On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, https://doi.org/10.1145/3528669.The transaction-rate bottleneck built into popular proof-of-work-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this paper, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions

Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems

This dissertation investigates the strategic integration of Proof-of-Work(PoW)-based blockchains and ML models to improve transaction inclusion, and consequently molding transaction fees, for clients using cryptocurrencies such as Bitcoin. The research begins with an in-depth exploration of the Bitcoin fee market, focusing on the interdependence between users and miners, and the emergence of a fee market in PoW-based blockchains. Our observations are used to formalize a transaction inclusion pattern. To support our research, we developed the Blockchain Analytics System (BAS) to acquire, store, and pre-process a local dataset of the Bitcoin blockchain. BAS employs various methods for data acquisition, including web scraping, web browser APIs, and direct access to the blockchain using Bitcoin Core software. We utilize time-series data analysis as a tool for predicting future trends, and transactions are sampled on a monthly basis with a fixed interval, incorporating a notion of relative time represented by block-creation epochs. We create a comprehensive model for transaction inclusion in a PoW-based blockchain system, with a focus on factors of revenue and fairness. Revenue serves as an incentive for miners to participate in the network and validate transactions, while fairness ensures equal opportunity for all users to have their transactions included upon paying an adequate fee value. The ML architecture used for prediction consists of three critical stages: the ingestion engine, the pre-processing stage, and the ML model. The ingestion engine processes and transforms raw data obtained from the blockchain, while the pre-processing phase transforms the data further into a suitable form for analysis, including feature extraction and additional data processing to generate a complete dataset. Our ML model showcases its effectiveness in predicting transaction inclusion, with an accuracy of more than 90%. Such a model enables users to save at least 10% on transaction fees while maintaining a likelihood of inclusion above 80%. Furthermore, adopting such model based on fairness and revenue, demonstrates that miners' average loss is never higher than 1.3%. Our research proves the efficacy of a formal transaction inclusion model and ML prototype in predicting transaction inclusion. The insights gained from our study shed light on the underlying mechanisms governing miners' decisions, improving the overall user experience, and enhancing the trust and reliability of cryptocurrencies. Consequently, this enables Bitcoin users to better select suitable fees and predict transaction inclusion with notable precision, contributing to the continued growth and adoption of cryptocurrencies

To Broadcast or Not to Broadcast: Decision-Making Strategies for Mining Empty Blocks

Resource efficiency in blockchain systems remains a pivotal concern in their design. While Ethereum often experiences network congestion, leading to rewarding opportunities for miners through transaction inclusions, a significant amount of block space remains underutilized. Remarkably, instances of entirely unutilized blocks contribute to resource wastage within the Ethereum ecosystem. This study delves into the incentives driving miners to produce empty blocks. We ascertain that the immediate rewards of mining empty blocks often lead miners to forego potential benefits from transaction inclusions. Moreover, our investigation reveals a marked reduction in empty blocks after the Ethereum\u27s Merge, highlighting that the Proof-of-Stake (PoS) consensus mechanism enhances block space efficiency in the blockchain sphere

Evaluating Blockchain Success: Integrating Organizational Decentralization with the DeLone and McLean IS Success Model

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementBlockchain technology is a distributed ledger without an intermediate where delivers decentralized consensus. The tremendous potential of this technology including anonymity, persistency, auditability, and traceability along with decentralization caused blockchain to receive attention globally. This study aims to identify the role of decentralization in blockchain success at firms by proposing a theoretical model based on the theory of success in information systems. The research model was empirically tested using 193 responses over an online survey questionnaire. The result reveals that service quality, system quality, and information quality were explained by decentralization. Likewise, decentralization and user’s satisfaction are an important criterion for the Net impact of blockchain success. Furthermore, this study explores the positive influence of decentralization as a moderator between the relationship of the user’s satisfaction and net impact. The findings have theoretical and practical implications for academics and managers

Multi-Agent Systems for Computational Economics and Finance

In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artificial intelligence, and economic theory. In particular, we focus on the design and analysis of mechanisms for systems involving multiple strategic agents, both from a theoretical and an applied perspective. We present an overview of our group’s activities, as well as its members, and then discuss in detail past, present, and future work in multi-agent systems