A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.

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Empirical Analysis of EIP-1559: Transaction Fees, Waiting Times, and Consensus Security

Blockchain empowers a decentralized economy by enabling distributed trust in a peer-to-peer network. Decentralization is a core property of blockchain that not only ensures system security but also enables the democratization of systems and processes through the promise of a distributed peer-to-peer network. However, surprisingly, a widely accepted definition or measurement of decentralization is still lacking. We explore an SoK on blockchain decentralization focusing on measurement, quantification, and methodology. First, we establish a taxonomy for analyzing blockchain decentralization in the five facets of consensus, network, governance, wealth, and transaction by qualitative analysis of existing research. Second, we propose an index that measures and quantifies the decentralization level of blockchain across different facets by transforming Shannon entropy. We verify the explainability of the index via comparative static simulations. We also define and discuss alternative indices, including the Gini Coefficient, Nakamoto Coefficient, and Herfindahl-Hirschman Index (HHI). We provide open-source Python code for calculating the decentralization index by comparing various alternatives on GitHub. Third, by applying our proposed index to empirical research on top DeFi applications, we demonstrate how the three scientific methods of descriptive, predictive, and causal inferences are useful in studying blockchain decentralization. Finally, we identify future research directions: 1) to explore the interactions between different facets of blockchain decentralization, 2) to design blockchain mechanisms that achieve sustainable decentralization, and 3) to study the interplay of decentralization levels and the ends of security, privacy, and efficiency. We also raise challenges in addressing the controversies in blockchain decentralization.

SoK: Blockchain Decentralization

In this paper, we use the methods of networks science to analyse the transaction networks of tokens running on the Ethereum blockchain. We start with a deep dive on four of them: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We study two types of blockchain addresses, smart contracts (SC), which run code, and externally owned accounts (EOA), run by human users, or off-chain code, with the corresponding private keys. We use preferential attachment and network dismantling strategies to evaluate their importance for the network structure. Subsequently, we expand our view to all ERC-20 tokens issued on the Ethereum network. We first study multilayered networks composed of Ether (ETH) and individual tokens using a dismantling approach to assess how the deconstruction starting from one network affects the other. Finally, we analyse the Ether network and Ethereum-based token networks to find similarities between sets of high-degree nodes. For this purpose, we use both the traditional Jaccard Index and a new metric that we introduce, the Ordered Jaccard Index (OJI), which considers the order of the elements in the two sets that are compared. Our findings suggest that smart contracts and exchange-related addresses play a structural role in transaction networks both in DeFi and Ethereum. The presence in the network of nodes associated to addresses of smart contracts and exchanges is positively correlated with the success of the token network measured in terms of network size and market capitalisation. These nodes play a fundamental role in the centralisation of the supposedly decentralised finance (DeFi) ecosystem: without them, their networks would quickly collapse.

/pdf/the-structural-role-of-smart-contracts-and-exchanges-in-the-2hxftm0w.pdf

The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets

The behaviour of Bitcoin owners is reﬂected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reﬂected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose conﬁgurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to ﬁnd that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.

Disorder Unleashes Panic in Bitcoin Dynamics

A monetary model is established to introduce that bitcoin does have the characteristics of a price rise when the economic situation is terrible under high risk aversion. At this time, Bitcoin has the property of a safe-haven asset, and when economic conditions are good and risk aversion is low, Bitcoin has a pro-cyclical nature. At this time, Bitcoin has a stronger property as a medium of exchange. To show the movement law of the bitcoin price, we make a logarithmic linearization of the model and simulation, and the result is consistent with the theoretical analysis. To better understand the role of bitcoin in the real economy, several standard portfolio models are used to measure the similarities and differences between gold and bitcoin in an investment portfolio. After the outbreak of COVID-19, bitcoin has shown stronger safe-haven asset properties.

https://www.mdpi.com/2075-1680/11/8/415/pdf?version=1661335569

Bitcoin as a Safe-Haven Asset and a Medium of Exchange

More than ten years ago the blockchain was acclaimed as the solution to overcome centralised trusted third parties for online payments. Through the years the crypto-movement changed and evolved, although decentralisation remained the core ideology and the necessary feature every new crypto-project should provide. In this paper we study the concept of centralisation in cryptocurrencies using a wide array of methodologies from the complex systems literature, on a comparative collection of blockchains, in order to define the many different levels a blockchain system may display (de-)centralisation and to question whether the present state of cryptocurrencies is, in a technological and economical sense, actually decentralised.

Francesco Maria De Collibus

Papers

The Evolution Of Centralisation on Cryptocurrency Platforms

The Structural Role of Smart Contracts and Exchanges in the Centralisation of Ethereum-Based Cryptoassets