Questions related to Bitcoin and other Informational Money

A collection of questions about Bitcoin and its hypothetical relatives Bitguilder and Bitpenny is formulated. These questions concern technical issues about protocols, security issues, issues about the formalizations of informational monies in various contexts, and issues about forms of use and misuse. Some questions are formulated in the more general setting of informational monies and near-monies. We also formulate questions about legal, psychological, and ethical aspects of informational money. Finally we formulate a number of questions concerning the economical merits of and outlooks for Bitcoin.Comment: 31 pages. In v2 the section on patterns for use and misuse has been improved and expanded with so-called contaminations. Other small improvements were made and 13 additional references have been include

Validation of Decentralised Smart Contracts Through Game Theory and Formal Methods

Decentralised smart contracts represent the next step in the development of protocols that support the interaction of independent players without the presence of a coercing authority. Based on protocols a` la BitCoin for digital currencies, smart contracts are believed to be a potentially enabling technology for a wealth of future applications. The validation of such an early developing technology is as necessary as it is complex. In this paper we combine game theory and formal models to tackle the new challenges posed by the validation of such systems

Exploring auction based energy trade with the support of MAS and blockchain technology

This document describes a simulation of the local energy market with support of multi-agent approach and blockchain technology. The investigated points include blockchain technology and its applications, Ethereum platform and smart contracts as a tool for storing data of operations and creating assets, multi-agent approach to model the local energy market. The document explores building a solution for proposed problem with blockchain technology, agent interactions on the simulated market and auction models, that provide sustainability and profit for the local energy market overall

k-Root-n: An Efficient Algorithm for Avoiding Short Term Double-Spending Alongside Distributed Ledger Technologies such as Blockchain

Blockchains such as the bitcoin blockchain depend on reaching a global consensus on the distributed ledger; therefore, they suffer from well-known scalability problems. This paper proposes an algorithm that avoids double-spending in the short term with just O(√n) messages instead of O(n); each node receiving money off-chain performs the due diligence of consulting k√n random nodes to check if any of them is aware of double-spending. Two nodes receiving double-spent money will in this way consult at least one common node with very high probability, because of the ‘birthday paradox’, and any common honest node consulted will detect the fraud. Since the velocity of money in the real world has coins circulating through at most a few wallets per day, the size of the due diligence communication is small in the short term. This ‘k-root-n’ algorithm is suitable for an environment with synchronous or asynchronous (but with fairly low latency) communication and with Byzantine faults. The presented k-root-n algorithm should be practical to avoid double-spending with arbitrarily high probability, while feasibly coping with the throughput of all world commerce. It is resistant to Sybil attacks even beyond 50% of nodes. In the long term, the k-root-n algorithm is less efficient. Therefore, it should preferably be used as a complement, and not a replacement, to a global distributed ledger technology.</jats:p

Models and applications for the Bitcoin ecosystem

Cryptocurrencies are widely known and used principally as a means of investment and payment by more and more users outside the restricted circle of technologists and computer scientists. However, like fiat money, they can also be used as a means for illegal activities, exploiting their pseudo-anonymity and easiness/speed in moving capitals. This thesis aims to provide a suite of tools and models to better analyze and understand several aspect of the Bitcoin blockchain. In particular, we developed a visual tool that highlights transaction islands, i.e., the sub-graphs disconnected from the super-graph, which represents the whole blockchain. We also show the distributions of Bitcoin transactions types and define new classes of nonstandard transactions. We analyze the addresses reuse in Bitcoin, showing that it corresponds to malicious activities in the Bitcoin ecosystem. Then we investigate whether solids or weak forms of arbitrage strategies are possible by trading across different Bitcoin Exchanges. We found that Bitcoin price/exchange rate is influenced by future and past events. Finally, we present a Stochastic Model to quantitative analyze different consensus protocols. In particular, the probabilistic analysis of the Bitcoin model highlights how forks happen and how they depend on specific parameters of the protocol

Contracts Ex Machina

Smart contracts are self-executing digital transactions using decentralized cryptographic mechanisms for enforcement. They were theorized more than twenty years ago, but the recent development of Bitcoin and blockchain technologies has rekindled excitement about their potential among technologists and industry. Startup companies and major enterprises alike are now developing smart contract solutions for an array of markets, purporting to offer a digital bypass around traditional contract law. For legal scholars, smart contracts pose a significant question: Do smart contracts offer a superior solution to the problems that contract law addresses? In this article, we aim to understand both the potential and the limitations of smart contracts. We conclude that smart contracts offer novel possibilities, may significantly alter the commercial world, and will demand new legal responses. But smart contracts will not displace contract law. Understanding why not brings into focus the essential role of contract law as a remedial institution. In this way, smart contracts actually illuminate the role of contract law more than they obviate it

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