Blockchain Papers

Malcolm Kinney

No abstract is available for this record.

Ankur Malik

Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor addresses -- yet compliance action is conducted per actor. This paper contributes an evaluation methodology for measuring how scoring granularity affects investigation queue composition under fixed review budgets. We formalize the evaluation through a projection framework mapping transaction-level scores to the actor-level action unit via four aggregation operators, and introduce budgeted investigation metrics -- yield@budget, burden decomposition, and case fragmentation. Using the public Elliptic++ Bitcoin dataset (203,769 transactions; 822,942 address occurrences), we train independent random forest classifiers at each level under a causal temporal protocol and compare review queues through Jaccard overlap, burden decomposition, and feature-matching ablations. At one-percent budget, temporal evaluation yields mean Jaccard of 0.374 (SD 0.171); static pooled evaluation yields 0.087 (95% CI [0.079, 0.094]). An enriched address model receiving all 237 features produces even lower overlap (Jaccard=0.051), with 4.3% illicit per 100 reviews versus 30.2% for the transaction-projected queue. Address-level detection value is temporally concentrated: two timesteps exceed 91% illicit per 100 reviews while the static burden is only 3.4%. A fixed hybrid policy underperforms the best single-level queue by 5.05pp (CI [-10.2pp, -0.9pp]). These findings establish that scoring granularity is a consequential design variable for AML investigation systems -- same data, same budget, different queues, different addresses investigated.

changzheng zhou, ziqing zhou

Existing theories of decentralized systems—typified by blockchain consensusprotocols and distributed autonomous organizations—universally harbor a foundational presupposition: governance rules and protocol structures are fully specifiedprior to system operation, and evolution occurs only within the parameter spaceof those rules. This paper systematically demonstrates the theoretical limits ofthis “fixed protocol” preset, pointing out that when the rules themselves becomethe focal point of conflict, traditional analytical frameworks lack the conceptualresources to address the situation. By integrating the bounded rationality tradition from decision theory with the self-organization ideas from complexity science,this paper proposes “cognitive ecosystem” as an alternative theoretical framework,reconceptualizing participants in decentralized systems as autonomous agents holding evolvable cognitive architectures, and redescribing the system as a whole as afield of structural coupling among multiple cognitive architectures. Under thisframework, forks are not system failures but legitimate expansions of conceptualspace, and consensus is not unanimous agreement but functional differentiationacross cognitive niches. The paper demonstrates the explanatory power of thisframework through the cases of the Bitcoin block size war of 2015–2017 and the2016 The DAO incident, and discusses its further application prospects in the governance of digital infrastructure.

Dustin M. Haggett

Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for cryptocurrency regime prediction. We evaluate three image encoding methods (raw candlestick charts, Gramian Angular Fields, and multi-channel GAF), five chart component configurations, four neural network architectures (CNN, ResNet18, EfficientNet-B0, and Vision Transformer), and the impact of ImageNet transfer learning. Through eight controlled experiments on Bitcoin, Ethereum, and S&P 500 data spanning 2018-2024, we identify optimal configurations for visual regime classification. Our results show that a simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC, outperforming larger pretrained models. Surprisingly, simpler representations (price-only charts, 128x128 resolution) consistently outperform more complex alternatives. We provide interpretability analysis using GradCAM and demonstrate that transfer learning improves performance by 4-16% despite the domain gap between natural images and financial charts.

Saket Maganti

The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not been rigorously stress-tested under a leakage-free evaluation protocol. We perform a seed-matched inductive-versus-transductive comparison and find that this consensus does not hold. Under a strictly inductive protocol, Random Forest on raw features achieves F1 = 0.821 and outperforms all evaluated GNNs, while GraphSAGE reaches F1 = 0.689 +/- 0.017. A paired controlled experiment reveals a 39.5-point F1 gap attributable to training-time exposure to test-period adjacency. Additionally, edge-shuffle ablations show that randomly wired graphs outperform the real transaction graph, indicating that the dataset's topology can be misleading under temporal distribution shift. Hybrid models combining GNN embeddings with raw features provide only marginal gains and remain substantially below feature-only baselines. We release code, checkpoints, and a strict-inductive protocol to enable reproducible, leakage-free evaluation.

Danila Valko, Jorge Marx Gómez

The Lightning Network (LN) is a rapidly evolving payment channel network that enables scalable, off-chain transactions on top of Bitcoin. While prior research has documented its topological structure and liquidity concentration, the joint relationships between node lifetime, connectivity, and capacity remain insufficiently understood. This study provides a comprehensive empirical analysis of these relationships using a large-scale dataset of LN topology snapshots spanning the period 2019-2023. We examine whether node lifetime influences shared channel capacity, and whether this effect is mediated and moderated by node degree. In addition, we account for hierarchical geographic structure and explore the role of country-level economic conditions. The results show that node lifetime has a positive but relatively modest direct effect on capacity. This relationship is largely mediated by node degree, indicating that liquidity accumulation primarily occurs through increased connectivity. Furthermore, the interaction between lifetime and degree reveals significant heterogeneity, with stronger effects observed among highly connected and high-capacity nodes. Mixed-level models demonstrate superior explanatory power, highlighting the importance of country- and region-level variation. The inclusion of GDP per capita confirms that broader economic conditions significantly influence capacity distribution. Overall, the findings suggest that liquidity in the LN emerges from the interplay of temporal dynamics, network structure, and economic context. This study contributes to a more integrated understanding of payment channel networks and provides a foundation for future research on their evolution and efficiency.

Wisam Bukaita, Xinrui Li

This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption.

Daniel Aronoff, Kristian Praizner, Armin Sabouri

Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone because the relevant queueing environment is unobserved. We develop and estimate a structural model of Bitcoin fee choice that treats the mempool as a market for scarce blockspace. We assemble a novel, high-frequency mempool panel, from a self-run Bitcoin node that records transaction arrivals, exits, block inclusion, fee-bumping events, and congestion snapshots. We characterize the fee market as a Vickery-Clarke-Groves mechanism and derive an equation to estimate fees. In the first-stage we estimate a monotone delay technology linking fee-rate priority and network state to expected confirmation delay. We then estimate how fees respond to that delay technology and to transaction characteristics. We find that congestion is the main determinant of delay; that the marginal value of priority is priced in fees, which is increasing in the gradient of confirmation time reduction per movement up in the fee queue; and that transactor choice of RBF, CPFP, and block conditions have economically important effects on fees.

Héritier Kayembe Mpiana, Eugene mukendi Mbuyi, Jean Didier Mwambanzambi Batubenga, Pierre Motumbe Kasengedia

This paper proposes the design and evaluation of a secure electronic payment system based on the Ethereum blockchain, applied to the payment of academic fees. The objective is to enhance transparency, security, and automation of financial transactions within higher education institutions. The methodology relies on developing a prototype using smart contracts, tested on Ethereum testnets. Experimental results show that the system reduces processing times and improves transaction traceability [1]. The integration of Layer 2 solutions and stablecoins also helps reduce transaction costs and improve scalability. However, challenges remain, particularly regarding regulation and user accessibility. As a decentralized and programmable platform, Ethereum represents a major innovation capable of transforming traditional payment systems. The emergence of Ethereum-based academic fee payment systems is part of an accelerated digital transformation and the search for alternatives to conventional financial infrastructures. Since the introduction of Bitcoin, the global financial system has undergone a profound shift, marked by the adoption of decentralized technologies [3]. This study required an in-depth technical understanding of the Ethereum blockchain, along with critical, economic, and regulatory analyses [5].

Priviledge Cheteni, Herrison Matsongoni

This study examines the factors contributing to cryptocurrency adoption in South Africa. This study utilized an exploratory research design that applied a qualitative technique. 10 key informants were selected using purposive sampling from organizations involved in the bitcoin industry in South Africa. The study demonstrates that the adoption of cryptocurrencies in the country is influenced by factors such as financial inclusion and access, innovation and entrepreneurship, economic diversification and regulatory frameworks, and teamwork. The challenges and hurdles encompass legislative ambiguity, cybersecurity risks, investor safeguarding, financial education and awareness, infrastructure limitations, and accessibility issues. The findings indicate that adopting cryptocurrencies can enhance financial inclusion, stimulate innovation and entrepreneurship, and tackle systemic problems in the financial industry. Nevertheless, the effective implementation and assimilation of cryptocurrencies in South Africa will necessitate a collaborative endeavour among all parties involved. Robust regulatory frameworks, comprehensive educational programmes, and cooperative endeavours are essential for maximizing the advantages of cryptocurrencies while minimizing the accompanying hazards.

Mustafa Doger, Sennur Ulukus

We consider the block withholding attacks on pools, more specifically the state-of-the-art Power Adjusting Withholding (PAW) attack. We propose a generalization called Temporary PAW (T-PAW) where the adversary withholds a fPoW from pool mining at most $T$-time even when no other block is mined. We show that PAW attack corresponds to $T\to\infty$ and is not optimal. In fact, the extra reward of T-PAW compared to PAW improves by an unbounded factor as adversarial hash fraction $α$, pool size $β$ and adversarial network influence $γ$ decreases. For example, the extra reward of T-PAW is 22 times that of PAW when an adversary targets a pool with $(α,β,γ)=(0.05,0.05,0)$. We show that honest mining is sub-optimal to T-PAW even when there is no difficulty adjustment and the adversarial revenue increase is non-trivial, e.g., for most $(α,β)$ at least $1\%$ within $2$ weeks in Bitcoin even when $γ=0$ (for PAW it was at most $0.01\%$). Hence, T-PAW exposes a significant structural weakness in pooled mining-its primary participants, small miners, are not only contributors but can easily turn into potential adversaries with immediate non-trivial benefits.

Aktham Maghyereh, Basel Awartani

This study examines the latent common volatility factor in cryptocurrency markets using daily data for ten major cryptocurrencies from January 2018 to September 2025. It estimates the common volatility factor (COVOL) within the factor-volatility framework of Engle and Campos-Martins (2023) and it identifies its determinants using machine learning and SHAP analysis. Results reveal a statistically significant common volatility factor that intensifies during major macroeconomic events and crypto-specific shocks. Bitcoin exhibits the highest exposure, while global financial stress and investor sentiment are found to be the primary drivers. This paper provides the first direct estimation of a common volatility factor in cryptocurrency markets, demonstrating their increasing integration with global financial conditions and offering important implications for risk management and portfolio diversification.

Lai T. Hoang, Trang Thu Phan

Using a comprehensive dataset from Deribit, we show that Bitcoin options trading activity is concentrated around two distinct intraday periods: 8:00–9:00 GMT and 14:00–15:00 GMT, relative to other hours of the day. The latter peak coincides with the opening of the New York Stock Exchange and is largely absent on weekends, suggesting spillovers from traditional equity markets to the Bitcoin options market. In contrast, the concentration of trading activity around the 8:00–9:00 GMT period appears to be driven by investors rolling over and re-establishing expiring options around the 8:00 GMT settlement, as this effect persists on both weekdays and weekends, and is stronger on days with more expiring contracts and for contracts with shorter maturities. These findings highlight how institutional trading conventions shape intraday activity in cryptocurrency derivatives and provide the first systematic evidence of intraday patterns in Bitcoin options trading.

Paul T. Sheppard

The frozen SUPT-CA phase-coherence probe (α = 0.01, zero free parameters) was applied to live blockchain data from Bitcoin, Ethereum, Solana, Cardano, and Polkadot. Consensus mechanism design directly determines geometric regime: deterministic hardware clocking (Solana, Polkadot) produces deep-lock distributions; regulated proof-of-stake with fee targeting (Ethereum, Cardano) produces coherence-zone distributions; probabilistic proof-of-work (Bitcoin) produces clutch-band timing with sub-floor transaction variability. A validated congestion oracle signal is identified for Ethereum: transaction count d_ij crossing 1.0 in a rolling 150-block window marks network congestion onset, confirmed against the May 2024 memecoin congestion event. All data from live public RPC endpoints, April 15, 2026. No parameters adjusted.

Javier Cifuentes-Faura, Hind Alofaysan, Magdalena Radulescu, Buhari Doğan

This study employs novel decomposed connectedness and portfolio analysis to assess the dynamic spillover effects among carbon finance, artificial intelligence, green energy markets, and bitcoin. The findings indicate that the average total connectedness index is 62%, especially during extreme market conditions. The decomposition of this measure into contemporaneous and lagged connectedness reveals that 56% of the metric can be attributed to contemporaneous dynamics. The portfolio exhibits high Hedging Effectiveness, particularly in extreme market conditions, suggesting that green assets can mitigate risks during periods of financial and geopolitical turmoil. The outcome shows that investments in Bitcoin and technology-related assets often yield the highest returns from 2018 to 2023. Based on the findings, relevant investment policies have been suggested for investors and policy decision-makers.

Dr. G. V. Mahesh Naath

Due to the fast development of cryptocurrency and blockchain technologies, the field of financial innovation, data privacy, and legal regulation has become a complex area with a multi-faceted regulatory environment. In this paper, the authors discuss the critical problem of ensuring the rights to privacy of individuals and the necessity of an effective control over the regulatory framework in decentralized digital financial systems. Although cryptocurrencies like Bitcoin have facilitated peer-to-peer payments, increased transparency, and financial inclusion, their pseudonymous and borderless characteristics have also brought serious concerns associated with money laundering, terrorist funding, market volatility, and consumer protection. In a comparative and interdisciplinary approach, the research assesses the current regulatory reactions and outlines the increasing role of international principles, constructed by the Financial Action Task Force. It contends that the conventional approaches to regulation, which were developed to deal with centralized financial institutions, cannot deal with the contingencies of decentralized ecosystems. In this regard, the paper will present a technology-based governance model that incorporates the use of law, institutional, and technological solutions to emerge with a harmonious regulatory strategy. This is highlighted in the study as the new technologies including blockchain analytics, artificial intelligence, smart contracts, and privacy protection tools like zero-knowledge proofs could be used to facilitate regulatory compliance without compromising user privacy. It also highlights the significance of risk-based, adaptive regulation, regulatory sandboxes and international collaboration in reducing regulatory arbitrage and global financial integrity. Finally, the paper argues that the future of cryptocurrencies regulation is in the creation of adaptable, innovation-oriented, and privacy-sensitive rules. A balance between law and technology can enable policymakers to create a secure, transparent, inclusive digital financial ecosystem and protect basic rights and the larger interest of society.

Abdulilah I. Mubarak

Type of the article: Research ArticleAbstractCryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.Acknowledgment“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”

Godala

This paper examines the evolving relationship between blockchain architecture and financial privacy, focusing on the inherent tension between transparency, pseudonymity, and regulatory oversight. It begins by analysing the structural foundations of blockchain systems, including distributed ledgers, cryptographic security, and decentralized consensus mechanisms, which collectively replace institution-based trust with system-based verification. While such architecture enhances transparency and immutability, it simultaneously generates new privacy challenges. Through a comparative analysis of Bitcoin, Monero, and Zcash, the paper highlights a spectrum of privacy designs within the cryptocurrency ecosystem. Bitcoin represents a model of transparent yet pseudonymous transactions, where public ledger visibility enables traceability despite the absence of explicit identity markers. In contrast, Monero adopts a privacy-centric approach using ring signatures, stealth addresses, and confidential transactions to obscure sender, receiver, and transaction value. Zcash introduces a hybrid model, employing zero-knowledge proofs (zk-SNARKs) to reconcile transactional confidentiality with verifiability, alongside selective disclosure mechanisms. The study further explores the limitations of transparent blockchains, including risks of transaction traceability, address clustering, and linkage to real-world identities through regulatory touchpoints such as exchanges. It also evaluates the regulatory implications of privacy-enhancing technologies, particularly their impact on anti-money laundering (AML) and counter-terrorism financing (CTF) frameworks. The paper underscores the growing role of international standards and regulatory bodies in shaping compliance mechanisms within decentralized ecosystems. Finally, the paper considers emerging solutions such as privacy-preserving smart contracts, decentralized identity systems, hybrid blockchain models, and regulatory technologies (RegTech), which aim to balance user privacy with legal accountability. It argues that the future of blockchain governance lies not in choosing between transparency and privacy, but in developing adaptive frameworks that integrate both. The study concludes that achieving this balance will require sustained interdisciplinary collaboration and coordinated global regulatory efforts.

Maxime L. D. Nicolas, François Sicard, Marion Laboure, Zixin Sun · 5 authors

This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.

José Pedro Ramos-Requena, Mahmut Bağcı

Abstract This study proposes a methodological strategy composed of econometric techniques and time series modelling to analyse the dynamic asynchrony between Bitcoin and a basket of traditional sustainable financial assets and emerging markets over a 10-year period marked by major economic and financial changes. The centrepiece of this proposal is the Relation Index that combines vector autoregression and detrended cross-correlation analysis to capture linear and nonlinear dependencies, causality, and time-scale sensitive correlations. Thus, this research fills existing gaps in understanding cross-market interdependencies by integrating cryptocurrencies, sustainability indices, and emerging economies into a rigorous multivariate time series framework. Sustainability indices, emerging markets and Bitcoin have shown a growing correlation since 2020, with both interest rates and Bitcoin having strong autoregressive components. The findings indicate that emerging market equities have undergone a structural shift towards synchronisation with global risk assets, with a correlation index that frequently exceeds 0.6 in periods of systemic stress. This evolution highlights the decline in the advantages offered by diversification in developed and developing economies in a complex and interrelated financial environment.

Mr. Heet Vipulkumar Chaudhary

Stock markets in emerging economies are shaped by a combination of global integration and domestic financial drivers. In recent years, modern variables such as cryptocurrencies have drawn attention as potential new determinants of equity performance. This study evaluates the comparative influence of traditional variables-Foreign Institutional Investor (FII) flows, USD/INR exchange rate, and NIFVIX-and a modern variable, Bitcoin returns, on the Nifty50 index. Monthly data spanning January 2015 to January 2025 were collected from Investing.com and Moneycontrol. Nifty50, Bitcoin, and USD/INR series were converted into log returns, while FII flows and NIFVIX were used in their original form. Correlation analysis and simple linear regression were done by using Microsoft Excel to measure associations and explanatory power. The results indicate a clear hierarchy of explanatory strength. USD/INR log returns emerged as the most influential determinant, explaining 26% of Nifty50 return variation with a strong negative relationship. NIFVIX explained 14% of the variation, also with a negative and highly significant effect. Bitcoin returns exhibited a modest but statistically significant positive effect, explaining around 8% of the variance. In contrast, both FII equity and total flows were statistically insignificant. The findings suggest that traditional variables-particularly exchange rates and volatility indices-remain dominant drivers of Indian equity returns, while modern variables such as Bitcoin are new but not yet central. The study contributes by showing one of the first systematic comparisons between traditional and modern variables in the Indian equity market context. Keywords: Nifty50, Bitcoin Returns, Foreign Institutional Investors (FII), USD/INR Exchange Rate, NIFVIX, Traditional vs. Modern Variables, Indian Stock Market

IJESAT

A hybrid analytical framework is developed for the forensic investigation of Bitcoin transaction networks, addressing the inherent challenges posed by the decentralized and pseudo-anonymous characteristics of blockchain systems. While Bitcoin transactions are publicly accessible, detecting illicit activities within complex transaction graphs remains a significant challenge. Existing approaches typically depend on isolated techniques, such as rule-based methods or standalone machine learning models, which often lack sufficient effectiveness.The proposed framework combines graph-based network analysis, statistical modeling, and machine learning to enhance detection capability. Transactions are represented as a directed graph, where wallet addresses function as nodes and transactions as edges. From this representation, structural, behavioral, and temporal features are systematically extracted and integrated into a unified dataset. A Random Forest classifier is subsequently employed to categorize wallet addresses as either normal or suspicious.This integrated approach improves accuracy, scalability, and robustness, facilitating efficient analysis of large-scale blockchain data and enabling more reliable identification of fraudulent activities in real-world forensic investigations.

Manuel Mueller-Frank, Minghao Pan, Omer Tamuz

The value of proof-of-work cryptocurrencies critically depends on miners having incentives to follow the protocol. However, the Bitcoin mining protocol proposed by Nakamoto (2008) and implemented in practice is well known not to constitute an equilibrium: Eyal and Sirer (2018) construct a profitable deviation called ``selfish mining'' which relies on strategically delaying disclosure of newly mined blocks rather than publishing them immediately. We propose inertial mining, a novel mining protocol. When miners follow inertial mining, they produce the outcome intended by Nakamoto, i.e., a single longest chain. But unlike the Bitcoin mining protocol, inertial mining constitutes an equilibrium (assuming no miner controls more than half of the mining power). Indeed, neither selfish mining nor any other deviation is profitable. Furthermore, inertial mining only changes miners' behavior in the event of off-path forks, and can be implemented in Bitcoin without any changes to its consensus mechanism or blockchain architecture.

Tiago Ferreira Cavazin

O presente artigo investiga como o tamanho do bloco afeta a propagação em redes blockchain, recorrendo à modelagem estocástica para quantificar os trade-offs entre throughput, segurança e descentralização. Estudos teóricos e empíricos indicam que blocos maiores elevam o tempo médio de propagação e a variância desse tempo, aumentando a probabilidade de forks e de blocos órfãos em mecanismos de consenso baseados em Prova de Trabalho (PoW) e variantes de Nakamoto. Modelos analíticos e de simulação demonstram que a relação entre o intervalo médio de geração de blocos e o atraso médio de propagação pode ser tratada por meio de sistemas de filas ou de processos de Poisson, nos quais a taxa de forks cresce quando o produto “taxa de blocos × atraso de propagação” se aproxima de um limiar crítico associado a um regime congestionado. Resultados de trabalhos de otimização de tamanho de bloco em PoW sugerem a existência de um tamanho “ótimo” que maximiza a eficiência econômica da rede – isto é, transações por segundo ponderadas pelo risco de órfãos –, e que esse ótimo depende fortemente da largura de banda média da rede e do grau de heterogeneidade entre nós. Evidências empíricas da rede Bitcoin mostram ainda que melhorias de protocolo, tais como Compact Blocks e redes de relay dedicadas, reduzem significativamente o impacto negativo de blocos maiores sobre a propagação, conquanto não eliminem o viés estrutural em favor de nós com melhor conectividade. Conclui-se que a modelagem estocástica do impacto do tamanho de bloco é fundamental para parametrizar blockchains de modo a manter a rede em regime funcional, minimizando taxa de forks e força centralizadora, ao mesmo tempo em que se atende à demanda por maior capacidade transacional na Web3.Blockchain