Abstract Buildings contribute significantly to global energy consumption, positioning them as pivotal to achieving global sustainability and climate goals. Although renewable energy technologies hold significant transformative potential, their integration into building systems is hindered by fragmented technological, economic, policy, and social dimensions. This systematic review addresses the existing lack of holistic synthesis by examining peer-reviewed studies published from 2019 to the second quarter (Q2) of 2025. Methodologically, the study adheres to the PRISMA 2020 framework to ensure transparency and replicability and employs bibliometric analysis to map thematic clusters across disciplines. Five major themes emerged from the synthesis: photovoltaic integration, retrofitting strategies, governance frameworks, smart grid applications, and stakeholder acceptance. Analysis highlights notable regional disparities, with Western Europe and East Asia demonstrating higher integration rates due to robust policy structures and established financial incentives. Conversely, regions like Sub-Saharan Africa and Latin America continue to experience significant barriers linked to financing constraints, regulatory fragmentation, and infrastructural limitations. Community-led microgrid initiatives in Kenya and Brazil emerge as exemplars of successful context-sensitive, low-cost renewable integration, emphasizing the value of inclusive governance and localized solutions. The review reconceptualizes buildings as active socio-technical nodes within decentralized energy networks rather than passive energy infrastructures. For renewable energy integration to progress effectively, coordinated systemic efforts spanning technical innovation, adaptive governance, and behavior-sensitive policy design are imperative. These insights carry substantial implications, offering actionable guidance for policymakers, engineers, and urban planners seeking equitable, contextually appropriate, and scalable renewable energy transitions within the built environment.
Reliable asset price data are critical for the functioning of decentralized finance (DeFi) protocols, particularly those involving collateralized lending. The accuracy of blockchain-based price oracles directly affects key processes such as collateral valuation, liquidation, and risk management. This paper presents a comprehensive empirical analysis of Chainlink Price Feeds (CPFs), the dominant oracle infrastructure in DeFi. We compile a novel dataset of over 150 million observations from 40 CPFs on Ethereum over an 18-month period, matched to benchmark prices from a centralized exchange. To identify the determinants of oracle inaccuracy, we estimate pooled OLS and fixed effects regressions, relating price deviations to design parameters, reporter dynamics, and market conditions. We then introduce a Markov-like state transition framework to model the resolution of target corridor violations, using multinomial logistic regression to estimate transition probabilities. Finally, we exploit position-level data from one of the largest decentralized lending markets and apply entity fixed effects regressions to examine how users adjust collateralization in response to oracle design. Our findings highlight economically significant deviations that are systematically related to oracle accuracy configurations and market stress, and show that users internalize these risks in their financial decisions. The results offer new insights for the design of resilient oracle systems and the management of risk in decentralized financial markets.
Eranga Bandara, Sachin Shetty, Ravi Mukkamala, Ross Gore · 12 authors
In recent years, blockchain has experienced widespread adoption across various industries, becoming integral to numerous enterprise applications. Concurrently, the rise of generative AI and LLMs has transformed human-computer interactions, offering advanced capabilities in understanding and generating human-like text. The introduction of the MCP has further enhanced AI integration by standardizing communication between AI systems and external data sources. Despite these advancements, there is still no standardized method for seamlessly integrating LLM applications and blockchain. To address this concern, we propose "MCC: Model Context Contracts" a novel framework that enables LLMs to interact directly with blockchain smart contracts through MCP-like protocol. This integration allows AI agents to invoke blockchain smart contracts, facilitating more dynamic and context-aware interactions between users and blockchain networks. Essentially, it empowers users to interact with blockchain systems and perform transactions using queries in natural language. Within this proposed architecture, blockchain smart contracts can function as intelligent agents capable of recognizing user input in natural language and executing the corresponding transactions. To ensure that the LLM accurately interprets natural language inputs and maps them to the appropriate MCP functions, the LLM was fine-tuned using a custom dataset comprising user inputs paired with their corresponding MCP server functions. This fine-tuning process significantly improved the platform's performance and accuracy. To validate the effectiveness of MCC, we have developed an end-to-end prototype implemented on the Rahasak blockchain with the fine-tuned Llama-4 LLM. To the best of our knowledge, this research represents the first approach to using the concept of Model Context Protocol to integrate LLMs with blockchain.
The subject of the research is methods for detecting attacks in networks with the Proof-of-Stake (PoS) consensus mechanism. The purpose of this experimental investigation and analysis is to evaluate the effectiveness of classical machine learning algorithms for detecting malicious nodes in blockchain systems. The tasks include the analysis of blockchain technology vulnerabilities, the creation and use of a specialized dataset for PoS networks, as well as the construction and testing of machine learning models. The main focus is placed on comparing three algorithms – Random Forest, Support Vector Machine, and k-Nearest Neighbors – in order to determine their suitability for monitoring node activity and detecting anomalies. To solve the tasks set, the following methods were implemented: modeling, empirical, and mathematical approaches were applied. Modeling consisted of software implementation of the selected algorithms and subsequent analysis of their performance using accuracy, recall, F1-score metrics, and confusion matrices. Empirical methods were realized through testing the models on a partially synthetic dataset containing more than 10,000 records of blockchain nodes and transactions. Mathematical methods involved the calculation of statistical performance indicators and the analysis of feature importance that characterizes node behavior. The achieved results include the validation of a dataset for PoS blockchains that incorporates key operational parameters of transactions and nodes, the development of recommendations for further use of machine learning models, and the testing of selected models. Conclusions. The study demonstrated that machine learning is an effective tool for identifying anomalies and malicious activity in blockchain systems. The obtained results lay the foundation for further research, which may focus on expanding the feature space, integrating deep neural networks, developing ensemble approaches, and adapting methods to different types of blockchains.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
Hadis Rezaei, Ahmed Afif Monrat, Karl Andersson, Francesco Palmieri
The deterministic nature of blockchain technology creates fundamental difficulties in producing secure random numbers within smart contracts, a limitation that exposes vulnerabilities in applications such as decentralized finance (DeFi) protocols and blockchain-based gaming platforms. From our observations, the current state-of-the-art detection tools suffer from inadequate precision while dealing with random number vulnerabilities. To address this problem, we propose TaintSentinel, a novel path-sensitive vulnerability detection system designed to analyze smart contracts at the execution path level and gradually analyze taint with domain-specific rules. This paper discusses a solution that incorporates a multifaceted approach, integrating rule-based taint analysis to track data flow, a dual-stream neural network to identify complex vulnerability signatures, and evidence-based parameter initialization to minimize false positives. The two-phase operation of the system involves the construction of semantic graphs and the analysis of taint propagation, followed by pattern recognition using PathGNN and global structural analysis via GlobalGCN. Our experiments on 4,844 contracts demonstrate the superior performance of TaintSentinel relative to existing tools, yielding an F1-score of 0.892, an AUC-ROC of 0.94, and a PRA accuracy of 97%.
The problem of reproducibility of experiments in optimizing validator allocation in blockchain networks with Proof of Stake consensus was investigated, in particular due to the absence of standardized datasets and unified testing methods, which complicates the objective comparison of algorithms. To tackle this issue, we propose a method for building test datasets that rely on deterministic pseudorandom sequence generators and validator profiles calibrated against Ethereum network statistics. Each validator is described by a set of parameters that includes the stake size with the minimum requirement according to Ethereum standards, performance with a uniform distribution, reliability in a high range, network delays depending on the geographical proximity of participants, geographical location according to the actual statistics of validator distribution by regions, quality of network connection, and slashing history according to the violation statistics in the Beacon Chain. Three datasets of different scales were created for small, medium, and large network configurations with fixed initial values of the generators to ensure full reproducibility of experiments. A multi-criteria evaluation system was developed based on a generalized quality indicator that maximizes system throughput and minimizes load imbalance and network delays with scientifically grounded weighting coefficients. The tenfold testing protocol ensures the statistical reliability of results and reduces the impact of randomness on conclusions. The experiments conducted a comparative analysis of four allocation algorithms: a hybrid metaheuristic method based on particle swarm optimization with local search, random allocation with correction, an adapted Ethereum shuffling mechanism, and a greedy algorithm. The experimental results revealed scale-dependent efficiency of the algorithms: the hybrid method provides high optimization quality at all investigated scales, but quadratic growth of execution time limits its application to periodic offline planning of network configuration; the shuffling mechanism demonstrates stable medium-quality results with fast execution; the random method is characterized by moderate speed with variable results; the greedy algorithm shows maximum speed with deterministic results but variable efficiency depending on the network scale. The proposed method forms a basis for standardizing experimental research in Proof of Stake consensus systems. It ensures the objective comparison of new algorithmic solutions for validator allocation in decentralized blockchain networks.
Open access
Advanced Research in Systems and Signal Processing
Yerlan Kistaubayev, Francisco Liébana‐Cabanillas, Aijaz A. Shaikh, Galimkair Mutanov · 6 authors
It has been recognized that Blockchain technology contributes to environmentally sustainable development goals (SDGs). It has emerged as a disruptive innovation capable of transforming various economic and social sectors significantly. This conceptual paper is driven by the need to explore how blockchain, specifically a consortium-based Ethereum architecture, can be integrated into higher education institutions to ensure data sovereignty, integrity, and verifiability while adhering to legal and ethical standards such as GDPR. We propose a multi-layered blockchain-based model for Kazakhstan’s Unified Platform of Higher Education (UPHE). This model employs hybrid on-chain/off-chain data storage, smart contract automation, and a Proof-of-Authority consensus mechanism to address system limitations, including data centralization and inadequate verification of academic credentials. Empirical simulations using Blockscout and Ethereum-compatible tools demonstrate the model’s feasibility and performance. This paper contributes to the growing discussion on educational blockchain applications by presenting a scalable, secure, and transparent architecture that aligns with institutional governance and Environmental, Social, and Governance (ESG) principles. It also supports the objectives of UN SDG 4 (i.e., Quality education) by fostering trust, transparency, and equitable access to verifiable educational credentials.
Wan Ki Wong, Sahel Torkamani, Michele Ciampi, Rik Sarkar
Evaluating the usefulness of data before purchase is essential when obtaining data for high-quality machine learning models, yet both model builders and data providers are often unwilling to reveal their proprietary assets. We present PrivaDE, a privacy-preserving protocol that allows a model owner and a data owner to jointly compute a utility score for a candidate dataset without fully exposing model parameters, raw features, or labels. PrivaDE provides strong security against malicious behavior and can be integrated into blockchain-based marketplaces, where smart contracts enforce fair execution and payment. To make the protocol practical, we propose optimizations to enable efficient secure model inference, and a model-agnostic scoring method that uses only a small, representative subset of the data while still reflecting its impact on downstream training. Evaluation shows that PrivaDE performs data evaluation effectively, achieving online runtimes within 15 minutes even for models with millions of parameters. Our work lays the foundation for fair and automated data marketplaces in decentralized machine learning ecosystems.
Jiri Gavenda, Petr Svenda, Stanislav Bobon, Vladimir Sedlacek
A coinjoin protocol aims to increase transactional privacy for Bitcoin and Bitcoin-like blockchains via collaborative transactions, by violating assumptions behind common analysis heuristics. Estimating the resulting privacy gain is a crucial yet unsolved problem due to a range of influencing factors and large computational complexity. We adapt the BlockSci on-chain analysis software to coinjoin transactions, demonstrating a significant (10-50%) average post-mix anonymity set size decrease for all three major designs with a central coordinator: Whirlpool, Wasabi 1.x, and Wasabi 2.x. The decrease is highest during the first day and negligible after one year from a coinjoin creation. Moreover, we design a precise, parallelizable privacy estimation method, which takes into account coinjoin fees, implementation-specific limitations and users' post-mix behavior. We evaluate our method in detail on a set of emulated and real-world Wasabi 2.x coinjoins and extrapolate to its largest real-world coinjoins with hundreds of inputs and outputs. We conclude that despite the users' undesirable post-mix behavior, correctly attributing the coins to their owners is still very difficult, even with our improved analysis algorithm.
Chong Chen, Ze Liu, Lingfeng Bao, Yanlin Wang · 7 authors
The cryptocurrency market offers significant investment opportunities but faces challenges including high volatility and fragmented information. Data integration and analysis are essential for informed investment decisions. Currently, investors use three main approaches: (1) Manual analysis across various sources, which depends heavily on individual experience and is time-consuming and prone to bias; (2) Data aggregation platforms-limited in functionality and depth of analysis; (3) Large language model agents-based on static pretrained models, lacking real-time data integration and multi-step reasoning capabilities. To address these limitations, we present Coinvisor, a reinforcement learning-based chatbot that provides comprehensive analytical support for cryptocurrency investment through a multi-agent framework. Coinvisor integrates diverse analytical capabilities through specialized tools. Its key innovation is a reinforcement learning-based tool selection mechanism that enables multi-step planning and flexible integration of diverse data sources. This design supports real-time interaction and adaptive analysis of dynamic content, delivering accurate and actionable investment insights. We evaluated Coinvisor through automated benchmarks on tool calling accuracy and user studies with 20 cryptocurrency investors using our interface. Results show that Coinvisor improves recall by 40.7% and F1 score by 26.6% over the base model in tool orchestration. User studies show high satisfaction (4.64/5), with participants preferring Coinvisor to both general LLMs and existing crypto platforms (4.62/5).
Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (LLMs) have recently made significant progress in smart contract vulnerability detection. However, they still face challenges such as high inference costs and substantial computational overhead. In this paper, we propose ParaVul, a parallel LLM and retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection. Specifically, we first develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning. SLoRA introduces sparsification by incorporating a sparse matrix into quantized LoRA-based LLMs, thereby reducing computational overhead and resource requirements while enhancing their ability to understand vulnerability-related issues. We then construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system that integrates dense retrieval with Best Matching 25 (BM25), assisting in verifying the results generated by the LLM. Furthermore, we propose a meta-learning model to fuse the outputs of the RAG system and the LLM, thereby generating the final detection results. After completing vulnerability detection, we design chain-of-thought prompts to guide LLMs to generate comprehensive vulnerability detection reports. Simulation results demonstrate the superiority of ParaVul, especially in terms of F1 scores, achieving 0.9398 for single-label detection and 0.9330 for multi-label detection.
Globally, the private sector is moving towards expanding the growth of tokenized assets trading, using private money channels (stablecoins) as payment systems. At the same time, the public sector, often central banks in alliance with private sector partners, is engaged in ambitious projects, exploring the feasibility of public payment systems using wholesale CBDCs to facilitate the cross-border use of fiat currencies and to ensure universal public trust in global cross-border payments. The advent of the use of distributed ledger technology over the past decade is enabling these activities to take place. This paper outlines these developments and their impacts and the future emergence of a global assets trading and payments ecosystem. The emergence of this system is also considered in the context of the gradual restructuring of global trade and monetary patterns, via private and public sector initiatives, and the potential significant impact of a Trumpian world order. The future seems to imply a more protectionist and, effectively, isolationist US—in monetary as well as trading terms.
This paper examines the transformation of decentralized financial ecosystems due to the emerging integration of artificial intelligence (AI) with cryptocurrency technologies. By enabling digital transactions that are adaptive, autonomous, and secure, AI-enhanced cryptocurrencies have the potential to upend conventional financial systems. While highlighting AI's potential to improve scalability, efficiency, fraud detection, and investment strategies within blockchain-based economies, this paper analyzes future trends, technical challenges, ethical concerns, and regulatory frameworks.
This paper introduces The Visual Ledger, a diagnostic analysis of European art history between 1849 and 1991 that treats painting as an archival instrument registering the progressive evacuation of ontological weight from the human figure. Using a diagnostic history methodology, the article argues that painters detected and documented shifts in how presence, consequence, and embodiment are organised decades before these transformations consolidated institutionally. Beginning with the material density of Realism in Courbet and Repin, the analysis traces a directional transformation through Impressionism’s optical dissolution, Pointillism’s cognitive fragmentation, Art Nouveau’s decorative masking, Malevich’s honest void, and Pop Art’s operationalisation of abstraction as governance. The AIDS crisis functions as a late twentieth-century visibility stress test, exposing the limits of surface-based legibility when confronted with suffering resistant to procedural categorisation. The paper demonstrates that visual archives independently converge with literary and administrative records, showing that populations currently collapsing under procedural abstraction are experiencing the settlement of a transformation painters registered when evacuation began. This contribution is conceptual and diagnostic rather than empirical. It forms one component of the broader Equilibrium Ledger framework, which examines how institutions generate and distribute cognitive costs, and how procedural abstraction renders embodied complexity administratively legible while humanly inaccessible.
In the digital era, managing royalties for creative works remains a major challenge. Existing systems are often outdated and lack the transparency and efficiency required to meet the growing demands of digital content distribution. This paper presents BlockRoyalty, an application based on blockchain and smart contract technologies, designed to modernize royalty management for digital books. The system automates the registration of authors, publishers and buyers. It manages book publication, applies dynamic pricing based on sales trends and ensures real-time royalty distribution. All transactions are securely and recorded in a verifiable manner on the blockchain, fostering trust among stakeholders.
This study investigates prospective Arab customers’ intentions to use cryptocurrencies. Using a quantitative approach, cross-sectional data from a purposive sample of 437 respondents were collected. The survey was distributed via 13 well-known social media platforms and Arab-focused social media groups. Direct, mediating, and moderating hypotheses are tested using structural equation modeling (SEM). The findings confirmed that the association between Digital Techno-stress (DTS) and the Intention to Adopt Cryptocurrency (IACR) is moderated by Ethical Issues (EI). Nevertheless, the study found that government regulations (GR) had no moderating effect on Arab cryptocurrency investors. The findings emphasize the necessity of ethical frameworks to increase credibility in Arab cryptocurrency marketplaces by fostering user-centric trading platforms, lowering techno-stress, and fostering trust.
Alexandru Ursu, Petru Lucian Curșeu, Sabina Trif, Alina Maria Fleştea
Cryptocurrencies are rapidly transforming digital finance and entrepreneurship, yet their adoption by entrepreneurs remains rather poorly understood. Drawing on the Threat-Rigidity Model (TRM) and the opportunity recognition literature, this study examines how entrepreneurial experience, financial literacy, perceived opportunities, and perceived threats influence entrepreneurial intention to use cryptocurrencies. We tested a moderated mediation model in which the association between financial literacy and experience, on the one hand, and intention to use cryptocurrencies, on the other, was mediated by perceived opportunities. In this model, perceived threats served as a moderator on the relationship between financial literacy and intention, as well as between perceived opportunities and adoption intention. Data were collected from a sample of 133 Romanian entrepreneurs across diverse industries. The results supported the mediating role of perceived opportunities in the relationship between financial literacy and intention to use cryptocurrencies in business and showed that the positive association between financial literacy and intention was attenuated by perceived threats. Entrepreneurial experience did not significantly influence perceived opportunities, while women entrepreneurs reported lower intention to adopt cryptocurrencies in business. This study is among the first to use the TRM to explore how the interplay of perceived opportunities and threats shapes cryptocurrency adoption in entrepreneurship. Other implications, limitations, and directions for future research are also discussed.
Iván Abellán Álvarez, Pol Hölzmer, Johannes Sedlmeir
Digital identity wallets promise significant advancements in digital identity management by offering users a high degree of convenience, security, and control over their data disclosure. However, there is also criticism regarding their privacy guarantees, especially when used in regulated use cases that require high levels of assurance on the correctness and binding of a legal identity. In this paper, we present a comprehensive privacy model and analysis of one of the most prominent digital wallets – the European Digital Identity Wallet (EUDIW) – as specified by the Architecture and Reference Framework (ARF) and the eIDAS 2.0 regulation. We employ a suite of qualitative privacy risk assessment methods to systematically map and evaluate information flows in three key use cases. Our analysis identifies multiple privacy risks – including linkability, identifiability, and excessive attribute data disclosure – and reveals that although the ARF is designed to comply with privacy-by-design principles, inherent design choices, such as the reliance on SD-JWT and mDOC data formats, as well as the concept of a Wallet Unit Attestation (WUA), retain risks to user privacy. Building on our findings, we then highlight how advanced Privacy-Enhancing Technologies (PETs), such as (general-purpose) Zero-Knowledge Proofs (ZKPs), can reduce or mitigate some of these risks.
Venture capital investment and hedge fund investment are two asset classes of alternative investment fund portfolios. The purpose of this study was to determine whether the digital currency named bitcoin truly adds to diversification in an alternative investment fund portfolio. Vector auto regression was used to determine any unidirectional or bidirectional relationship between variables. The DCC-GARCH test was conducted to determine any conditional correlations that impact volatility transmission over a shorter and longer duration of time between variables. The results showed that there was no unidirectional or bidirectional relationship between bitcoin and FTSE venture capital index, as well as between bitcoin and the Barclays Hedge Fund Index. The DCC model showed no volatility transmission between bitcoin and the Barclays Hedge Fund Index, whereas volatility persists between bitcoin and the FTSE Venture Capital Index, connecting risk between the financial time series with only low correlations. These findings suggest that bitcoin could be used by investors, policy makers, and hedgers for diversification in alternative investment fund portfolios.
AI text-to-video systems, such as OpenAI’s Sora, promise substantial efficiency gains in media production but also pose risks of biased outputs, opaque optimization, and deceptive content. Using the Orientation–Stimulus–Orientation–Response (O-S-O-R) model, we conduct an empirical study with 209 Chinese new media professionals and employ structural equation modeling to examine how information elaboration relates to AI knowledge, perceptions, and adoption intentions. Our findings reveal a knowledge paradox: higher objective AI knowledge negatively moderates elaboration, suggesting that centralized information ecosystems can misguide even well-informed practitioners. Building on these behavioral insights, we propose a blockchain-based governance framework that operationalizes five mechanisms to enhance oversight and trust while maintaining efficiency: Expert Assessment DAOs, Community Validation DAOs, real-time algorithm monitoring, professional integrity protection, and cross-border coordination. While our study focuses on China’s substantial new media market, the observed patterns and design principles generalize to global contexts. This work contributes empirical grounding for Web3-enabled AI governance, specifies implementable smart-contract patterns for multi-stakeholder validation and incentives, and outlines a research agenda spanning longitudinal, cross-cultural, and implementation studies.
The increasing prevalence of Maximal Extractable Value (MEV) in blockchain networks has highlighted critical challenges in achieving fair and predictable transaction ordering. On Ethereum, where block builders possess unrestricted control over transaction sequencing, users face significant risks from frontrunning and sandwich attacks, particularly within decentralized finance (DeFi) applications interacting with shared contract states. To address this issue, this paper proposes a hybrid MEV mitigation method employing Lamport-style logical clocks, designed to establish a local causal ordering mechanism within individual smart contracts. The proposed approach equips each smart contract, such as a decentralized exchange liquidity pool, with a local logical timestamp counter. Transactions submitted to the contract carry logical timestamps, enabling the enforcement of a causally consistent execution order. A key benefit of this method is that it does not necessitate alterations to Ethereum’s global consensus mechanism, thus ensuring compatibility with the current Ethereum ecosystem, as well as rollups and modular app-chain architectures. The study details the protocol design, explores various implementation strategies for both on-chain and off-chain execution environments, and addresses resilience against adversarial attempts such as timestamp manipulation and denial-of-service attacks. The primary advantage of this approach lies in its effectiveness in mitigating intra-contract MEV extraction by strictly controlling transaction reordering for conflicting state interactions, while preserving concurrency for non-conflicting transactions. Findings indicate that the use of local Lamport clocks provides a practical, low-overhead solution for MEV-sensitive applications, including decentralized exchanges and rollup sequencing systems.
This paper analyzes the time-varying herding behavior in the non-fungible token (NFTs) and cryptocurrency markets and investigates their interrelationship. Using the daily market data from January 1st, 2020 to April 30th, 2023, our study covers the period characterized by Covid and post-Covid-19 induced global financial market volatility, capturing the dynamics in the global macroeconomic system and the Federal Reserve’s interest rate policy. Based on the rolling window method, our findings show the presence of herding behavior in both markets, where herding behavior in these markets may be influenced by the major events announcements particularly those related to the Federal Reserve's interest rate policy. Vector error correction model (VECM) indicates that the NFT market impacts the price of Ethereum, thereby influencing the broader cryptocurrency market. Such finding contributes to a deeper understanding of the market dynamics. By examining herding behavior, our findings indicate that the NFT market demonstrates relative independence from the volatile prices of the cryptocurrency market, suggesting the potential diversification benefits of incorporating NFTs for investors’ portfolio construction and risk management.
سازمانهای خودگردان غیرمتمرکز (DAO)، به دلیل ویژگیهایی چون فقدان مدیریت انسانی متمرکز و ساختار فراملی، با مفهوم سنتی شخصیت حقوقی در تضاد ماهوی قرار دارند. پژوهش حاضر از توصیف این بنبست شناختهشده عبور کرده و به یک پرسش راهبردی پاسخ میدهد: نظام حقوقی ایران چگونه میتواند با الهام از مبانی فقهی و تحلیل تطبیقی تجارب نوین جهانی (مانند وایومینگ)، مدلی جدید تحت عنوان «شخصیت حقوقی الگوریتمی» را طراحی و شناسایی کند؟ این تحقیق با روش توصیفی-تحلیلی، پس از اثبات ناکارآمدی چارچوبهای فعلی برای حل بحران مسئولیت و صلاحیت قضایی، به عنوان یافته اصلی، ارکان و الزامات یک مدل مفهومی نوین را ارائه میدهد. این مدل، اهلیت و موجودیت نهاد را نه بر ارکان انسانی، بلکه بر شفافیت کد، قابلیت حسابرسی الگوریتم و معرفی یک عامل ثبتشده استوار میسازد. مقاله همچنین با بررسی تطبیقی رویکردهای جهانی و پاسخگویی به انتقادات کلیدی، اعتبار و کارآمدی مدل پیشنهادی را تقویت مینماید. نتیجهگیری پژوهش آن است که مواجهه کارآمد با DAO مستلزم عبور از راهکارهای اصلاحی و حرکت به سمت قانونگذاری جدید و ویژهای است که این شخصیت حقوقی فناورانه را به رسمیت بشناسد و ضمن فراهم آوردن بستر نوآوری اقتصادی، پاسخگویی حقوقی و قضایی این نهادها را در برابر حاکمیت تضمین نماید.