As a pivotal innovation in digital infrastructure, blockchain ledger technology catalyzes the development of nascent business paradigms and applications globally. Utilizing Rothwell and Zegveld's taxonomy of twelve innovation policy tools, this study offers a nuanced comparison of domestic blockchain policies, dissecting supply, environment, and demand-driven policy dimensions to distill prevailing strategic orientations towards blockchain healthcare adoption. The findings indicate that blockchain technology has seen rapid growth in the healthcare industry. However, a certain misalignment exists between the corporate and policy layers in terms of supply and demand. While companies focus more on technological applications, existing policies are geared towards regulations and governance. Government emphasis lies on legal supervision through environmental policies, aiming to guide the standardization and regulation of blockchain technology. This maintains a balance between encouraging innovation and market and legal regulatory order, thereby providing a reference for the development of the distributed intelligent healthcare industry in our country.
Kostantinos Papadamou, Jay Patel, Jeremy Blackburn, Philipp Jovanovic · 5 authors
This paper presents a large-scale analysis of the cryptocurrency community on Reddit, shedding light on the intricate relationship between the evolution of their activity, emotional dynamics, and price movements. We analyze over 130M posts on 122 cryptocurrency-related subreddits using temporal analysis, statistical modeling, and emotion detection. While /r/CryptoCurrency and /r/dogecoin are the most active subreddits, we find an overall surge in cryptocurrency-related activity in 2021, followed by a sharp decline. We also uncover a strong relationship in terms of cross-correlation between online activity and the price of various coins, with the changes in the number of posts mostly leading the price changes. Backtesting analysis shows that a straightforward strategy based on the cross-correlation where one buys/sells a coin if the daily number of posts about it is greater/less than the previous would have led to a 3x return on investment. Finally, we shed light on the emotional dynamics of the cryptocurrency communities, finding that joy becomes a prominent indicator during upward market performance, while a decline in the market manifests an increase in anger.
Abstract The use of computer technology to automate the enforcement of law is a promising alternative to simplify bureaucratic procedures. However, careless automation might result in an inflexible and dehumanized law enforcement system driven by algorithms that do not account for the particularities of individuals or minorities. In this article, we argue that hybrid smart contracts deployed to monitor rather than blindly enforce regulations can be used to add flexibility. Enforcement is a suitable alternative only when prevention is strictly necessary; however, we argue that in many situations a corrective approach based on monitoring is more flexible and suitable. To add more flexibility, the hybrid smart contract can be programmed to stop to request the intervention of a human or of a group of them when human judgment is needed.
Reddiment is a web-based dashboard that links sentiment analysis of subreddit texts with share prices. The system consists of a backend, frontend and various services. The backend, in Node.js, manages the data and communicates with crawlers that collect Reddit comments and stock market data. Sentiment is analyzed with the help of Vader and TextBlob. The frontend, based on SvelteKit, provides users with a dashboard for visualization. The distribution is carried out via Docker containers and Docker Compose. The project offers expansion options, e.g. the integration of cryptocurrency rates. Reddiment enables the analysis of sentiment and share prices from subreddit data.
Accountability in the workplace is critically important and remains a challenging problem, especially with respect to workplace safety management. In this paper, we introduce a novel notion, the Internet of Responsibilities, for accountability management. Our method sorts through the list of responsibilities with respect to hazardous positions. The positions are interconnected using directed acyclic graphs (DAGs) indicating the hierarchy of responsibilities in the organization. In addition, the system detects and collects responsibilities, and represents risk areas in terms of the positions of the responsibility nodes. Finally, an automatic reminder and assignment system is used to enforce a strict responsibility control without human intervention. Using blockchain technology, we further extend our system with the capability to store, recover and encrypt responsibility data. We show that through the application of the Internet of Responsibility network model driven by Big Data, enterprise and government agencies can attain a highly secured and safe workplace. Therefore, our model offers a combination of interconnected responsibilities, accountability, monitoring, and safety which is crucial for the protection of employees and the success of organizations.
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.
Nikolaos Misirlis, Yiannis Nikolaidis, Anna Sabidussi
Metaverse, a burgeoning technological trend that combines virtual and augmented reality, provides users with a fully digital environment where they can assume a virtual identity through a digital avatar and interact with others as they were in the real world. Its applications span diverse domains such as economy (with its entry into the cryptocurrency field), finance, social life, working environment, healthcare, real estate, and education. During the COVID-19 and post-COVID-19 era, universities have rapidly adopted e-learning technologies to provide students with online access to learning content and platforms, rendering previous considerations on integrating such technologies or preparing institutional infrastructures virtually obsolete. In light of this context, the present study proposes a framework for analyzing university students' acceptance and intention to use metaverse technologies in education, drawing upon the Technology Acceptance Model (TAM). The study aims to investigate the relationship between students' intention to use metaverse technologies in education, hereafter referred to as MetaEducation, and selected TAM constructs, including Attitude, Perceived Usefulness, Perceived Ease of Use, Self-efficacy of metaverse technologies in education, and Subjective Norm. Notably, Self-efficacy and Subjective Norm have a positive influence on Attitude and Perceived Usefulness, whereas Perceived Ease of Use does not exhibit a strong correlation with Attitude or Perceived Usefulness. The authors postulate that the weak associations between the study's constructs may be attributed to limited knowledge regarding MetaEducation and its potential benefits. Further investigation and analysis of the study's proposed model are warranted to comprehensively understand the complex dynamics involved in the acceptance and utilization of MetaEducation technologies in the realm of higher education
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.
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.
Oct 30, 2023·Proc of the 14th International Conference on Cloud Computing, GRIDs, and Virtualization (Cloud Computing 2023), Nice, France, June 2023, pp. 41-46, ISSN 2308-4294
We consider the problem of supply chain data visibility in a blockchain-enabled supply chain network. Existing methods typically record transactions happening in a supply chain on a single blockchain and are limited in their ability to deal with different levels of data visibility. To address this limitation, we present FoodFresh -- a multi-chain consortium where organizations store immutable data on their blockchains. A decentralized hub coordinates the cross-chain exchange of digital assets among the heterogeneous blockchains. Mechanisms for enabling blockchain interoperability help to preserve the benefits of independent sovereign blockchains while allowing for data sharing across blockchain boundaries.
Identity documentation for refugees is a complex process and crucial for host nations. A secured identity management system ensures both security and the efficient provision of services for the host nation and the donor organizations. Realizing the benefits, a handful of studies enriched the blockchain-based security identification for refugees. The research studies presented the introductory, conceptual, and practical solution related to the blockchain-based smart contract. There is a common agreement in the studies that blockchain-based smart contract not only streamlines refugee identity verification but also safeguards against unauthorized entries. Since it is a technology as well, it has been essential to know the present status of the technology in the social context. In such a situation it becomes essential to review the existing research studies to provide insight for future studies. In this study, we reviewed current studies using a thematic approach. Our findings suggest researchers are more inclined to provide conceptual models as the models are important in advancing technology; however, the models need to be implemented for practical advances. However, the main contribution of this study is that this study gathers current efforts in smart contract-based refugee identity management. This study is important for the refugee host nations as well as for stakeholders. Knowledge gained from the study is expected to provide insight into how the technology can be developed using existing theory and implementation frameworks.
Joshua Tan, Tara Merk, Sarah Hubbard, Eliza R. Oak · 29 authors
Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations.
In recent years, the maturation of emerging technologies such as Virtual Reality, Digital twins, and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities that bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future.
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.
The World Wide Web, a ubiquitous source of information, serves as a primary resource for countless individuals, amassing a vast amount of data from global internet users. However, this online data, when scraped, indexed, and utilized for activities like web crawling, search engine indexing, and, notably, AI model training, often diverges from the original intent of its contributors. The ascent of Generative AI has accentuated concerns surrounding data privacy and copyright infringement. Regrettably, the web's current framework falls short in facilitating pivotal actions like consent withdrawal or data copyright claims. While some companies offer voluntary measures, such as crawler access restrictions, these often remain inaccessible to individual users. To empower online users to exercise their rights and enable companies to adhere to regulations, this paper introduces a user-controlled consent tagging framework for online data. It leverages the extensibility of HTTP and HTML in conjunction with the decentralized nature of distributed ledger technology. With this framework, users have the ability to tag their online data at the time of transmission, and subsequently, they can track and request the withdrawal of consent for their data from the data holders. A proof-of-concept system is implemented, demonstrating the feasibility of the framework. This work holds significant potential for contributing to the reinforcement of user consent, privacy, and copyright on the modern internet and lays the groundwork for future insights into creating a more responsible and user-centric web ecosystem.
Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing applications such as Artificial Intelligence (AI)/Machine Learning for Natural Language Processing, Scientific Simulations, and Cryptocurrency Mining have been estimated. In contrast to the bit-level switching, in which transistors achieved energy efficiency due to geometrical scaling, higher energy is expended both at the at the instructions and simulations levels of an application. Additionally, the analysis based on AI/ML Accelerators indicate that changes in architectures using an older semiconductor technology node have comparable energy efficiency with a different architecture using a newer technology. Further comparisons of the energy in computing systems with the thermodynamic and biological limits, indicate that there is a 27-36 orders of magnitude higher energy requirements for total simulation of an application. These energy estimates underscore the need for serious considerations of energy efficiency in computing by including energy as a design parameter, enabling growing needs of compute-intensive applications in a digital world.
Feng Liu, Jie Yang, Kun-peng Xu, Cang-long Pu · 5 authors
The influence of opinion leaders impacts different aspects of social finance. How to analyse the utility of opinion leaders' influence in realizing assets on the blockchain and adopt a compliant regulatory scheme is worth exploring and pondering. Taking Musk's call on social media to buy Dogecoin as an example, this paper uses an event study to empirically investigate the phenomenon in which opinion leaders use ICOs (initial coin offerings) to exert influence. The results show that opinion leaders can use ICOs to influence the price of token assets with money and data traffic in their social network. They can obtain excess returns and reduce the cost of realization so that the closed loop of influence realization will be accelerated. Based on this phenomenon and the results of its impact, we use the ChainLink Oracle with Active Sensor Networks(CO-ASnet) to design a safe and applicable decentralized regulatory scheme that can constructively provide risk assessment strategies and early warning measures for token issuance. The influence realization of opinion leaders in blockchain issuance is bound to receive widespread attention, and this paper will provide an exemplary reference for regulators and enterprises to explore the boundaries of blockchain financial product development and governance.
Catherine Carpentier-Desjardins, Masarah Paquet-Clouston, Stefan Kitzler, Bernhard Haslhofer
*PLEASE REFER TO THE SECOND VERSION UPLOADED IN JANUARY 2025. THIS VERSION CONTAINS A FEW DUPLICATES. VERSION 2 IS AVAILABLE FOR DOWNLOAD HERE: https://zenodo.org/records/14706760 README - Crime Events Dataset This document provides a detailed overview of the structure of the dataset for the paper: "Mapping the DeFi crime landscape: An Evidence-based Picture". The following fields are included, each representing different aspects of the events collected. Data Fields 1. unique_key Description: A unique number assigned to identify each event in the dataset. 2. Agregators Description: The sources where the event is listed. Aggregators include: - De.Fi REKT - SlowMist - CryptoSec (rebranded to ChainSec as of February 2023) 3. DeFi actor involved Description: The name of the DeFi actor involved in the event (target, perpetrator, or intermediary). Sources: - On De.Fi REKT: Found as the "Title" of the event’s listing. - On SlowMist: Found under the “Hacked target” title. - On CryptoSec: Found in the "Title" of the event’s listing with the date. 4. REKT URL Description: The URL to the event's listing on De.Fi REKT. Process: Found by searching for the DeFi actor involved in the REKT Database: https://de.fi/rekt-database 5. SlowMist URL Description: The URL to the event's listing on SlowMist. Process: Available via https://hacked.slowmist.io/search/. Note that searching the actor's name will lead to the event but without an individualized URL. 6. CryptoSec URL Description: The URL to the event's listing on CryptoSec. Process: Found at https://chainsec.io/defi-hacks/. Events are listed on a single page; use traditional keyboard search to locate specific events. 7. Aggregator Summary Description: A summary of the event provided by the aggregator. Sources: - On De.Fi REKT: Found under "Quick Summary" and "Details of the Exploit". - On SlowMist: Under "Description of the event". - On CryptoSec: Below the title in quotation marks. 8. Aggregator sources URL Description: The URLs of references linked by the aggregator in the event’s listing. Sources: - On De.Fi REKT: Found at the bottom by clicking "Source" or "Archived link". - On SlowMist: Found by clicking "View Reference Sources". - On CryptoSec: Available by clicking the source’s name at the end of the summary. 9. Event date Description: The date the event occurred. Sources: - On De.Fi REKT: Listed under the "Date" field. - On SlowMist: At the top right of the listing. - On CryptoSec: Listed in parentheses behind the actor’s name. 10. Event year Description: The year the event occurred, extracted from the Event date. 11. Stolen amount USD Description: The total amount stolen, converted to USD. Sources: - On De.Fi REKT: Found under "Funds lost". - On SlowMist: Under the title “Amount of loss”. - On CryptoSec: Behind the title "Amount stolen". Note: If needed, conversions were manually performed using CoinMarketCap’s historical data as explained in the paper. 12. Implication of actor Description: Indicates whether the DeFi actor was a target, perpetrator, or intermediary in the event. Manually coded after reviewing the aggregator’s summary and linked sources. 13. Strategy Description: The main approach used to steal funds. Six categories are possible: Technical vulnerability, Human risks, Undetermined, Malicious use of contract, Misappropriation of funds, and Imitation. This was manually coded from the event summary and sources. 14. General tactic Description: The common techniques or methods used by malicious actors. Eleven categories are possible, defined in the appendix. Manually coded after reviewing the summary and linked sources. 15. Specific tactic Description: The precise technique used to commit the crime. Thirty-seven categories are possible, defined in the appendix. This was manually coded based on the event summary and sources. 16. Paper category Description: The main area of operation of the involved DeFi actor. Twelve categories are possible: Blockchain, Bridge, DApp, Derivatives, Exchange, Fungible Token (FT), Non-Fungible Token (NFT), Oracle, Yield, Staking, and Others. This was determined by the event summary and research on the actor. 17. Stack category Description: The technical layer of the DeFi Stack Reference (DSR) model corresponding to the paper category. Five categories are possible: DeFi Compositions (CP), DeFi Protocols (P), Cryptoassets (CA), Distributed Ledger Technology (DLT), and Interfaces (INT). --- For more detailed information on the tactics, strategies, or categories used, please refer to the appendix of the dataset or the associated documentation.
Massimiliano Silenzi, Umut Can Cabuk, Enis Karaarslan, Omer Aydin
The fast-growing cryptocurrency sector presents both challenges and opportunities for businesses and consumers alike. This study investigates the knowledge, expertise, and buying habits of people who shop using cryptocurrencies. Our survey of 516 participants shows that knowledge levels vary from beginners to experts. Interestingly, a segment of respondents, nearly 30%, showed high purchase frequency despite their limited knowledge. Regression analyses indicated that while domain knowledge plays a role, it only accounts for 11.6% of the factors affecting purchasing frequency. A K-means cluster analysis further segmented the respondents into three distinct groups, each having unique knowledge levels and purchasing tendencies. These results challenge the conventional idea linking extensive knowledge to increased cryptocurrency usage, suggesting other factors at play. Understanding this varying crypto-shopper demographic is pivotal for businesses, emphasizing the need for tailored strategies and user-friendly experiences. This study offers insights into current crypto-shopping behaviors and discusses future research exploring the broader impacts and potential shifts in the crypto-consumer landscape.
National digital identity systems have become a key requirement for easy access to online public services, specially during Covid-19. While many countries have adopted a national digital identity system, many are still in the process of establishing one. Through a comparative analysis of the technological and legal dimensions of a few selected national digital identity solutions currently being used in different countries, we highlight the diversity of technologies and architectures and the key role of the legal framework of a given digital identity solution. We also present several key issues related to the implementation of these solutions, how to ensure the State sovereignty over them, and how to strike the right balance between private sector and public sector needs. This position paper aims to help policy makers, software developers and concerned users understand the challenges of designing, implementing and using a national digital identity management system and establishing a legal framework for digital identity management, including personal data protection measures. The authors of this paper have a favorable position for self-sovereign identity management systems that are based on Blockchain technology, and we believe they are the most suitable for national digital identity systems.
Most algorithms deployed in healthcare do not consider gender and sex despite the effect they have on individuals' health differences. Missing these dimensions in healthcare information systems is a point of concern, as neglecting these aspects will inevitably perpetuate existing biases, produce far from optimal results, and may generate diagnosis errors. An often-overlooked community with distinct care values and needs are LGBT+ older adults, which has traditionally been under-surveyed in healthcare and technology design. This paper investigates the implications of missing gender and sex considerations in distributed ledger technologies for LGBT+ older adults. By using the value sensitive design methodology, our contribution shows that many value meanings dear to marginalized communities are not considered in the design of the blockchain, such as LGBT+ older adults' interpretations of trust, privacy, and security. By highlighting the LGBT+ older population values, our contribution alerts us to the potential discriminatory implications of these technologies, which do not consider the gender and sex differences of marginalized, silent populations. Focusing on one community throughout - LGBT+ older adults - we emphasize the need for a holistic, value sensitive design approach for the development of ledger technologies for healthcare, including the values of everyone within the healthcare ecosystem.
Non-fungible tokens (NFTs) are unique cryptographic assets representing the ownership of digital media. NFTs have soared in popularity and trading prices. However, there exists a large gap in the literature regarding NFTs, especially regarding the stakeholders and online communities that have formed around NFT projects. Bored Ape Yacht Club (BAYC) is one of the most influential NFT projects. Through an observational study of online BAYC communities across social media platforms and semi-structured interviews with four participants who owned BAYC NFTs, we explored the experiences of NFT collectors within the online NFT community. Positive community experiences, i.e., personal expression and identity, mutual support among BAYC holders, and exclusive access to online and offline events, were expressed. Encountered challenges included scams and "cash grab" NFT projects as well as trolling. The results of this study point towards the welcoming, positive nature of the NFT community, which is a possible causation factor of the initial rise in popularity of NFTs. Demotivators, on the other hand, countered the established trustworthiness of NFT technology among its consumers.
Fair machine learning is a thriving and vibrant research topic. In this paper, we propose Fairness as a Service (FaaS), a secure, verifiable and privacy-preserving protocol to computes and verify the fairness of any machine learning (ML) model. In the deisgn of FaaS, the data and outcomes are represented through cryptograms to ensure privacy. Also, zero knowledge proofs guarantee the well-formedness of the cryptograms and underlying data. FaaS is model--agnostic and can support various fairness metrics; hence, it can be used as a service to audit the fairness of any ML model. Our solution requires no trusted third party or private channels for the computation of the fairness metric. The security guarantees and commitments are implemented in a way that every step is securely transparent and verifiable from the start to the end of the process. The cryptograms of all input data are publicly available for everyone, e.g., auditors, social activists and experts, to verify the correctness of the process. We implemented FaaS to investigate performance and demonstrate the successful use of FaaS for a publicly available data set with thousands of entries.
Attention-Aware Social Choice tackles the fundamental conflict faced by some agent communities between their desire to include all members in the decision making processes and the limited time and attention that are at the disposal of the community members. Here, we investigate a combination of two techniques for attention-aware social choice, namely Natural Language Processing (NLP) and Sampling. Essentially, we propose a system in which each governance proposal to change the status quo is first sent to a trained NLP model that estimates the probability that the proposal would pass if all community members directly vote on it; then, based on such an estimation, a population sample of a certain size is being selected and the proposal is decided upon by taking the sample majority. We develop several concrete algorithms following the scheme described above and evaluate them using various data, including such from several Decentralized Autonomous Organizations (DAOs).