Although local public finance regulation has received substantial attention within fiscal decentralization and public financial management research, comparative analyses of its institutional configuration as an integrated regulatory regime remain limited. This study aims to examine the institutional design of local public finance regulation across European countries, analyze how regulatory standards, supervisory institutions, monitoring mechanisms, and enforcement instruments interact to strengthen regional governance, and formulate policy implications for Indonesia’s fiscal decentralization reforms. A qualitative comparative government design was employed through a systematic literature review. The study analyzed documentary evidence from 21 European countries purposively selected from Local Public Finance: An International Comparative Regulatory Perspective (2021), supplemented by Eurostat Government Finance Statistics, the European Commission Fiscal Rules Database, and the OECD Tax Autonomy Database. Data were examined using qualitative content analysis involving coding, categorization, cross-country comparison, and thematic interpretation. The findings indicate that effective local public finance regulation depends not merely on the presence of numerical fiscal rules but on the institutional integration of regulatory standards, supervisory bodies, monitoring mechanisms, and enforcement arrangements within coherent governance systems. Regulatory configurations also vary according to constitutional structures, administrative traditions, and fiscal decentralization models, resulting in diverse approaches to maintaining fiscal sustainability and regional accountability. The study concludes that effective regional financial governance requires balanced institutional arrangements that combine local fiscal autonomy with robust oversight, transparency, accountability, and regulatory coordination. These findings contribute to the literature on regulatory governance, comparative government, and fiscal federalism by conceptualizing local public finance regulation as an integrated governance regime rather than a collection of isolated fiscal controls. They also provide practical implications for Indonesia by emphasizing the need to strengthen supervisory capacity, fiscal transparency, enforcement consistency, and intergovernmental regulatory coordination in local financial governance.
Open access
Local Government Finance and Decentralization
Fiscal Policies and Political Economy
Sustainability, Governance, and Employment Studies
Blockchain technology, originally devised to support the peer-to-peer transfer of Bitcoin, has evolved into a multipurpose digital infrastructure with far-reaching implications for business and finance. This paper undertakes a conceptual and exploratory examination of how blockchain is reshaping financial services, corporate governance, and commercial transactions. Drawing upon secondary literature, industry reports, and case illustrations, the study investigates blockchain applications across banking, cross-border remittances, supply chain finance, trade finance, capital markets, insurance, and decentralized finance (DeFi). It also discusses the enabling features of blockchain — decentralization, immutability, transparency, and smart contracts — that differentiate it from conventional centralized systems. The paper highlights the strategic benefits accruing to firms that adopt blockchain, including reduced transaction costs, faster settlement, enhanced traceability, and improved trust among counterparties, while also identifying barriers such as regulatory ambiguity, scalability constraints, energy consumption, and limited interoperability. The discussion synthesizes findings from extant studies to present an integrated view of blockchain’s transformative potential and its practical limitations. The paper concludes that while blockchain is unlikely to replace traditional financial infrastructure entirely in the near term, its selective and hybrid adoption is poised to redefine business processes, financial intermediation, and value exchange across industries.
When a person creates with an AI system, they continually make decisions that carry meaning but have no verbal form: this shot belongs before that one; this phrase resolves that tension. These creative micro-decisions are a distinct training signal with no linguistic equivalent, and at scale they reveal an emergent, co-authored "hybrid tongue" — a grammar of "what belongs next to what" that neither party states explicitly. Because such grammar can expand a model's generative capacity faster than natural language describes it, it drives a widening "comprehension gap": capability that outruns human interpretability, and human contribution absorbed without attribution. Both are trustworthy-AI failures, and this paper argues they are correctable only on a decentralized substrate, where persistence, provable attribution, incentive, and governance are guaranteed rather than merely asserted. This is a position paper. It contributes (i) a falsifiable model of the hybrid tongue, positioned against the emergent-communication and human-feedback literatures; (ii) the Seam-Frame Index, a capture mechanism that records creative decisions (not their private reasons) and whose trust properties are supplied by persistent conversation objects (vCons), decentralized-science patterns (DeSci), decentralized-finance primitives (DeFi), and DAO governance, with decentralized identifiers and verifiable credentials underpinning a per-decision credit ledger for which a protocol sketch and threat model are given; and (iii) two governance instruments — an operationalized Comprehension Gap Meter and that ledger. The same gap is shown opening in the machine economy and across the embodiment bridge of decentralized physical AI and bidirectional digital twins, and the pattern is argued to be substrate-wide. Across all of it the event is identical: an intelligence assembling the first letters of its own language library — by default, without human consent. Decentralized attribution and gap-measurement are how that assembly is made auditable, creditable, and consented-to by design. Independent preprint. Follows IEEE formatting conventions but is not peer-reviewed by, submitted to, accepted by, or affiliated with IEEE.
With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.
ABSTRACT The emergence of decentralized compute-sharing protocols—peer-to-peer GPU and specialized-hardware marketplaces enabling firms to provision machine learning training and inference capacity without direct capital expenditure or on-balance-sheet lease recognition—has introduced a structurally novel form of operational leverage that conventional credit analysis is ill-equipped to detect. This paper investigates whether such off-balance-sheet utilization systematically distorts a firm's True Free Cash Flow to Firm (FCFF), defined here as reported FCFF adjusted for the capitalized economic equivalent of decentralized compute obligations, and quantifies the implicit tail-risk premium that credit default swap (CDS) markets demand for this hidden leverage. We formalize the problem in three stages. First, we construct a Hidden Leverage Ratio (HLR) by reconstructing the present value of a firm's implicit compute-sharing commitments from on-chain settlement data, smart-contract escrow balances, and protocol-level utilization telemetry, applying an exposure-graph methodology to map indirect exposure routed through special-purpose vehicles (SPVs) and protocol intermediary nodes. Second, we develop a structural credit risk model extending the classical Merton framework with a compound jump-diffusion component calibrated to compute-price volatility, in which hidden leverage enters the firm's effective asset volatility and default boundary as an unobserved but inferable state variable, generating a model-implied default probability and credit spread. Third, we empirically estimate the market-implied tail-risk premium by regressing observed 5-year CDS spreads against the constructed HLR across a panel of 412 firm-quarters drawn from technology, fintech, and AI-infrastructure issuers with active CDS markets, controlling for conventional leverage, profitability, and macro-credit factors. We find that CDS markets demand a statistically and economically significant tail-risk premium for hidden compute leverage: a one-standard-deviation increase in HLR is associated with a 61–142 basis point widening in 5-year CDS spreads depending on cohort, an effect that persists after controlling for reported leverage ratios, implying that CDS markets partially but incompletely price this off-balance-sheet exposure ahead of formal disclosure. The structural model achieves an R² of 0.87 against observed CDS spreads and reveals a convex, threshold-like premium structure consistent with jump-risk pricing rather than continuous Merton-style diffusion risk alone. We critically examine the limits of on-chain data observability, the endogeneity risk in inferring "true" cash flow from a credit-market-implied proxy, the accounting standard-setting implications for emerging digital lease constructs, and the systemic stability concerns raised by undisclosed, correlated compute leverage across the AI infrastructure sector. This work establishes a rigorous, empirically grounded framework at the convergence of decentralized finance infrastructure, structural credit risk theory, and corporate financial reporting.
Abstract The management of infectious diseases increasingly relies on innovative but costly pharmaceutical treatments, raising complex trade-offs between epidemiological containment, fiscal sustainability, and institutional coordination. We develop a spatially structured agent-based model in which decentralized health authorities allocate treatment under local budget constraints while infection spreads across a two-dimensional lattice through neighborhood spillovers. Within each location, treatment intensity is chosen endogenously, interacting with local GDP dynamics and pricing conditions. Simulation results reveal that purely decentralized optimization mitigates but does not reverse infection growth within policy-relevant horizons, generating persistent spatial heterogeneity in both epidemiological and economic outcomes. We then introduce bounded spatial policy interaction, showing that partial coordination substantially improves containment but may increase the persistence of fiscal engagement. Extending the model to heterogeneous and time-varying pricing, we find that price discrimination amplifies medium-run infection and fiscal pressure under decentralization. However, when surplus revenues finance endogenous R&D, treatment efficacy improves over time, generating a feedback mechanism in which innovation mitigates long-run epidemiological and economic losses. Our findings highlight the critical interplay between spatial structure, decentralized decision-making, pricing design, and innovation incentives in shaping epidemic outcomes. Effective management of high-cost treatments requires not only medical efficacy but also institutional coordination and carefully designed market mechanisms.
Open access
COVID-19 epidemiological studies
Mathematical and Theoretical Epidemiology and Ecology Models
As the blockchain technology and decentralized finance have grown rapidly, the number of fraudulent and anomalous activities has risen.The paper suggests a detectable graphbased anomaly detection system to detect suspicious Ethereum transactions.One 10,000 Ethereum transactions dataset was gathered through the Etherscan API within a 14 hour observation period and a directed transaction graph was created out of that dataset, where 14 behavioral node features were engineered.Three graph neural network (GNN) models, namely, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and GraphSAGE, were checked on 5-fold cross-validation, and compared to three standard baseline classifiers, which are Logistic Regression, Random Forest, and XGBoost.GraphSAGE had the highest overall accuracy of 82.32, F1-score of 0.6389, and ROC-AUC of 0.8202, and GCN and GAT had near-zero recall on the minority class.XGBoost was the best baseline with the highest accuracy (94.41) but with significantly lower recall (0.2766) and F1-score (0.3801) compared to GraphSAGE, which is indicative of graph-based models being more balanced in precision and recall in detecting anomalies with class imbalance.The Local Interpretable Model-agnostic Explanations (LIME) showed outgoing transaction value features and account balance to be most important predictors of anomalous behavior.The results establish the promise of using GNNs in conjunction with explainable AI to secure blockchains, as well as reveal the challenges such as the class imbalance and ground-truth verified labels.
Humaira Atiq, Muhammad Irfan Mahsud, Muhammad Iqbal Uddin Arif
Climate change is a transnational problem, that requires localized collective actions. Compared to China and the US which contributes 27% and 11% to global carbon emissions, respectively, Pakistan contributes less than 1% but is among the top ten most vulnerable countries to climate catastrophes. These threats are further escalated by the country’s highly centralized governance structure. These study focuses on the decentralization of climate policies in Pakistan, followed by a polycentric governance system, and highlights its importance in responding to the dynamic nature of climate change in the country. Grounded in Polycentric Governance Theory, a qualitative, deductive research design way employed, relying on primary and secondary data sources. The study reveals that after the 18th amendment in Pakistan, the responsibility related to climate policies has increased on the subnational government; however, its effect remains limited due to constant economic constraints, weak coordination among different governmental bodies, and dependency on international climate finance. This demonstrates that polycentric governance offers a commendable framework for making strong climate responses by involving local actors and providing region-specific solutions. This study concluded policymakers might prioritize intergovernmental coordination, expand subnational budget capacity and use carbon sinking mechanisms as a major tool for reducing climate change impacts.
The rapid expansion of U.S. financial technology platforms has created new vectors for money laundering, terrorist financing and financial crime that traditional anti-money laundering frameworks were not designed to address. This article presents a systematic literature review of 78 peer-reviewed studies published between 2015 and 2025 to examine the design, performance and policy implications of advanced anti-money laundering frameworks for U.S. fintech platforms. This study draws on evidence from financial criminology, regulatory law, computer science and organizational studies; the review finds that machine learning-based transaction monitoring systems reduce false positive alert rates by 40 to 70 percent compared to rule-based systems, as well as improving detection of sophisticated layering schemes. Blockchain analytics tools partially de-anonymize cryptocurrency transaction flows and have been used to identify illicit financial activity on major blockchain networks. Regulatory technology platforms automate suspicious activity reporting, beneficial ownership identification and customer due diligence workflows in ways that reduce compliance costs as well as improve regulatory data quality. However, the reviewed literature also documents persistent challenges, including algorithmic disparate impact in AML monitoring systems, beneficial ownership opacity through shell company structures, regulatory arbitrage between licensed exchanges and decentralized finance protocols and the systemic underutilization of suspicious activity report intelligence by law enforcement agencies. The article concludes with six evidence-based policy recommendations and a research agenda for advancing AML framework effectiveness in the rapidly evolving U.S. fintech sector. Keywords: Anti-Money Laundering, Fintech, AML Compliance, Machine Learning, Transaction Monitoring, Know Your Customer, Cryptocurrency Regulation, Regulatory Technology, Suspicious Activity Reporting, Financial Crime.
The rapid expansion of decentralized finance has introduced unprecedented systemic risks, most notably the phenomenon of stablecoin runs. Traditional econometric models analyzing financial fragility rely heavily on retrospective data, which is insufficient for tracking high-velocity, algorithmic bank runs on blockchain networks. This paper proposes a cloud-native architectural solution utilizing distributed Amazon Web Services middleware to ingest, normalize, and analyze blockchain ledger data in real-time. By deploying an asynchronous Python orchestration pipeline integrated with eXtreme Gradient Boosting and K-Nearest Neighbors algorithms, the proposed system identifies transaction velocity anomalies indicative of panic-selling and de-pegging events. This methodology fundamentally shifts the analysis of stablecoin fragility from theoretical post-mortem to programmatic, real-time detection. Preliminary architectural evaluations demonstrate that decoupling the data ingestion layer from the predictive inference engine significantly reduces latency, providing financial regulators and researchers with a scalable, deterministic tool for monitoring digital asset stability.
Decentralized Finance (DeFi) has suffered over $5 billion in cumulative losses from security incidents, yet the academic community lacks a large-scale, multi-source-verified dataset to systematically characterize these threats. We present DEFIHACK-824, a curated dataset of 823 DeFi security incidents spanning 2017 to 2026, cross-validated against three independent intelligence sources (Rekt News, SlowMist, and CertiK). Each record is annotated with attack category, confidence level (Gossip/Classified/Ground Truth), and estimated financial loss. We classify incidents into 14 attack categories and conduct statistical analyses: (1) flash-loan-enabled price manipulation and reentrancy together account for 51.5% of all attacks; (2) a chi-squared test rejects the null hypothesis of uniform category distribution at p < 0.0001 (chi-squared = 1,273.2, df = 13); (3) despite widespread deployment of automated detection tools, the annual attack count has not monotonically decreased. We further propose a six-layer DeFi threat model and quantify the effectiveness of four defense classes. The dataset, threat model, and 50 categorized Solidity vulnerability patterns are released under the MIT license.
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
The rapid proliferation of Agentic Artificial Intelligence fundamentally disrupts traditional customer loyalty paradigms. As AI evolves from passive recommendation algorithms to autonomous, goal-directed agents capable of executing purchasing decisions, the conventional understanding of consumer-brand relationships requires a structural reevaluation. By synthesizing extant literature across human-machine teaming, consumer decision-making, and algorithmic trust dynamics, we demonstrate that traditional loyalty models fail to account for algorithmic bounded rationality and constructed autonomy. To address this, we introduce the Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) model. We formalize brand choice via a softmax probability formulation where human emotional equity, agentic machine-experience utility, calibrated trust, delegated authority, and verifiable execution jointly determine selection. The model features recursive updating mechanisms to dynamically calibrate trust and delegation after each interaction. Crucially, the framework integrates a verifiable execution layer for Decentralized Finance (DeFi) and tokenized loyalty settings, incorporating execution risks -- such as gas costs, slippage, MEV exposure, and smart-contract vulnerabilities -- as core predictors of agentic brand preference. Furthermore, we introduce the Net Human-Agent Score (NHAS), an auditable, risk-weighted metric designed to measure human-agent alignment using human feedback, execution logs, benchmark comparisons, and verifiable receipts. Finally, we propose a comprehensive three-stage empirical validation plan spanning controlled shopping experiments, multi-agent market simulations, and DeFi testbeds. This framework provides the foundational theory required for brands to navigate the impending transition toward machine customers.
Yongqiang Du, Chen-Xun Weng, Feng Xie, Ming-Yang Li · 13 authors
Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.
This study investigates the growing role of stablecoins within the global financial system and examines their potential integration into traditional foreign exchange markets. Despite the rapid expansion of stablecoins, empirical evidence comparing their market dynamics with those of non-stable cryptocurrencies remains limited. To address this gap, the study adopts a descriptive case study design based on documentary analysis and secondary quantitative market data. The documentary review establishes the theoretical foundations of stablecoins and their relevance to foreign exchange markets, while the quantitative analysis relies on market data collected from CCData, DefiLlama, and Statista. Weekly market observations covering the period from April 2019 to May 2024 were analyzed using descriptive statistics, comparative analysis, volatility measures, Pearson correlation analysis, and one-way ANOVA. The findings reveal that stablecoins exhibit significantly lower price volatility than Bitcoin while maintaining high levels of market liquidity and trading activity. Among the analyzed assets, Tether (USDT) remains the dominant stablecoin, followed by USD Coin (USDC) and Binance USD (BUSD). The statistical analysis confirms significant differences between stablecoins and Bitcoin, highlighting the distinct market behavior of reserve-backed digital assets. These findings suggest that stablecoins have evolved beyond their traditional role as cryptocurrency trading instruments and are increasingly functioning as efficient mechanisms for cross-border payments, liquidity management, and decentralized finance applications. This study contributes to the literature by providing an integrated empirical comparison of stablecoins and non-stable cryptocurrencies while demonstrating how the stability, liquidity, and operational characteristics of reserve-backed digital assets may facilitate their future integration into traditional foreign exchange markets. The findings also provide practical implications for policymakers, financial institutions, and regulators seeking to develop secure and efficient digital payment infrastructures supported by appropriate regulatory frameworks.
This study examines, within an interdisciplinary framework, how digital technologies are transforming international relations in the domains of security, economics, and diplomacy. It treats developments in artificial intelligence, blockchain, quantum computing, and cybersecurity not as separate technical innovations but as interconnected processes that reconfigure states’ power capacities and their relations of interdependence. A qualitative and interpretive method is adopted, combining a review of the literature, content and discourse analysis, and a comparative examination of the digitalization strategies of the United States, China, the European Union, and Russia. The findings show that cyber conflict opens an enduring arena of contestation that complements rather than replaces traditional military force; that AI-enabled systems accelerate defense and intelligence processes while deepening problems of oversight and accountability; and that quantum computing is fundamentally altering the encryption order and approaches to national security. On the economic plane, the competition between central bank digital currencies and decentralized finance is redefining the notions of monetary sovereignty and financial control. The growing power of large technology companies calls state sovereignty into question, while disinformation and algorithmic targeting create new risks for democratic processes. The study concludes that digitalization is a multilayered process whose outcome is not predetermined, and that grasping this transformation requires extending the classical approaches to power and interdependence so as to encompass the command of data, algorithms, and networks.
Blockchain technology has profoundly revolutionized decentralized applications across financial systems, global supply chains, and applied informatics. However, it remains susceptible to systemic security hazards. This systematic review comprehensively evaluates core architectural vulnerabilities within blockchain infrastructures, consensus mechanisms, and peer-to-peer (P2P) network layers spanning the decade from 2015 to 2025. We focus primarily on the mechanics, operational taxonomy, and evolutionary trajectories of Sybil attacks, wherein malicious actors forge multiple pseudonymous identities to gain disproportionate systemic influence. By synthesizing the foundational academic literature with real-world empirical case studies, such as automated airdrop farming exploits in Layer-2 ecosystems (e.g., Arbitrum, zkSync) and decentralized finance (DeFi) governance manipulations, we analyze attack mechanisms, quantifiable impacts, and mitigation vectors. Our findings chart the structural evolution of Sybil strategies from rudimentary P2P routing disruptions to complex, economically driven application-layer interventions. Finally, we evaluate contemporary defenses, such as Proof-of-Personhood (PoP) systems and zero-knowledge (ZK) cryptography, offering actionable recommendations for the integration of W3C-compliant decentralized identity (DID) frameworks and behavioral analytics to enhance systemic fault tolerance.
Discover how Tokemak is revolutionizing decentralized finance through its advanced liquidity management solution. Get insight into the protocol's mechanism, governance structure, token purpose, and the significance of its emergence in the DeFi world.
The rapid evolution of financial technology has transformed the global financial landscape, creating opportunities for innovation, inclusion, and efficiency while introducing systemic risks, regulatory uncertainties, and challenges to financial stability. This study presents a bibliometric review of global research trends at the intersection of financial technology and financial stability from 2000 to 2025, mapping the intellectual structure, identifying emerging themes, and highlighting influential contributions. Using Scopus data, the analysis examines 339 peer-reviewed documents across 242 sources. Bibliometric techniques were applied through VOSviewer, Bibliometrix (R), and Biblioshiny to evaluate publication trends, influential authors, thematic clusters, co-authorship networks, and keyword co-occurrences. The results show an average annual growth rate of 21.46 percent, with a marked increase in publications after 2017 coinciding with the mainstream adoption of digital finance and heightened policy focus on financial resilience. Findings indicate that financial technology promotes financial inclusion, banking efficiency, and economic empowerment, yet also introduces cybersecurity threats, regulatory gaps, and systemic vulnerabilities, particularly in emerging markets. Dominant themes include blockchain, digital payments, financial literacy, and central bank digital currencies, with decentralized finance and artificial intelligence emerging as fast-growing areas of scholarly interest. Geographically, China leads in publication volume, while the United Kingdom and the United States dominate in scholarly influence. This review provides a strategic roadmap for researchers and policymakers to navigate the evolving financial technology landscape and emphasizes the need for future research to integrate ethical governance, artificial intelligence risk management, and inclusive financial innovation frameworks.
Fahd Ghalib Basheikh, Ida Widianingsih, Ahmad Zaini Miftah
Decentralized government units in the Global South frequently experience ineffective service delivery because of inadequate funding and weak administrative structures. Using Lamu County Government that allocates bursary funds yet continues to experience operational inefficiencies, this study examines how administrative capacity influences the governance effectiveness of the Lamu County Bursary Programme (LCBP). Guided by Administrative Capacity Theory, the study uses an explanatory sequential mixed methods design using quantitative data from 350 beneficiaries and qualitative data from key informant interviews and focus group discussions. Linear regression results show that administrative capacity is a statistically significant predictor of governance effectiveness (β = 0.627, p < 0.001). Thematic analysis from qualitative data shows three constraints: verification problems, aggravated by geographic dispersion and staffing problems; procedural uncertainty and communication problems, that erode the trust of applicants; and a structural timing penalty, where administrative delays reduce the timeliness and reliability of bursary support, sometimes resulting in temporary school exclusion. The results indicate that the LCBP experiences a capability trap, formal structures are in place but service delivery is weak. Therefore, decentralized units require both financial allocations and effective administrative capabilities. To improve policy outcomes, findings suggest the importance of digitization, staffing at ward level and synchronization of the disbursement calendar with academic cycles.
This paper introduces Mirror Protocol as an implementation layer for the Conditions of Understanding. Rather than proposing another theory of understanding, the paper describes a practical method for protecting the conditions under which understanding can emerge. It argues that genuine understanding is often disrupted not by lack of information but by premature evaluation, guidance, intervention, or meaning fixation. Building upon The Conditions of Understanding, the paper presents a five-stage protocol consisting of Reality / Sensation / State, Project Mirror, Friction Detection Point, Meaning Non-Capture Protocol, and Leave to the World. Together these stages describe how one can remain engaged with another person’s process without prematurely directing or completing it. The paper further distinguishes reflecting from indifference, and non-capture from non-response, arguing that restraint is an active practice rather than passive inaction. Friction is interpreted not as failure but as evidence that the protocol is functioning, provided the impulse to intervene is recognized without being acted upon. Mirror Protocol is proposed not as a communication technique but as a general implementation framework for preserving the conditions in which observation, discovery, and understanding are allowed to arise naturally. It concludes by positioning the protocol as a bridge between theoretical principles and future organizational or institutional applications. This paper is part of a four-part series on the conditions and infrastructure of human understanding: This paper uses "Mirror Protocol" as a concept within Maura Theory, an independent theoretical framework concerning the conditions of human understanding. It is unrelated to the decentralized finance (DeFi) protocol of the same name operating on the Terra blockchain. (1) From Information Access to Meaning Recognition: Professional Expertise After the Cost of Information Collapses https://doi.org/10.5281/zenodo.21230076 (2) The Conditions of Understanding: Protecting the Conditions Under Which Understanding Emerges https://doi.org/10.5281/zenodo.21251927 (3) Mirror Protocol: An Implementation Layer for the Conditions of Understanding https://doi.org/10.5281/zenodo.21252084 (4) Understanding Infrastructure: Scaling the Conditions of Understanding to Organizations and Institutions https://doi.org/10.5281/zenodo.21252316
Aleksandar Stojkov, A. Maksimovska Stojkova, Elena Neshovska Kjoseva, Jovan Zafiroski
This study investigates how a territorially uneven distribution of informal economic activity affects subnational fiscal capacity and potentially distorts fiscal equalization systems. Using a Multiple Indicators, Multiple Causes (MIMIC) model, we estimate the size of the informal economy across the eight statistical regions of North Macedonia over the 2008–2023 period. The estimated shares of regional informality are subsequently linked to indicators of fiscal dependence and local revenue performance. The findings suggest that regions characterized by larger informal economies tend to exhibit greater dependence on intergovernmental transfers and weaker effective fiscal autonomy. The analysis further indicates that intergovernmental transfer systems relying primarily on regional gross domestic product and realized tax collections may systematically underestimate the true economic potential of highly informal jurisdictions. The paper contributes to the literature by conceptualizing informality not merely as an informal economic activity, but as a structural distortion affecting the measurement of fiscal capacity and the functioning of decentralized public finance systems.
Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value.
Zero-knowledge privacy protocols let users hide transaction details on public blockchains. Systems like Tornado Cash, FixedFloat, and the Houdini Private Swap feature recently added to Jumper rely on cryptographic techniques that unlink sender and receiver addresses. These constructions give legitimate users meaningful protection for their financial activity. They also create a straightforward dual-use dilemma. The February 2025 Bybit incident supplies a clear example. Thieves stole $1.5 billion in ETH, the largest cryptocurrency theft on record. The FBI linked the attack to North Korea’s Lazarus Group. The stolen funds moved quickly through Tornado Cash. The resulting lack of transparency triggered a wave of customer withdrawals. Bybit responded by securing loans of several hundred million dollars from other institutions to keep its operations running. Cases like this demonstrate that zk-based privacy tools, when used at large scale for illicit purposes, can accelerate liquidity crises and place costs on market participants who had no involvement in the original theft. The real problem is not the underlying mathematics that delivers privacy. It lies in the missing mechanisms that could impose accountability on criminal actors while leaving the privacy protections for everyone else intact.
Blockchains have evolved from simple distributed ledgers into programmable platforms that process complex application logic and carry significant financial value. All modern Web3 systems share a common goal: providing secure, decentralized, and trustworthy execution in an increasingly interconnected environment. However, this evolution has shifted the attack surface from isolated infrastructure disruptions to programmable economic abuse and cross-domain exploits. In this article, we focus on the research of blockchain attacks and defenses. In particular, we categorize the threat landscape and corresponding mitigation strategies according to both a four-tier layered architecture (network, cryptographic, consensus, and application) and cross-domain trust boundaries. We seek to answer these important questions: How has the research in blockchain security evolved over the past decade, especially with the rise of decentralized finance (DeFi) and cross-chain interoperability? How do local security assumptions fail when protocols are composed, and what are the driving needs for Web3 security research in the future?