We introduce a micro-velocity framework for analysing the on-chain circulation of Lido’s liquid-staking tokens, stETH, and its wrapped ERC-20 form, wstETH. By reconstructing full transfer and share-based accounting histories, we compute address-level velocities and decompose them into behavioural components. Despite their growing importance, the micro-level monetary dynamics of LSTs remain largely unexplored. Our data reveal persistently high velocity for both tokens, reflecting intensive reuse within DeFi. Yet activity is highly concentrated: a small cohort of large addresses, likely institutional accounts, are responsible for most turnover, while the rest of the users remain largely passive. We also observe a gradual transition in user behavior, characterized by a shift toward wstETH, the non-rebasing variant of stETH. This shift appears to align with DeFi composability trends, as wstETH is more frequently deployed across protocols such as AAVE, Spark, Balancer, and SkyMoney. To make the study fully reproducible, we release (i) an open-source pipeline that indexes event logs and historical contract state, and (ii) two public datasets containing every Transfer and TransferShares record for stETH and wstETH through 2024-11-08. This is the first large-scale empirical characterisation of liquid-staking token circulation. Our approach offers a scalable template for monitoring staking asset flows and provides new, open-access resources to the research community.

In this paper, we analyze Shared Send Mixers (SSMs) within Cardano's Extended Unspent Transaction Output (EUTXO) model, presenting significant contributions to the understanding of UTXO-based blockchains. Firstly, we define the EUTXO SSM untangling problem and propose an algorithm to address it, along with providing an open-source implementation of the solution. Utilizing real transaction examples, we formulate and solve this problem in a systematic manner, shedding light on the intricacies of coin circulation within the Cardano blockchain. Through our analysis, we reveal insights into the usage of SSMs, including statistics on their frequency and effectiveness in untangling transactions. With this method, our findings show that 11% of transactions—as per the method—seem to involve SSMs, 83% of which enable unique untangling. Moreover, we discuss the potential application of our algorithm in enhancing address clustering results of transaction-level heuristics. Overall, our work contributes to a deeper understanding of transactional dynamics within UTXO cryptocurrencies, particularly within the context of Cardano's EUTXO model.

In this paper, we conduct an in-depth analysis of the Cardano blockchain, focusing on the Proof of Stake (PoS) protocol, Ouroboros, and its implications for entity wealth and stake delegation dynamics within the ecosystem. Utilizing a heuristic-based address clustering method, previously introduced in the literature, we aggregate Cardano addresses to identify distinct entities within the network. This approach allows us to examine the wealth distribution and staking dynamics at an entity level, offering a unique perspective on the Cardano network’s economic behaviors. Our study reveals insightful patterns in the emergence of new entities and their participation in stake delegation, alongside the rewards they accrue. We investigate the distribution of wealth among these entities, employing statistical measures such as the probability density and the Gini index for wealth, delegated stakes, and rewards over the blockchain’s history. This analysis provides a comprehensive view of the wealth distribution within the Cardano ecosystem. Moreover, the paper explores how entities of varying wealth levels engage in stake delegation to pools of different sizes and the resultant reward distribution. We also delve into the temporal evolution of the number of pools within the network, examining how pools of various sizes are rewarded and the extent of inequality in reward distribution among them. Through this analysis, the paper sheds light on the complex interplay between wealth distribution, stake delegation, and reward mechanisms in the Cardano ecosystem, offering valuable insights into the economic and staking landscape of one of the leading blockchain networks.

In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in the form of both arbitrage loops (e.g. the arbitrage path starts from token A and back to token A again in the end, $A \rightarrow B \rightarrow, \ldots, \rightarrow A$) and non-loops (e.g. the arbitrage path starts from token A and stops at a different token $N, A \rightarrow B \rightarrow, \ldots, \rightarrow N)$. The Moore-Bellman-Ford algorithm, often coupled with the “walk to the root” technique, is commonly employed for detecting arbitrage loops in the token graph of decentralized exchanges (DEXs) such as Uniswap. However, a limitation of this algorithm is its ability to recognize only a limited number of arbitrage loops in each run. Additionally, it cannot specify the starting token of the detected arbitrage loops, further constraining its effectiveness in certain scenarios. Another limitation of this algorithm is its incapacity to detect non-loop arbitrage paths between any specified pairs of tokens. In this paper, we develop a new method to solve these problems by combining the line graph and a modified Moore-Bellman-Ford algorithm (MMBF). This method can help to find more arbitrage loops by detecting at least one arbitrage loop starting from any specified tokens in the DEXs and can detect the nonloop arbitrage paths between any pair of tokens. Then, we applied our algorithm to Uniswap V2 and found more arbitrage loops and non-loops indeed compared with applying the Moore-Bellman-Ford (MBF) combined algorithm. The found arbitrage profit by our method in some arbitrage paths can be even as high as one million dollars, far larger than that found by the MBF combined algorithm. Finally, we statistically compare the distribution of arbitrage path lengths and the arbitrage profit detected by both our method and the MBF combined algorithm, and depict how potential arbitrage opportunities change with time by our method.

This paper presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the recent switch from Proof-of-Work (PoS) to Proof-of-Stake within Merge upgrade. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balancing attacks. Using a simple and flexible agent-based model, this study employs a time-continuous simulation algorithm to analyze the evolution of the blocktree and assess the impact of initial conditions on consensus quality. The model simulates validator node behavior and the information propagation throughout the peer-to-peer network of validators to analyze the resulting blockchain structure. Key variables in the model include the topology of the peer-to-peer network and average block and attestation latencies. Metrics to evaluate consensus quality are established, and means to observe the model's responsiveness to changes in parameters are provided. The simulations reveal a phase transition in which the system switches from a consensus state to a non-consensus state, with a theoretical justification presented for this observation.

Many variants of (delegated) Proof-of-Stake blockchains have been introduced since 2016, among which Cardano, Algorand, and Tezos have been more widely adopted. Each of these blockchains has attempted to address the scalability and consumption issues of Proof-of-Work (PoW) blockchains from different angles. However, the economic incentives of these blockchains have raised the concern about the decentralisation level of key players, such as validators, and stakeholders, in those “expected to be decentralised” networks. In this work, we conduct an analysis of Cardano's activity and wealth concentration. For the first time, we perform a clustering of the addresses that belong to the same wallet based on existing heuristics for UTXO-like blockchains. Moreover, an analysis of reward distribution on Cardano is presented. The analysis results of this work disclose objective insights into the Cardano blockchain, including the rewards distribution, stake balance distribution, and wealth concentration.

While there are many data collection and analysis tools for Ethereum - the largest smart contract blockchain by market capitalization, development of similar tools for other smart contract blockchains is lacking. Reasons for this are non-existent standards, changing specifications d ue t o rapid development, common usage of the off-chain storage, and lack of developers. One of such blockchains is Polkadot - a layer- zero blockchain featuring a single relay chain whose role is to secure smart contract transactions on multiple other parachains. In this paper we describe a data collection pipeline for Polkadot blockchain that we then use to perform an analysis of the bow-tie structure of its transfer network over time, with special emphasis on the role of nominators and validators in this structure. We find evidence that t he Polkadot ecosystem iss lowly maturing from a system dominated by nominators and validators, both of which require some technical skill as well as willingness to bond sufficient amount of funds, into a system increasingly populated by regular users using the financial services of Polkadot.

We consider the “block withholding attack” as introduced by Eyal, where mining pools may infiltrate others to decrease their revenues. However, when two mining pools attack each other and neither controls a strict majority, the so-called miner’s dilemma arises. Both pools are worse off than without an attack. Knowing this, pools may make implicit non-attack agreements. Having said this, the miner’s dilemma is known to emerge only if no pool controls the majority of the mining power. In this work, we allow for miner migration and show that the miner’s dilemma emerges even for pools whose mining power exceeds 50%. We construct a game, where two mining pools attack each other and use simulation analysis methods to analyze the evolution the pools’ mining power, infiltration preferences and revenue densities under the influence of different mining pool sizes and miner migration preferences. The results show that underlying game experiences a phase transition fueled by miners’ migration preference. Without migration, it is profitable for a large mining pool to attack the other pool. The higher the migration preference of the miners, the more the game transitions into the miner’s dilemma and attacking makes both pools worse off. In a second step, we introduce solo-mining into the system. Introducing solo-mining cannot prevent the miner’s dilemma, however, it improves the efficiency of the mining process as the infiltration preferences of the mining pools are lowered. Thus, solo-mining has a control effect on the miner’s dilemma by keeping the infiltration preference below a certain threshold.

In this paper we present a prototype system for easy testing and visualization of the Bitcoin consensus protocol. The system consists of a modified version of the Bitcoin Core client which is intended to run on Raspberry PI machines, modified so that the latency in the block production and propagation can be manually introduced, which simulates network communication issues in the real Bitcoin network. Built-in LCD displays on each machine visualize current state of their local blockchains which allows easy visual observation of the consensus in the network. As machines are running fully capable Bitcoin Core clients, as well as mining clients for production of new blocks in the Proof of Work (PoW) consensus protocol, it is possible to investigate different configuration scenarios and when these lead to the break of the consensus.

Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This article uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive design in permissioned blockchains and discuss its latest implications.

Large-scale distributed systems are becoming more widespread and, at the same time, their sizes grow day by day. In this type of systems, the adoption of blockchains is gaining particular traction for data storage in a secure and distributed manner. Nevertheless, design and testing of new protocols and features face the challenge of determining whether the proposed modifications would actually improve the system as expected. In the case of existing cryptocurrency systems, building an evaluation platform poses additional difficulties due to the resource-consuming nature of the associated processes. In this paper, we propose a novel methodology that relies on container-based network emulation to create scalable local testbeds in which Proof-of-Work-based blockchain systems can be evaluated. Using one of the mainstream Ethereum clients, we replaced the mining algorithm with a simulation model built upon the statistical characteristics of the mining process and instrumented the client to capture relevant network events. These events are used to create an offline reconstruction of the global view of the blockchain and all forking events, and to completely characterize the working conditions under arbitrary setups. Based on the versatility and scalability of our platform, we are able to test several network scenarios of increasing size in which we analyze the incidence of the target time in the generation of contradictory views of the blockchain (i.e. forks). We show that even using a limited testbed constituted by just commodity hardware, it is possible to use our platform to study the dynamics of blockchain-based systems up to hundreds of nodes.

Efficient bug triaging procedures are an important precondition for successful collaborative software engineering projects. Triaging bugs can become a laborious task particularly in open source software (OSS) projects with a large base of comparably inexperienced part-time contributors. In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away. Our classification is based on nine measures to quantify the social embeddedness of bug reporters in the collaboration network. We demonstrate its applicability in a case study, using a comprehensive data set of more than 700, 000 bug reports obtained from the Bugzilla installation of four major OSS communities, for a period of more than ten years. For those projects that exhibit the lowest fraction of valid bug reports, we find that the bug reporters' position in the collaboration network is a strong indicator for the quality of bug reports. Based on this finding, we develop an automated classification scheme that can easily be integrated into bug tracking platforms and analyze its performance in the considered OSS communities. A support vector machine (SVM) to identify valid bug reports based on the nine measures yields a precision of up to 90.3% with an associated recall of 38.9%. With this, we significantly improve the results obtained in previous case studies for an automated early identification of bugs that are eventually fixed. Furthermore, our study highlights the potential of using quantitative measures of social organization in collaborative software engineering. It also opens a broad perspective for the integration of social awareness in the design of support infrastructures.

We study the role of hierarchical structures in a simple model of collective consensus formation based on the bounded confidence model with continuous individual opinions. For the particular variation of this model considered in this paper, we assume that a bias towards an extreme opinion is introduced whenever two individuals interact and form a common decision. As a simple proxy for hierarchical social structures, we introduce a two-step decision making process in which in the second step groups of like-minded individuals are replaced by representatives once they have reached local consensus, and the representatives in turn form a collective decision in a downstream process. We find that the introduction of such a hierarchical decision making structure can improve consensus formation, in the sense that the eventual collective opinion is closer to the true average of individual opinions than without it. In particular, we numerically study how the size of groups of like-minded individuals being represented by delegate individuals affects the impact of the bias on the final population-wide consensus. These results are of interest for the design of organisational policies and the optimisation of hierarchical structures in the context of group decision making.

Out of all the complex phenomena displayed in the behaviour of animal groups, many are thought to be emergent properties of rather simple decisions at the individual level. Some of these phenomena may also be explained by random processes only. Here we investigate to what extent the interaction dynamics of a population of wild house mice (Mus domesticus) in their natural environment can be explained by a simple stochastic model. We first introduce the notion of perceptual landscape, a novel tool used here to describe the utilisation of space by the mouse colony based on the sampling of individuals in discrete locations. We then implement the behavioural assumptions of the perceptual landscape in a multi-agent simulation to verify their accuracy in the reproduction of observed social patterns. We find that many high-level features--with the exception of territoriality--of our behavioural dataset can be accounted for at the population level through the use of this simplified representation. Our findings underline the potential importance of random factors in the apparent complexity of the mice's social structure. These results resonate in the general context of adaptive behaviour versus elementary environmental interactions.