalent systems accept higher constraint counts to preserve full bytecode fidelity, while systems that relax that fidelity attain substantially lower constraint counts. We close with the critical open problems and future research directions that this constraint-level view brings into focus.
Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predominantly simulate anthropomorphic roles that inadvertently introduce emotional biases and rely on peripheral information, while being constrained by the necessity for continuous inference during deployment. In this paper, we pioneer the harmonization of strategic depth in agents with the mechanical rationality essential for quantitative trading. Consequently, we present TiMi (Trade in Minutes), a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment. TiMi leverages specialized LLM capabilities of semantic analysis, code programming, and mathematical reasoning within a comprehensive policy-optimization-deployment chain. Specifically, we propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection. Extensive evaluations across 200+ trading pairs in stock and cryptocurrency markets empirically validate the efficacy of TiMi in stable profitability, action efficiency, and risk control under volatile market dynamics.
Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging blockchain and artificial intelligence technologies, Web 3.0 offers unprecedented opportunities for users to create, own, and monetize their content, thereby enabling User-Generated Content (UGC) to an entirely new level. However, some self-interested users may exploit the limitations of content curation mechanisms and generate low-quality content with less effort, obtaining platform rewards under information asymmetry. Such behavior can undermine Web 3.0 performance. To this end, we propose \textit{LMM-Incentive}, a novel Large Multimodal Model (LMM)-based incentive mechanism for UGC in Web 3.0. Specifically, we propose an LMM-based contract-theoretic model to motivate users to generate high-quality UGC, thereby mitigating the adverse selection problem from information asymmetry. To alleviate potential moral hazards after contract selection, we leverage LMM agents to evaluate UGC quality, which is the primary component of the contract, utilizing prompt engineering techniques to improve the evaluation performance of LMM agents. Recognizing that traditional contract design methods cannot effectively adapt to the dynamic environment of Web 3.0, we develop an improved Mixture of Experts (MoE)-based Proximal Policy Optimization (PPO) algorithm for optimal contract design. Simulation results demonstrate the superiority of the proposed MoE-based PPO algorithm over representative benchmarks in the context of contract design. Finally, we deploy the designed contract within an Ethereum smart contract framework, further validating the effectiveness of the proposed scheme.
Blockchain technology has revolutionized decentralized systems, with applications that span finance, healthcare, and supply chain management. However, scalability challenges, particularly limited transaction throughput and high computational overhead, hinder its broader adoption. This work presents and validates a machine learning-driven consensus mechanism to enhance scalability while preserving security and decentralization. The proposed approach dynamically adjusts the mining difficulty and resource allocation in real time by employing Bayesian-optimized ensemble models (XGBoost and Random Forest) to predict the conditions of the blockchain network. Experimental evaluations show improved throughput, lower latency, and more equitable miner participation compared to traditional Proof of Work (PoW). The findings suggest that data-driven consensus can mitigate long-standing performance bottlenecks, enabling next-generation decentralized systems for industrial-scale deployment.