Francesco Maria De Collibus, Carlo Campajola, Claudio J. Tessone
Abstract The transfer velocity of money is a macroeconomic quantity that measures the frequency of exchanges in an economy. For cryptoassets it can be exactly measured adopting a new approach, MicroVelocity. In this study we apply the framework to Ether, the native cryptocurrency of the Ethereum blockchain, to investigate velocity and its top contributors and how they can be characterised in the Ethereum ecosystem. While the inequalities and heterogeneity in wealth are well known, we here find that the same inequalities occur as well for MicroVelocity distribution and that this inequality is not explained just by wealth, but rather by the behaviour and economic activity of each individual agent.
This article delves into the transformative impact of blockchain technology on enhancing transaction quality and efficiency. Since the emergence of blockchain alongside Bitcoin in 2008, its decentralised and transparent nature has significantly improved transaction speed, security, and cost efficiency. These advancements have solidified blockchain as a foundational innovation in financial services. The paper examines critical milestones in blockchain, including Bitcoin, Ethereum, and Binance Coin (BNB), and their role in reshaping global finance by automating processes and reducing reliance on intermediaries. Additionally, the study evaluates blockchain’s impact on quality management, particularly emphasising how its immutable ledger system enhances the reliability and transparency of financial transactions. Despite challenges such as scalability, energy consumption, and regulatory hurdles, the potential for blockchain to redefine transaction quality in financial services is evident. This research contributes to the growing body of literature by integrating blockchain technology and traditional quality management systems, providing a comprehensive perspective on how the two domains influence one another. The findings underscore blockchain’s ability to drive innovation in financial services while addressing security, efficiency, and operational quality concerns.
Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, such as Bitcoin -- or digital items and services in more permissive systems based on smart contracts such as Ethereum.
This thesis explores the implementation of a Self-Sovereign Identity (SSI) system using Ethereum and Decentralized Identifiers (DIDs). The project focuses on leveraging blockchain technology to create a secure and decentralized framework for digital identity management, incorporating Verifiable Credentials (VCs) and Verifiable Presentations (VPs). Key components include DID document management, secure user authentication, and user-friendly interface. What makes this system different is the integration with existing wallets, privacy and user control through selective disclosure, allowing users to share only necessary information, and key rotation. It also uses EIP-712 signatures for secure and structured data signing, which allows users to clearly see and understand what they are signing while the cryptography is securely handled by the wallet. Future work will focus on adding more wallet support, improving data storage, and enhancing system scalability and security.
Anak Agung Surya Pradhana, Kadek Suarjuna Batubulan
Forecasting Ethereum price changes presents challenges due to the cryptocurrency market’s volatility and rapid fluctuations. This study applies Long Short-Term Memory (LSTM) networks to predict Ethereum price trends using hourly historical data. The LSTM model captures temporal dependencies effectively, achieving moderate accuracy with a Root Mean Squared Error (RMSE) of 11.42. It performs well in stable market conditions, with predicted prices closely aligning with actual values, validating its potential for identifying long-term trends. However, the model struggles during high-volatility periods, failing to predict abrupt price spikes and market crashes accurately. Overfitting is also observed, indicated by disparities between training and test errors, limiting the model’s generalizability to unseen data. To address these issues, this research suggests incorporating features such as trading volumes, market sentiment, macroeconomic indicators, and blockchain metrics to enhance predictive accuracy. Additionally, employing advanced architectures like attention mechanisms, hybrid models, and real-time learning frameworks is recommended to improve adaptability and robustness in dynamic market environments. These enhancements aim to create a more comprehensive and reliable predictive tool. This study contributes to the advancement of predictive analytics in cryptocurrency markets, offering valuable insights for traders, investors, and policymakers navigating the complexities of digital finance.
As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they do elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 35 specific issues. Specifically, we propose a large language models (LLMs)-based framework, SCALM. It combines Step-Back Prompting and Retrieval-Augmented Generation (RAG) to effectively identify and address various bad practices. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.
Elia Henrichs, Meta Leonie Boller, Johnathan Stolz, Christian Krupitzer
Background: Food and pharmaceutical supply chains face similar issues, such as product counterfeits allowing low-quality products to enter the market and supply chain inefficiencies. Applying blockchain technology could increase transparency and efficiency in the supply chains. However, the technology is still relatively young and, thus, has barely been implemented. Scope and Approach: This work aims to provide an overview of blockchain applications in food and pharmaceutical supply chains. Following the PRISMA method, the systematic literature review analyzes 78 applications in 74 publications. Deriving from the results, a general framework for blockchain applications in food and pharmaceutical supply chains is proposed, which should support practitioners in implementing blockchains and researchers in identifying research challenges. Key Findings and Conclusions: The literature review reveals that permissioned and private blockchain networks are most commonly applied, using Ethereum and Hyperledger Fabric as leading platforms. Many applications stored data off the blockchain and implemented different techniques to restrict access to confidential data. Smart contracts are crucial for improving supply chain management as they enable automatization. The general framework recommends a permissioned consortium network using the Hyperledger Fabric platform and Proof-of-Authority consensus protocol for supply chains. Challenges like regulations, standardization, and infrastructure must be solved to foster technology adoption in operations. • Review of blockchain technology applied in food and pharmaceutical supply chains. • Following a structure literature review, we reviewed 78 applications in detail. • Permissioned and private blockchain networks are applied most. • We propose a framework to foster blockchain implementation in these supply chains. • Challenges like regulations, standardization, and infrastructure as current hurdles.
Elvira Albert, Samir Genaim, Daniel Kirchner, Enrique Martin-Martin
Program optimization is a key factor for green software. In the context of the Ethereum blockchain, optimization is particularly relevant because there is a fee to pay for each EVM (Ethereum Virtual Machine) instruction executed and also there exist bytecode-size limitations for deploying the software on the blockchain. Still, optimization of EVM code is not as widely spread as one could imagine. This is at least partly due to the lack of trust in the correctness of the tools, as security is even more relevant than efficiency in the blockchain context in which bugs may cause huge economical losses. This article develops a formal verification framework using Coq to ensure the security of EVM optimizations performed on jump-free sequences of EVM bytecode. By means of Coq’s theorem proving capabilities, we are able to automatically verify/certify that an optimized jump-free sequence of EVM opcodes is semantically equivalent to a given original one. We also present an extension to our framework that can handle inter-block optimizations that propagate global information across blocks. We have applied our tool to successfully prove the security of peephole optimizations performed by the standard Solidity compiler, and also to existing EVM superoptimization tools (namely GASOL and Superstack) in which we have found bugs that have been reported and fixed.
In recent times, the number of fake drugs has increased dramatically, which has resulted in millions of victims severely affected by poisoning and treatment failures, resulting in a need for Drug Supply Chain (DSC) traceability. The DSC is generally reluctant to share traceability data and includes several parties having heterogeneous interests. Moreover, existing provenance and traceability systems for DSCs need more trust, data sharing transparency, and separated data storage. By realizing decentralized, trustless systems, a decentralized Blockchain (BC)-based solution is proposed to tackle these constraints. BC is an immutable, decentralized, shared network that allows management directly through a peer-to-peer (P2P) network without the necessity of a central authority to check transactions. This study proposes a new Blockchain Non-Fungible Token-based Drug Traceability with Enhanced Pharmaceutical Supply Chain Management (BNFTDT-EPSCM) model. The proposed BNFTDT-EPSCM model presents transparent and more secure reporting of changes in the operating condition of transported pharmaceutical products to prevent drug recalls. The Ethereum BC enables transactions and computational services using the cryptocurrency Ether (ETH). Simultaneously, an enhanced Byzantine fault-tolerant consensus (RB-BFT) leverages a reputation system to address reliability issues of primary nodes and reduce communication complexity inherent in the Practical Byzantine algorithm (PBFT). The BNFTDT-EPSCM model presents a decentralized solution using Non-Fungible Tokens (NFTs) to improve the traceability and tracking capabilities of the standard serialization process. In addition, the BNFTDT-EPSCM model employs a Deep Belief Network (DBN) approach to perform the inbound logistics task prediction process. Finally, the Tasmanian Devil Optimization (TDO) method is utilized to enhance the hyperparameter tuning of the DBN approach. A detailed set of simulations was executed to examine the effectiveness of the BNFTDT-EPSCM approach, demonstrating a higher throughput at the highest user count of 6000 and achieving 551.22 TPS, significantly outperforming existing models.
Abstract The blockchain was initially designed to secure cryptocurrency transactions, a distributed and unalterable registry technology. After its innovative application in the world of cryptocurrencies, solutions based on this technology are now offered to deal with various issues in various sectors, such as the agricultural sector. It is currently one of the most disruptive technologies. This article explores the convergence of Hyperledger Aries blockchain technology and artificial intelligence to improve the management of agricultural disease detection data. We present an innovative system that guarantees complete traceability of each data point, from initial detection to analysis results. Blockchain ensures the transparency and immutability of information, while artificial intelligence, integrated into the detection process, accurately distinguishes infected sheets from healthy ones. This approach provides a robust solution for sustainable agriculture, enabling rapid and targeted response to disease threats while ensuring data integrity.
Technology enhancement through blockchain in the healthcare sector is lately increased for ehealth solutions.Due to its decentralised nature and reliability, blockchain has shown to have significant potential in various e-health businesses, including the secure transfer of patient electronic health records (EHRs) across different healthcare beneficiaries.The proposed Ethereum blockchain based on patient-centric architecture gives complete freedom to patients to access private data and preserves rights to share that data with other entities of the system.This article mainly focuses on building a secure Ethereum blockchain (EBC) based healthcare data security system and reducing execution gas costs for proposed attacks on the system.IPFS off-chain storage helps to address the blockchain's scalability problem.Two different attacks have been tested on the proposed system with reference to execution time for attacks.This article also proposed countermeasures for these tested attacks and the system ultimately plays an important role in sharing sensitive healthcare data.
Blockchain-based smart contracts offer a promising approach to securely manage and monetize machine learning (ML) models within the banking and finance sector. This paper introduces a protocol leveraging Ethereum blockchain technology to create trustless contracts for the evaluation and exchange of ML models, enabling financial institutions to access high-quality predictive models without direct reliance on third-party trust. Financial firms can submit datasets and evaluation functions to the blockchain, establishing automated contracts that incentivize data scientists to submit optimized ML models for specific financial applications like credit scoring, fraud detection, or risk assessment. Our approach leverages cryptographic validation to ensure model performance transparency and accuracy, eliminating counterparty risk and establishing a decentralized marketplace for AI solutions in finance. This blockchain-driven protocol not only facilitates secure model exchange but also promotes competitive pricing and the efficient use of computational resources, thus enhancing AI accessibility in finance while mitigating operational and cybersecurity risks.
2017'den sonra kripto para birimlerinde yaşanan fiyat dalgalanmaları, getiri fırsatları ve volatilite, yatırımcıların ilgisini çekerken; büyüyen işlem hacmi ve piyasa değeri, bu varlıkları geleneksel yatırımlara ek olarak yüksek kazanç ve portföy çeşitlendirme olanağı sunan bir seçenek haline getirmiştir. Buradan hareketle çalışmada piyasa değeri en yüksek üç kripto para biriminin (Bitcoin, Ethereum ve Tether USDt) 2017-2024 dönemi için volatilite düzeyleri asimetrik volatilite ölçüm modellerinden EGARCH modeli ile karşılıklı olarak incelenmektedir. EGARCH modellerine göre, Bitcoin ve Ethereum'da kötü haberler, getiri volatilitesini iyi haberlerden daha fazla etkilerken, kaldıraç etkisi gözlemlenmiştir. Buna karşılık, Tether USDt'de iyi haberlerin volatilite üzerindeki etkisi daha güçlü olup, anti-kaldıraç etkisi söz konusudur. Piyasadaki şokların, kripto paraların getiri volatilitesi üzerinde daha kalıcı bir etkiye sahip olduğu ve en çok Ethereum'un getiri oynaklığını etkilediği görülmektedir. Yarı ömür volatilite ölçüsü sonuçları, Bitcoin, Ethereum ve Tether USDt için sırasıyla 7 gün, 8 gün ve 74 gün olduğunu ortaya koymuştur. Bu durum, Bitcoin ve Ethereum’da yaşanan volatilitenin benzer sürede etkisinin kaybolduğunu, ama Tether USDt’de ise daha uzun sürdüğünü göstermektedir. Bunun sebebi Tether USdt kripto paranın stabil coin olmasıdır. Bu bağlamda, yatırımcılar ve portföy yöneticilerinin, kararlarını şekillendirirken kripto paraların asimetrik özellikleri ile oynaklık seviyelerini göz önünde bulundurmaları oldukça önemlidir.
The aim of this research is to investigate the long-term relationships among the dollar exchange rate (TRY/USD), gold (GAU/USD), the Borsa Istanbul 100 Index (BIST 100) and the prices of Bitcoin (BTC/USD), Ethereum (ETH/USD), and Binance Coin (BNB/USD). Since the series contain structural breaks, Fourier unit root tests were used to model the structural breaks. As the method of this study, the relationships between variables in the long term were examined by using Fourier Shin (FSHIN) and Shin (1994) (SHIN) cointegration tests. The findings of this study showed that cryptocurrencies are cointegrated among themselves under structural breaks in the long term; investment instruments are cointegrated among themselves. In addition, as a result of this study, it was determined financial instruments and cryptocurrencies do not move in along over time under structural breaks.
This article examines the potential of blockchain technology to revolutionize the jewelry supply chain by enhancing trust, transparency, and efficiency. Utilizing Ethereum, we developed a blockchain network tailored to the industry's needs. Blockchain operates as a secure, immutable ledger, ensuring data integrity and transparency while preventing fraud and tampering due to its decentralized nature. Ethereum's key features, including nodes, addresses, and smart contracts, make it an ideal platform for this application. The system incorporates robust security measures, addressing vulnerabilities such as reentrancy attacks and unauthorized access. Performance tests on networks demonstrated the solution's viability, with Layer 2 optimizations reducing transaction costs. The system also uses IPFS (InterPlanetary File System) to store certificate templates in order to improve scalability and data accessibility. Six primary participants in the supply chain, from miners to customers, engage with the blockchain, ensuring full traceability and transparency. Certificates are dynamically generated by retrieving transaction hashes from the blockchain. The certificate template is stored on the InterPlanetary File System (IPFS), and when needed, the relevant data is populated into the template in real-time to produce the certificate. While challenges remain in terms of industry-wide adoption and regulatory compliance, the solution's potential to enhance transparency and efficiency positions it as a significant advancement for the jewelry supply chain within the Industry 4.0 framework.
Jahanzaib Alvi, Kehkashan Nizam, S. M. A. Jafri, Muhammad Rehan · 5 authors
This paper investigates the efficacy of deep learning models such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU) for cryptocurrency price prediction, examining their short-term and long-term forecasting accuracy for investor guidance and advancing AI in financial analysis. The study uses time series analysis with LSTM, CNN, and GRU models on daily cryptocurrency prices from Investing.com, preprocessing data before testing on Bitcoin, Ethereum Classic, Ethereum, Litecoin, Monero, and the other 37 cryptocurrencies. RMSE, MAE, and accuracy rates measure performance. Findings revealed that only six cryptocurrencies were selected for final analysis, including Bitcoin, Ethereum Classic, Ethereum, Litecoin, and Monero. Results indicate that the deep learning models, particularly the LSTM and GRU, can predict cryptocurrency prices with high accuracy, especially for short-term forecasts within a 7-day window. The CNN model demonstrates significant predictive power, suggesting its utility for immediate trading decisions. Across the models, short-term precision was remarkably high, while long-term predictions maintained a moderate level of accuracy. This study presents a comparative analysis of LSTM, GRU, and CNN models for forecasting cryptocurrency prices, emphasizing LSTM and GRU's ability to navigate price volatility and suggesting their use for real-time trading analysis. The study's historical data reliance curtails forecasting unforeseen market shifts. Future studies should include new variables like social sentiment and blockchain analytics and test real-time adaptive models to enhance predictive strength. Model validation in actual market conditions is recommended for practical application.
This research compares the effectiveness of the hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) method and the Convolutional Neural Network (CNN) method in predicting the Ethereum (ETH) exchange rate against the United States Dollar (USD). The research uses historical ETH/USD data from Yahoo Finance for the period 2017-2022. Evaluation of the two models was carried out using the performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy rate. The results showed that the CNN-LSTM hybrid model significantly outperformed the CNN model in predicting the ETH/USD exchange rate with a Test RMSE value of 94.67 compared to 129.02 for CNN, as well as an accuracy rate of 96.31% versus 94.89%. These findings contribute to the fintech literature by providing empirical evidence of the superiority of hybrid methods for high volatility cryptocurrency exchange rate prediction.
Nik Nor Muhammad Saifudin Nik Mohd Kamal, Safwah Afiqah, Sara Khadeja, Aliya Nasuha · 9 authors
Ethereum and Hyperledger are two popular and well-known block chain platforms which represent two kinds of application differentiation. Ethereum is a decentralized platform that also allows DApps to operate on it; many of the conditions for performing functions on Ethereum’s blockchain do not require permission to be granted, but smart contracts are available. On the other hand, the Hyperledger Fabric, an enterprise grade blockchain solution, provides the permission to access, update, and apply scalability, privatization, and mandatory access control mechanisms. Due to the decentralized nature and the capacity of performing smart contracts using Ethereum Virtual Machine (EVM), it has been used in a number of areas across the world in financial transactions and DApp. Hyperledger fabric, on the other hand, is pursuant to the permissioned network standards and is centred on providing the set of components that suffice the requirement of an enterprise thereby making it easier for the organization to build a blockchain, which is both highly scalable and security conscious. Some of the studies have researched on Ethereum as well as Hyperledger Fabric in a variety of contexts as depicted by the following: From these studies, it explains how blockchain has the potential in increasing volume in various areas while enhancing its characteristics such as, openness, origin and audibility. Analysing the concrete features of the Ethereum and Hyperledger Fabric platforms, it is almost obligatory for the companies interested into the implementation of the blockchain technology to understand the possibilities offered by one system and the drawbacks some complexity or singularity of the other. That is why, the features of each platform are distinctive and could be utilized for the development of business processes in specific spheres when designing problem-solving approaches.
Stephan Dübler, Federico Badaloni, Pedro Moreno-Sánchez, Clara Schneidewind
The heterogeneity of the blockchain landscape has motivated the design of blockchain protocols tailored to specific blockchains and applications that, hence, require custom security proofs. We observe that many blockchain protocols share common security and functionality goals, which can be captured by an atomic transfer graph (ATG) describing the structure of desired transfers. Based on this observation, we contribute a framework for generating secure-by-design protocols that realize these goals. The resulting protocols build upon Conditional Timelock Contracts (CTLCs), a novel minimal smart contract functionality that can be implemented in a large variety of cryptocurrencies with a restricted scripting language (e.g., Bitcoin), and payment channels. We show how ATGs, in addition to enabling novel applications, capture the security and functionality goals of existing applications, including many examples from payment channel networks and complex multi-party cross-currency swaps among Ethereum-style cryptocurrencies. Our framework is the first to provide generic and provably secure protocols for all these use cases while matching or improving the performance of existing use-case-specific protocols.
Open access
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cs.CR
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Like any digital certificate, Verifiable Credentials (VCs) require a way to revoke them in case of an error or key compromise. Existing solutions for VC revocation, most prominently Bitstring Status List, are not viable for many use cases because they may leak the issuer's activity, which in turn leaks internal business metrics. For instance, staff fluctuation through the revocation of employee IDs. We identify the protection of issuer activity as a key gap and propose a formal definition for a corresponding characteristic of a revocation mechanism. Then, we introduce CRSet, a non-interactive mechanism that trades some space efficiency to reach these privacy characteristics. For that, we provide a proof sketch. Issuers periodically encode revocation data and publish it via Ethereum blob-carrying transactions, ensuring secure and private availability. Relying Parties (RPs) can download it to perform revocation checks locally. Sticking to a non-interactive design also makes adoption easier because it requires no changes to wallet agents and exchange protocols. We also implement and empirically evaluate CRSet, finding its real-world behavior to match expectations. One Ethereum blob fits revocation data for about 170k VCs.
Taro Tsuchiya, Jin-Dong Dong, Kyle Soska, Nicolas Christin
In many blockchains, e.g., Ethereum, Binance Smart Chain (BSC), the primary representation used for wallet addresses is a hardly memorable 40-digit hexadecimal string. As a result, users often select addresses from their recent transaction history, which enables blockchain address poisoning. The adversary first generates lookalike addresses similar to one with which the victim has previously interacted, and then engages with the victim to ``poison'' their transaction history. The goal is to have the victim mistakenly send tokens to the lookalike address, as opposed to the intended recipient. Compared to contemporary studies, this paper provides four notable contributions. First, we develop a detection system and perform measurements over two years on both Ethereum and BSC. We identify 13~times more attack attempts than reported previously -- totaling 270M on-chain attacks targeting 17M victims. 6,633 incidents have caused at least 83.8M USD in losses, which makes blockchain address poisoning one of the largest cryptocurrency phishing schemes observed in the wild. Second, we analyze a few large attack entities using improved clustering techniques, and model attacker profitability and competition. Third, we reveal attack strategies -- targeted populations, success conditions (address similarity, timing), and cross-chain attacks. Fourth, we mathematically define and simulate the lookalike address generation process across various software- and hardware-based implementations, and identify a large-scale attacker group that appears to use GPUs. We also discuss defensive countermeasures.
David Cisar, Benjamin Schellinger, Jens-Christian Stoetzer, Nils Urbach · 7 authors
Abstract Corporate bonds are an attractive option for corporate financing. However, current bond markets face many challenges and inefficiencies, resulting in high transaction costs (TAC). In recent years, technological advancements like blockchain technology have enabled the possibility of reducing TAC in bond markets. Even though practice experiments with such solutions, academic literature lacks generic design knowledge under the TAC lens to design blockchain-based bonds. Thus, our research follows the design science research (DSR) paradigm to design and develop a bond prototype using the Ethereum blockchain protocol. Our results highlight the capability of blockchain-based bond markets to reduce TAC in the three dimensions of asset specificity, uncertainty, and transaction frequency. Further, our research provides design principles to contribute to both practice and the academic discourse on developing blockchain-based bond markets with reduced TAC.
Decentralized finance (DeFi) markets spread across Layer-1 (L1) and Layer-2 (L2) blockchains rely on arbitrage to keep prices aligned. Today most price gaps are closed against centralized exchanges (CEXes), whose deep liquidity and fast execution make them the primary venue for price discovery. As trading volume migrates on-chain, cross-chain arbitrage between decentralized exchanges (DEXes) will become the canonical mechanism for price alignment. Yet, despite its importance to DeFi-and the on-chain transparency making real activity tractable in a way CEX-to-DEX arbitrage is not-existing research remains confined to conceptual overviews and hypothetical opportunity analyses. We study cross-chain arbitrage with a profit-cost model and a year-long measurement. The model shows that opportunity frequency, bridging time, and token depreciation determine whether inventory- or bridge-based execution is more profitable. Empirically, we analyze one year of transactions (September 2023 - August 2024) across nine blockchains and identify 242,535 executed arbitrages totaling 868.64 million USD volume. Activity clusters on Ethereum-centric L1-L2 pairs, grows 5.5x over the study period, and surges-higher volume, more trades, lower fees-after the Dencun upgrade (March 13, 2024). Most trades use pre-positioned inventory (66.96%) and settle in 9s, whereas bridge-based arbitrages take 242s, underscoring the latency cost of today's bridges. Market concentration is high: the five largest addresses execute more than half of all trades, and one alone captures almost 40% of daily volume post-Dencun. We conclude that cross-chain arbitrage fosters vertical integration, centralizing sequencing infrastructure and economic power and thereby exacerbating censorship, liveness, and finality risks; decentralizing block building and lowering entry barriers are critical to countering these threats.
Execution Tickets are currently discussed as a next evolutionary step in Ethereum's block space allocation mechanism, separating consensus rewards from execution rewards and selling execution rights through a dedicated market. We present a theoretical framework identifying three core objectives for this mechanism - Decentralization, MEV capture, and Block Producer Incentive Compatibility - alongside practical metrics for evaluating each objective. To meet these goals, we explore seven key design parameters: ticket quantity, expiry, refundability, resalability, enhanced lookahead, pricing mechanism, and target ticket amount. We then evaluate four pricing mechanisms and construct six concrete mechanism designs from these parameters. To assess trade-offs in real-world conditions, we perform an agent-based simulation with over 300 runs. Our findings suggest that auction-driven formats, particularly second-price, excel in capturing significant MEV. The simulation also indicates that offering a secondary market can help alleviate centralization, since specialized ticket holders can enter and exit the market as needed. Non-expiring tickets show promise in reducing valuation risks, as ticket holders are not influenced by expiry-related discounting. Likewise, removing refundability simplifies the mechanism without notably impairing performance. Extended lookahead periods benefit price predictability and smoothness at a slight cost to price accuracy. Overall, this study provides a theoretical framework on the mechanism design space for Execution Tickets as well as a practical implementation of an agent-based simulation to test mechanism design choices. Further, it provides an exploratory evaluation of Execution Ticket mechanism designs, offering insights into optimal configurations that balance MEV capture, decentralization, and operational efficiency in Ethereum's block space allocation.