Distributed ledger technology (DLT) has the potential to transform the current centralized architecture of traditional payment systems. In this paper, applying a multivocal literature review and a thematic analysis to analyze the grey and academic literature, we identify and categorize the use cases related to DLT-based applications in central bank payment systems functions. We identified six DLT-based use cases as real-time gross settlement systems, cross-border settlements arrangements, infrastructure for central bank digital currencies, information registry and data sharing, and digital know your customer/anti-money laundering applications. Furthermore, we explore the motivation behind adopting DLT and the role of central banks for applicable use cases. Our findings show that the primary and recurrent theme on motivation across the different use cases is to improve efficiency compared to conventional systems. The range of DLT platforms and consensus algorithms that we identified indicates that there is no universal solution that can be applied in all cases. Finally, we provide insights into the current state of research in this niche area. We observe that the practical implementation of the use cases is mostly discussed in the grey literature, which emphasizes its importance in providing complementary perspectives on the practical and theoretical aspects of the use cases.
Giacomo Vella, Valeria Portale, Daniel Trabucchi, Luca Gastaldi
This study investigates how blockchain-based platforms redefine traditional innovation platform models by analyzing Ethereum as a paradigmatic case.While classical platforms are characterized by centralized governance and selective openness, Ethereum introduces a decentralized architecture that structurally embeds openness, composability, and permissionless participation into its protocol.Drawing on a qualitative single-case study based on over 100 secondary sources, this research explores three dimensions: governance and orchestration, complementor engagement, and architectural modularity.Findings highlight that Ethereum operates through multi-actor orchestration, where governance is distributed among core developers, the Ethereum Foundation, decentralized autonomous organizations, and community contributors.Complementors engage directly with the platform, leveraging smart contracts and token-based incentives to build interoperable applications without the need for approval from a central authority.Moreover, Ethereum extends composability beyond core-periphery dynamics, enabling peer-level recombination of complements through public, reusable smart contracts.A comparative analysis with traditional platforms and open-source ecosystems positions Ethereum as a novel archetype of innovation platform, blending open-source governance with blockchain-enabled modularity and decentralized economic coordination.The study advances platform theory by proposing new conceptualizations of openness, modularity, and governance in decentralized digital ecosystems.
Die KomplexitĂ€t moderner Lieferketten erfordert die Koordination und Kommunikation einer Vielzahl an Wertschöpfungspartnern und benötigt somit resiliente und transparente Systeme, um Störungen effektiv zu bewĂ€ltigen und eine verlĂ€ssliche Versorgung sicherzustellen. Diese Notwendigkeit wird angesichts von unvorhergesehenen Störungen wie Pandemien, geopolitischen Konflikten wie Kriegen oder ökoligischer Rahmenbedingungen noch verstĂ€rkt und erfordert widerstandsfĂ€hige, transparente und nachhaltige Wertschöpfungsnetzwerke, um sowohl regulatorische Anforderungen als auch gesellschaftliche Erwartungen zu erfĂŒllen. Vor diesem Hintergrund bietet die Distributed-Ledger-Technologie (DLT) durch Eigenschaften wie UnverĂ€nderlichkeit, Transparenz und Automatisierung ĂŒber Smart Contracts das Potential, Kommunikationsineffizienzen, mangelnde Nachvollziehbarkeit sowie Defizite bei der Compliance-PrĂŒfung in SC ĂŒberwinden zu können. Da es sich bei DLTs jedoch um eine Technologie handelt, die erst im Jahr 2008 mit der Bitcoin-Blockchain aufkam, ist das vorhandene Wissen zum DLT-Einsatz in Lieferketten in Forschung und Praxis zum aktuellen Zeitpunkt begrenzt. Die vorliegende Dissertation adressiert dies und leitet gestaltungsorientiertes Wissen fĂŒr den Einsatz von DLTs in Lieferketten her. Eingangs zeigt eine Literatur-Analyse als Teil der Dissertation, dass die Forschung bislang eine Fokussierung auf sektorspezifische Prototypen und technische Konzepte aufweist, Dies fĂŒhrt dazu, dass generalisierbare Erkenntnisse, praxisorientierte ImplementierungsÂŹstrategien und systematische Governance-Ăberlegungen weitestgehend fehlen. Aufbauend auf diesen Erkenntnissen adressieren fĂŒnf komplementĂ€re, im Rahmen der Dissertation durchgefĂŒhrte ForschungsbeitrĂ€ge diese identifizierten ForschungslĂŒcken. HierfĂŒr werden zuerst sinnvolle Einsatzgebiete fĂŒr eine DLT im SC-Kontext erfasst und anschlieĂend Erfolgsfaktoren sowie Barrieren fĂŒr die technologische sowie organisatorische EinfĂŒhrung formuliert. Diese Erkenntnisse werden u. a. aus der Literatur sowie gescheiterten Praxisprojekten wie TradeLens oder Serai erhoben. AbschlieĂend erfolgen gestaltungsorientierte Studien, die einen Prototypen fĂŒr Zollprozesse sowie eine Interviewstudie umfassen und die identifizierten Erkenntnisse der Dissertation in 19 Gestaltungsempfehlungen zur EinfĂŒhrung von DLTs in Lieferketten ĂŒberfĂŒhren. Diese umfassen hierbei nicht nur die technische Implementierung, sondern auch Gestaltungswissen fĂŒr das Betriebsmodell einer DLT, da diese von mehreren Lieferkettenparteien gemeinsam betrieben und genutzt werden muss. Die Ergebnisse zeigen somit, dass erfolgreiche DLT-Anwendungen nicht allein von technischer Machbarkeit, sondern vor allem von Governance-Strukturen, Anreizmechanismen und regulatorischer Einbettung abhĂ€ngen. Damit liefert die Arbeit sowohl theoretische BeitrĂ€ge zur Weiterentwicklung bestehender Rahmenwerke als auch praxisorientierte Gestaltungsempfehlungen fĂŒr die schrittweise EinfĂŒhrung dezentraler Systemarchitekturen in Lieferketten. Die Dissertation schlieĂt mit einem Ausblick auf zukĂŒnftige Forschung, insbesondere der Integration von DLT mit Internet-of-Things- und Electronic-Data-Interchange-Systemen.
Previous research has investigated how certain strategies can influence people's decisions in simple, everyday choices, such as selecting a loaf of bread or purchasing a book online. The objective of this study was to further the understanding on choice architecture elements of default opt-ins and social proof tags, which are interface elements that signal the use of a product by other individuals. We analyzed their effect in an e-commerce context, specifically exploring high-stake economic decision-making that is characterized by high economic cost (financial or opportunity cost) and high decision importance. We achieved this through investigating the effect of default opt-ins on test ride bookings for an automobile, as well as the influence of social proof tags on click-through rates and âbookingsâ, which involve a payment of ~5% of the vehicle price made by customers to reserve a place for them on the purchase waitlist. We hypothesized that a default opt-in in the test ride form would have a significant positive influence on the conversion rate. Our findings supported our hypothesis. We further hypothesized that the addition of social proof tags on choices within a set of alternatives would result in a significant increase in the consideration of those choices for purchase. Though the results were inconclusive, our comparative analysis showed the potential importance of both the default options and social proof tags on consumer decision-making, creating an opportunity for further research into the effective use of the combination of defaults and social proof tags in an e-commerce context.
Ayush Agnihotri, Ashutosh Pandey, Rajat Verma, Namrata Dhanda
This abstract delves into the transformative role of smart contracts and decentralized applications in Video DRM (Digital Rights Management). As digital content protection undergoes a paradigm shift, the integration of Video DRM, smart contracts, and DApps presents a pioneering approach to content management within the confluence of cryptography, blockchain, and artificial intelligence. Smart contracts, functioning as self-executing agreements, merge seamlessly with Video DRM principles, offering a secure and transparent means to encode content access rights, licensing terms, and royalty payments. DApps, residing on decentralized blockchain networks, harness this synergy to create tamper-resistant ecosystems for content distribution and digital rights management. This abstract highlight the potential of Video DRM, facilitated by smart contracts and DApps, to empower content creators, enabling them to securely manage their intellectual property within a trustless, automated, and auditable environment. The interplay between these technologies redefines content protection in the digital landscape, guarding the rights of creators and distributors alike.
Decentralized Finance (DeFi) is rapidly growing, promising to make financial services more open and efficient. The prospect of DeFi mass adoption has attracted attention in recent economic research. This literature review examines how users respond to DeFi, evaluating whether large-scale adoption is feasible in the current environment. It focuses on behavioral biases â herding, investor attention, fear of missing out, momentum, and sentiment â that contribute to market fragility and inefficiency. The findings indicate that while DeFi offers promising solutions to some challenges of traditional finance, its current state remains unprepared for mass adoption. However, as research in this area is still emerging and blockchain technology continues to advance, DeFi retains significant potential for future development.
Decentralized finance (DeFi) is the concept of building financial infrastructures without relying on centralized intermediaries. A notable development in DeFi is the creation of decentralized exchanges (DEXs), which operate as smart contracts on a blockchain. Due to the high cost of on-chain operations, automated market makers (AMMs) such as Uniswap v3 have emerged as the prevailing model of liquidity provision on DEXs. Two closely related research questions arise in the DeFi space: (1) What are the optimal strategies of liquidity providers given an AMM design such as Uniswap v3? (2) How should the design of AMMs be optimized to achieve objectives such as profit maximization? This thesis addresses these two central research questions using computational methods, in particular, through differentiable optimization. Chapters 2 and 3 study the optimal strategies of liquidity providers (LPs) in Uniswap v3. In both chapters' formulations, the expected utility of an LP is differentiable with respect to its liquidity allocation under any exogenous price sequence, enabling differentiable optimization of LP strategies. With the formulation of a convex stochastic optimization problem that can be solved in a differentiable manner, Chapter 2 explores optimal static LP strategies in economic settings with varying factors such as an LP's belief about price dynamics, risk aversion, and for different specifications of the Uniswap v3 liquidity pool. Understanding LP strategies also leads to insights into the design of Uniswap v3 liquidity pools. Under a similar optimization framework, Chapter 3 extends from static LP strategies to dynamic LP strategies, specifically LP strategies that reallocate liquidity whenever the price movement reaches a certain threshold. These proposed dynamic strategiesâparticularly context-dependent variants modeled by a neural network, which adapt the shape of liquidity allocation to contextual information such as price and moving average of non-arbitrage trade volume at the time of reallocationâare shown to lead to significant gains compared to static LP strategies. Taking a broader perspective on AMM design, Chapter 4 optimizes market-making mechanisms for a single trade in settings with multiple traded goods, seeking market maker profit maximization under adverse selection. Conjectures of optimal mechanisms are generated using tools of differentiable economics, which uses differentiable optimization for economic design. To prove the optimality of proposed mechanisms, a duality theorem is established between the market-making mechanism design problem and an optimal transport problem. This approach of combining differentiable economics with theoretical analysis is used to develop a parameterized class of optimal market-making mechanisms. These results also establish that, in some cases, the optimal market maker across multiple goods must use complex bundling. Additional conjectures about the structure of optimal mechanisms are presented, and an empirical optimality bound is established for some conjectures by approximately solving the dual with linear programming. The second part of this thesis studies transfer learning of the Gaussian process (GP) prior in Bayesian optimization (BO), a widely used black-box function optimization method. Previous GP-based transfer learning methods for BO are limited to utilizing historical data collected from black-box functions with the same domain as the new black-box function to be optimized. The proposed method, model pre-training on heterogeneous domains (MPHD), employs a neural network that maps from domain-specific contextual information to specifications of hierarchical GPs for a given domain. As a result, MPHD is able to transfer knowledge across heterogeneous domains such as hyperparameter-tuning for different machine learning models. It is shown through theoretical analysis and empirical results that MPHD is a practical transfer learning method for BO, with demonstrations of competitive performance on challenging real-world hyperparameter-tuning tasks.
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Opportunism caused by conflicts of interest significantly impedes collaboration within decentralized autonomous organizations (DAOs). To address this issue, we develop a model to illustrate whether the unique aspect of tokensâthe interest alignment effectâcan mitigate collective action problems in DAOs. This model integrates the ârational cheaterâ framework with the interest alignment effects introduced by token rewards. It also examines the interaction between token rewards and membersâ social motivation, particularly focusing on the dynamics of crowding-out effects. Our findings highlight that the constraints on opportunism in DAOs primarily include the interest alignment costs associated with token holdings and the psychological costs tied to membersâ social motivation. These costs complement each other in reducing opportunism in DAOs. Additionally, the effect of increasing token rewards on opportunism depends on the balance between the current token value and how opportunistic actions potentially affect the organizationâs value.
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Misalignment between individual preferences and organizational decisions can lead to demotivation, absence, and turnover. This is particularly challenging for decentralized organizations that rely on management tools based on hierarchical authority to limit these negative effects of misalignment. We theorize that (decentralized) organizations can use decision-making structures to moderate the negative impact of misalignment. Using a dataset of 140 decentralized autonomous organizations, we find that implementing weighted decision-making structures, where members can express preference intensity, significantly alleviates this negative impact. Likewise, quadratic decision-making structures, which limits the influence of members with high voting power, reduces the adverse effects of misalignment.
This paper explores the intersection of decentralized governance, blockchain technology, and the digital commons through the lens of Elinor Ostromâs principles. It examines how Decentralized Autonomous Organizations (DAOs) and tokenization models present both opportunities and risks for managing digital resources in transparent, communityâdriven ways. The authors assess how tokenâbased, reputationâbased, and hybrid governance mechanismsâranging from quadratic voting to Soulbound Tokensâcan enhance democratic participation and accountability within blockchain ecosystems, while also recognizing their susceptibility to plutocracy, voter apathy, and collusion. Drawing on case studies such as MakerDAO, MolochDAO, Commons Stack, and Aragon, the paper critically analyzes realâworld implementations of decentralized governance and the extent to which they adhere toâor deviate fromâOstromâs design principles for commonâpool resource management. It highlights structural limitations in governance design, especially in the presence of unequal voting power and centralized control disguised as decentralization. The paper also critiques the socio-economic implications of blockchainâs global expansion, noting how digital governance can replicate neo-colonial dynamics in the Global South and amplify state surveillance in authoritarian contexts. Further, it underscores the environmental costs of blockchain infrastructure and introduces DAOs like KlimaDAO and Regen Network as emerging experiments to align decentralized finance with sustainability goals. Ultimately, the authors propose a âdual imperativeâ: to develop contextâsensitive, inclusive governance architectures within DAOs, while pursuing international legal recognition and standards. The conclusion calls for communitarian models that fuse algorithmic rule enforcement with human-centered deliberation to protect the emancipatory potential of blockchain governance. Whether blockchain becomes a force for democratization or digital enclosure, the authors argue, will depend on how its governing architectures are designed, contested, and evolved by the communities that steward them.
The fusion of composable finance and generative AI signals a transformative leap in decentralized asset management. Composable finance, grounded in modular and interoperable Decentralized Finance (DeFi) protocols, allows developers and users to assemble, customize and automate financial services using plug-and-play smart contract components. Generative AI, leveraging advanced architectures like transformers and diffusion models, introduces new possibilities for dynamic portfolio generation, synthetic asset creation and predictive market analysis. This paper presents an integrated view of how generative AI can enhance composability by intelligently automating decision pathways, risk profiling and liquidity routing across blockchain ecosystems. Drawing insights from Finance 4.0 innovations, AI- powered automation in financial infrastructure and the design of secure, data-driven DeFi environments, we explore use cases that redefine user interaction and asset control in decentralized settings. The research proposes a reference architecture where generative agents act as co-creators of financial strategies, supporting autonomous rebalancing and compliance monitoring in real time. We also analyze how Decentralized Autonomous Organizations (DAOs) can integrate AI agents for governance optimization and crowd-sourced financial intelligence. Challenges such as model transparency, tokenomics, adversarial manipulation and explainability are examined in depth. The paper outlines a future-forward blueprint for scalable, AI-augmented composable finance platforms that reduce technical complexity, increase inclusivity and align with the core tenets of decentralization, user sovereignty and verifiable execution in the Web3 era.