This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\mathcal{P}$ and $\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\mathcal{P}$ and $\mathcal{Q}$ to meet varying demands and market conditions.
Peer review is a laborious, yet essential, part of academic publishing with crucial impact on the scientific endeavor. The current lack of incentives and transparency harms the credibility of this process. Researchers are neither rewarded for superior nor penalized for bad reviews. Additionally, confidential reports cause a loss of insights and make the review process vulnerable to scientific misconduct. We propose a community-owned and -governed system that 1) remunerates reviewers for their efforts, 2) publishes the (anonymized) reports for scrutiny by the community, 3) tracks reputation of reviewers and 4) provides digital certificates. Automated by transparent smart-contract blockchain technology, the system aims to increase quality and speed of peer review while lowering the chance and impact of erroneous judgements.
Xintong Wu, Wanling Deng, Yutong Quan, Lin William Cong · 5 authors
Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.
This paper investigates the distribution of public school expenditures across U.S. school districts using a bayesian maximum entropy model. Covering the period 2000-2016, I explore how inter-jurisdictional competition and household choice influence spending patterns within the public education sector, providing a novel empirical treatment of the Tiebout hypothesis within a statistical equilibrium framework. The analysis reveals that these expenditures are characterized by sharply peaked and positively skewed distributions, suggesting significant socioeconomic stratification. Employing Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling, I fit these patterns into a statistical equilibrium model to elucidate the roles of competition, as well as household mobility and arbitrage in shaping the distribution of educational spending. The analysis reveals how the scale parameters associated with competition and household choice critically shape the equilibrium outcomes. The model and analysis offer a statistical basis for shaping policy measures intended to affect distributional outcomes in scenarios characterized by the decentralized provision of local public goods.
Decentralized Autonomous Organizations (DAOs), utilizing blockchain technology to enable collective governance, are a promising innovation. This research addresses the ongoing query in blockchain governance: How can DAOs optimize human cooperation? Focusing on the Network Nervous System (NNS), a comprehensive on-chain governance framework underpinned by the Internet Computer Protocol (ICP) and liquid democracy principles, we employ theoretical abstraction and simulations to evaluate its potential impact on cooperation and economic growth within DAOs. Our findings emphasize the significance of the NNS's staking mechanism, particularly the reward multiplier, in aligning individual short-term interests with the DAO's long-term prosperity. This study contributes to the understanding and effective design of blockchain-based governance systems.
A tool to improve the effectiveness and the efficiency of public spending is proposed here. In the 19th century banknotes had a serial number. However, in modern days the use of digital transactions that do not use physical currency has opened the possibility to digitally track almost each cent of the economy. In this article a serial number or tracking number for each cent, pence or any other monetary unit of the economy is proposed. Then, almost all cents can be tracked by recording the transactions in a public distributed ledger, rather than recording the amount of the transaction, the information recorded in the block of the transaction is the actual serial number or tracking number for each cent that changes ownership. In order to keep the privacy of the transaction, only generic identification of private companies and individuals are recorded along with generic information about the concept of transaction, the region and the date/time. A secondary public distributed ledger whose blocks are identified by a hash reference that is recorded in the bank statement available to the payer and the payee allows for checking the accuracy of the first public distributed ledger by comparing the transactions made in one day, one region and one type of concept. However, the transactions made or received by the government are recorded with a much higher level of detail in the first ledger and a higher level of disclosure in the second ledger. The result is a tool that is able to accurately track public spending, to keep privacy of individuals and companies and to make statistical analysis and experiments or real tests in the economy of a country. This tool has the potential to assist public policymakers in demonstrating the societal benefits resulting from their policies, thereby enabling more informed decision-making for future policy endeavours.
In this paper, we take a close look at a problem labeled maximal extractable value (MEV), which arises in a blockchain due to the ability of a block producer to manipulate the order of transactions within a block. Indeed, blockchains such as Ethereum have spent considerable resources addressing this issue and have redesigned the block production process to account for MEV. This paper provides an overview of the MEV problem and tracks how Ethereum has adapted to its presence. A vital aspect of the block building exercise is that it is a variant of the knapsack problem. Consequently, this paper highlights the role of designing auctions to fill a knapsack--or knapsack auctions--in alleviating the MEV problem. Overall, this paper presents a survey of the main issues and an accessible primer for researchers and students wishing to explore the economics of block building and MEV further.
Abhimanyu Nag, Samrat Gupta, Sudipan Sinha, Arka Datta
Decentralized Finance (DeFi) governance models have become increasingly complex due to the involvement of numerous independent agents, each with their own incentives and strategies. To effectively analyze these systems, we propose using Multi Agent Influence Diagrams (MAIDs) as a powerful tool for modeling and studying the strategic interactions within DeFi governance. MAIDs allow for a comprehensive representation of the decision-making processes of various agents, capturing the influence of their actions on one another and on the overall governance outcomes. In this paper, we study a simple governance game that approximates real governance protocols and compute the Nash equilibria using MAIDs. We further outline the structure of a MAID in MakerDAO.
Tao Yan, Shengnan Li, Benjamin Kraner, Luyao Zhang · 5 authors
Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for their contributions. However, the implementation of blockchain technology often diverges from its central tenet of decentralized consensus, presenting significant analytical challenges. Our study collects consensus reward data from the Ethereum Beacon chain and conducts a comprehensive analysis of reward distribution and evolution, categorizing them into attestation, proposer and sync committee rewards. To evaluate the degree of decentralization in PoS Ethereum, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI). Our comprehensive dataset is publicly available on Harvard Dataverse, and our analytical methodologies are accessible via GitHub, promoting open-access research. Additionally, we provide insights on utilizing our data for future investigations focused on assessing, augmenting, and refining the decentralization, security, and efficiency of blockchain systems.
In this paper, we studyhow to optimize existing non-fungible token (NFT) incentives. Upon exploring a large number of NFT-related standards and real-world projects, we uncover an unexpected finding: current NFT incentive mechanisms, often organized in an isolated and one-time-use fashion, tend to overlook their potential for scalable organizational structures. To address this, we propose, analyze, and implement a novelreference incentivemodel, inherently structured as a directed acyclic graph (DAG)-based NFT network. Leveraging the Stackelberg game framework and deep reinforcement learning (DRL), this model aims to maximize connections (or references) between NFTs, enabling isolated NFTs to expand their networks and accumulate rewards from subsequent or subscribed ones. Through both theoretical and practical analyses, we demonstrate the optimal utility of the proposed model.
Crypto donations now represent a significant fraction of charitable giving worldwide. Nonfungible token (NFT) charity fundraisers, which involve the sale of NFTs of artistic works with the proceeds donated to philanthropic causes, have emerged as a novel development in this space. A unique aspect of NFT charity fundraisers is the significant potential for donors to reap financial gains from the rising value of purchased NFTs. Questions may arise about donors' motivations in these charity fundraisers, potentially resulting in a negative social image. NFT charity fundraisers thus offer a unique opportunity to understand the economic consequences of a donor's social image. We investigate these effects in the context of a large NFT charity fundraiser. We identify the causal effect of purchasing an NFT within the charity fundraiser on a donor's later market outcomes by leveraging random variation in transaction processing times on the blockchain. Further, we demonstrate a clear pattern of heterogeneity based on an individual's decision to relist (versus hold) the purchased charity NFTs (a sign of perceived strategic generosity) and based on an individual's social exposure within the NFT marketplace. We show that charity-NFT 're-listers' experience significant penalties in the market regarding the prices they can command for their other NFTs, particularly among those who are more socially exposed. Finally, we report the results of a scenario-based online experiment, which again support our findings, highlighting that the re-listing a charity NFT for sale at a profit leads others to perceive their initial donation as strategic generosity and reduces those others' willingness to purchase NFTs from the donor. Our study underscores the growing importance of digital visibility and traceability, features that characterize crypto-philanthropy, and online philanthropy more broadly.
Decentralized exchanges are widely-used platforms for trading cryptoassets. The most fundamental and openly-accessible type of decentralized exchange is based on automated market makers (AMMs), where traders transact against asset reserves managed by smart contracts. These assets are provided by liquidity providers in exchange for a fee. Unlike traditional markets, AMMs pool liquidity from numerous retail participants, and prices are determined by publicly-known mathematical functions. Static analysis shows that small price changes in one of the assets result in losses for passive liquidity providers due to arbitrage trading. However, most existing literature focuses on static effects and does not adequately address the dynamic impact of fees from arbitrageurs over time. Therefore, we investigate the impermanent loss problem in a dynamic setting using Monte Carlo simulations. We contribute to the literature by demonstrating that arbitrage fees may constitute the primary revenue driver for passive liquidity providers. Arbitrageurs exert opposing effects on profitability: they impose rebalancing costs but generate fee revenue. For passive liquidity providers, rebalancing costs are independent of the number of arbitrage trades, while fees are directly proportional to trading volume. Consequently, increased arbitrage activity enhances the profitability of passive liquidity providers. Moreover, we show that this effect is amplified when trading barriers are low and arbitrage competition is intense. As a result, in the absence of barriers,arbitrageurs emerge as the disproportionately predominant source of fee revenue.
The measurement of the velocity of money is still a significant topic. In this paper, we proposed a method to calculate the velocity of money by combining the holding-time distribution and lifespan distribution. By derivation, the velocity of money equals the holding-time distribution's value at zero. When we have much holding-time data, this problem can be converted to a regression problem. After a numeric simulation, we find that the calculating accuracy is high even if we used only a small part of the holding time data, which implies a potential application in measuring the velocity of money in reality, such as digital money. We also tested the methods on Cardano and found that the method can also provide a reasonable estimation of velocity in some cases.
This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.
Can the general structure of a mortgage-backed security (MBS) contract be programmatically represented through the use of decentralized autonomous organizations (DAOs)? Such an approach could allow for the portfolio of loans to be managed by investors in a trustless and transparent way. The focus and scope of this paper is to explore the potential for applying the tools of modern fintech, such as asset tokenization, smart contracts, and DAOs, to reconstruct traditional structured products that have a greater degree of transparency and traceability. MBS investors face considerable value uncertainty as time increases between the actual occurrence (or non-occurrence) of cash flows and subsequent reporting. Given that an MBS is a financial contract, it should be expressible logically using the Algorithmic Contract Types Unified Standards (ACTUS). Since each underlying mortgage in an MBS derives its cash flows in a prescribed way over the life of the contract, implementation on a public blockchain could enable real-time ratings systems, improving market efficiency. We explore the potential for creating formal algorithmic designs of MBS-DAOs that incorporate individual mortgages, the underlying real estate assets (collateral), and any loan guarantees.
Sabrina Leo, Andrea Delle Foglie, Luca Barbaro, Edoardo Marangone · 6 authors
Credit Guarantee Schemes (CGSs) are crucial in mitigating SMEs' financial constraints. However, they are renownedly affected by critical shortcomings, such as a lack of financial sustainability and operational efficiency. Distributed Ledger Technologies (DLTs) have shown significant revolutionary influence in several sectors, including finance and banking, thanks to the full operational traceability they bring alongside verifiable computation. Nevertheless, the potential synergy between DLTs and CGSs has not been thoroughly investigated yet. This paper proposes a comprehensive framework to utilise DLTs, particularly blockchain technologies, in CGS processes to improve operational efficiency and effectiveness. To this end, we compare key architectural characteristics considering access level, governance structure, and consensus method, to examine their fit with CGS processes. We believe this study can guide policymakers and stakeholders, thereby stimulating further innovation in this promising field.
The burgeoning interest in both the circular economy and blockchain technology has spurred numerous proposed integrations. Despite this enthusiasm, empirical research examining the practical feasibility and critical assessment of blockchain's potential within the circular economy remains limited. This study engages with eleven distinguished blockchain experts to critically analyze the prospects of technology integration across various facets of the circular economy, aiming to predict potential outcomes. Utilizing the Delphi method, this research seeks to attain a consensus on the experts' visions and opinions. The findings suggest a nuanced perspective: while certain integrations in the circular economy may face challenges and are unlikely to succeed, others could prove effective in the long term, provided specific conditions are met. When appropriately designed Tokenomics are in place, and the necessary level of digitalization is achieved, blockchain technology can significantly incentivize circular economy practices. However, the complete disintermediation of circular practices through blockchain is viewed as less feasible, owing to its reliance on external data providers.
Nir Chemaya, Lin William Cong, Emma Jorgensen, Dingyue Liu · 5 authors
Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.
Non-fungible tokens (NFTs) are decentralized digital tokens to represent the unique ownership of items. Recently, NFTs have been gaining popularity and at the same time bringing up issues, such as scams, racism, and sexism. Decentralization, a key attribute of NFT, contributes to some of the issues that are easier to regulate under centralized schemes, which are intentionally left out of the NFT marketplace. In this work, we delved into this centralization-decentralization dilemma in the NFT space through mixed quantitative and qualitative methods. Centralization-decentralization dilemma is the dilemma caused by the conflict between the slogan of decentralization and the interests of stakeholders. We first analyzed over 30,000 NFT-related tweets to obtain a high-level understanding of stakeholders' concerns in the NFT space. We then interviewed 15 NFT stakeholders (both creators and collectors) to obtain their in-depth insights into these concerns and potential solutions. Our findings identify concerning issues among users: financial scams, counterfeit NFTs, hacking, and unethical NFTs. We further reflected on the centralization-decentralization dilemma drawing upon the perspectives of the stakeholders in the interviews. Finally, we gave some inferences to solve the centralization-decentralization dilemma in the NFT market and thought about the future of NFT and decentralization.
An important virtue of distributed ledger technologies is their acclaimed higher level of decentralisation compared to traditional financial systems. Empirical literature, however, suggests that many systems tend towards centralisation as well. This study expands the current literature by offering a first-time, data-driven analysis of the degree of decentralisation of the platform Hedera Hashgraph, a public permissioned distributed ledger technology, employing data directly fetched from a network node. The results show a considerably higher amount of released supply compared to the release schedule and a growing number of daily active accounts. Also, Hedera Hashgraph exhibits a high centralisation of wealth and a shrinking core that acts as an intermediary in transactions for the rest of the network. However, the Nakamoto index and Theil index point to recent progress towards a more decentralised network.
The rise of digital currency and the public ledger Block Chain has led to the development of a new type of electronic contract known as "smart contracts." For these contracts to be considered valid, they must adhere to traditional contract rules and be concluded without any impediments. Once written, encrypted, and signed, smart contracts are recorded in the Block Chain Ledger, providing transparent and secure record-keeping. Smart contracts offer several benefits, including their ability to execute automatically without requiring human intervention, their provision of public visibility of contract provisions on the Block Chain, their avoidance of financial crimes like Money Laundering, and their prevention of contract abuses. However, disputes arising from smart contracts still require human intervention, presenting unique challenges in enforcing these contracts, such as evidentiary issues, enforceability of waivers of defenses, and jurisdictional and choice-of-law considerations. Due to the novel nature of smart contracts, there are currently no standardized regulations that apply to them. Countries that have approved them have turned to customary law to legitimize their use. The Delphi method was used to identify critical success factors for applying blockchain transactions in a manufacturing company. Stepwise Weight Assessment Ratio Analysis (SWARA) was then utilized to determine the most influential factors. The proposed methodology was implemented, and results show that the most influential factors for the successful application of blockchain transactions as smart contracts in a manufacturing company are: turnover, the counter argument, vision, components for building, and system outcome quality. Conversely, connections with government entities and subcontractors, and the guarantee of quality have the least influence on successful implementation. These findings can contribute to the development of a legal framework for smart contracts in a manufacturing company.
Blockchain technology is leading a revolutionary transformation across diverse industries, with effective governance standing as a critical determinant for the success and sustainability of blockchain projects. Community forums, pivotal in engaging decentralized autonomous organizations (DAOs), wield a substantial impact on blockchain governance decisions. Concurrently, Natural Language Processing (NLP), particularly sentiment analysis, provides powerful insights from textual data. While prior research has explored the potential of NLP tools in social media sentiment analysis, a gap persists in understanding the sentiment landscape of blockchain governance communities. The evolving discourse and sentiment dynamics on the forums of top DAOs remain largely unknown. This paper delves deep into the evolving discourse and sentiment dynamics on the public forums of leading DeFi projects—Aave, Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancer—placing a primary focus on discussions related to governance issues. Despite differing activity patterns, participants across these decentralized communities consistently express positive sentiments in their Discord discussions, indicating optimism towards governance decisions. Additionally, our research suggests a potential interplay between discussion intensity and sentiment dynamics, indicating that higher discussion volumes may contribute to more stable and positive emotions. The insights gained from this study are valuable for decision-makers in blockchain governance, underscoring the pivotal role of sentiment analysis in interpreting community emotions and its evolving impact on the landscape of blockchain governance. This research significantly contributes to the interdisciplinary exploration of the intersection of blockchain and society, with a specific emphasis on the decentralized blockchain governance ecosystem. We provide our data and code for replicability as open access on GitHub.
This paper investigates the "Exploitation Business" model, which capitalizes on information asymmetry to exploit vulnerable populations. It focuses on businesses targeting non-experts or fraudsters who capitalize on information asymmetry to sell their products or services to desperate individuals. This phenomenon, also described as "profit-making activities based on informational exploitation," thrives on individuals' limited access to information, lack of expertise, and Fear of Missing Out (FOMO). The recent advancement of social media and the rising trend of fandom business have accelerated the proliferation of such exploitation business models. Discussions on the empowerment and exploitation of fans in the digital media era present a restructuring of relationships between fans and media creators, highlighting the necessity of not overlooking the exploitation of fans' free labor. This paper analyzes the various facets and impacts of exploitation business models, enriched by real-world examples from sectors like cryptocurrency and GenAI, thereby discussing their social, economic, and ethical implications. Moreover, through theoretical backgrounds and research, it explores similar themes like existing exploitation theories, commercial exploitation, and financial exploitation to gain a deeper understanding of the "Exploitation Business" subject.
This research paper presents a thorough economic analysis of Bitcoin and its impact. We delve into fundamental principles, and technological evolution into a prominent decentralized digital currency. Analysing Bitcoin's economic dynamics, we explore aspects such as transaction volume, market capitalization, mining activities, and macro trends. Moreover, we investigate Bitcoin's role in economy ecosystem, considering its implications on traditional financial systems, monetary policies, and financial inclusivity. We utilize statistical and analytical tools to assess equilibrium , market behaviour, and economic . Insights from this analysis provide a comprehensive understanding of Bitcoin's economic significance and its transformative potential in shaping the future of global finance. This research contributes to informed decision-making for individuals, institutions, and policymakers navigating the evolving landscape of decentralized finance.