Navigating the Research Landscape of Decentralized Autonomous Organizations: A Research Note and Agenda

• URL: http://arxiv.org/abs/2312.17197v1
• Date: Thu, 28 Dec 2023 18:29:40 GMT
• Title: Navigating the Research Landscape of Decentralized Autonomous Organizations: A Research Note and Agenda
• Authors: Christian Ziegler, Quinn DuPont
• Abstract summary: This note serves as a cause for thought for scholars interested in researching Decentralized Autonomous Organizations (DAOs) It covers key aspects of data retrieval, data selection criteria, issues in data reliability and validity. The agenda aims to equip scholars with the essential knowledge required to conduct nuanced and rigorous academic studies on DAOs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This note and agenda serve as a cause for thought for scholars interested in researching Decentralized Autonomous Organizations (DAOs), addressing both the opportunities and challenges posed by this phenomenon. It covers key aspects of data retrieval, data selection criteria, issues in data reliability and validity such as governance token pricing complexities, discrepancy in treasuries, Mainnet and Testnet data, understanding the variety of DAO types and proposal categories, airdrops affecting governance, and the Sybil problem. The agenda aims to equip scholars with the essential knowledge required to conduct nuanced and rigorous academic studies on DAOs by illuminating these various aspects and proposing directions for future research.

Related papers

• The Blind Men and the Elephant: Mapping Interdisciplinarity in Research on Decentralized Autonomous Organizations [0.0]
  Decentralized Autonomous Organizations (DAOs) are attracting interdisciplinary interest, particularly in business, economics, and computer science. Research remains fragmented across disciplines, limiting a comprehensive understanding of their potential. Current research remains predominantly applied and case-driven, with limited theoretical integration.
  arXiv  Detail & Related papers  (2025-02-14T07:06:43Z)
• Ten Challenging Problems in Federated Foundation Models [55.343738234307544]
  Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency.
  arXiv  Detail & Related papers  (2025-02-14T04:01:15Z)
• Data governance: A Critical Foundation for Data Driven Decision-Making in Operations and Supply Chains [5.909817496975273]
  This study aims to call attention on Data Governance (DG) research in the field of operations and supply chain management (OSCM) Built upon three case studies, we exanimated and analyzed real life data issues in the industry. Four types of cause related to data issues were found: 1) human factors, 2) lack of written rules and regulations, 3) ineffective technological hardware and software, and 4) lack of resources.
  arXiv  Detail & Related papers  (2024-09-23T15:41:56Z)
• Navigating the Data Trading Crossroads: An Interdisciplinary Survey [33.64953318642493]
  Data has been increasingly recognized as a critical factor in the future economy. However, constructing an efficient data trading market faces challenges such as privacy breaches, data monopolies, and misuse. This paper aims to identify existing problems, research gaps, and propose potential solutions.
  arXiv  Detail & Related papers  (2024-07-16T08:07:16Z)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• Mapping STI ecosystems via Open Data: overcoming the limitations of conflicting taxonomies. A case study for Climate Change Research in Denmark [0.0]
  Science, Technology and Innovation (STI) decision-makers often need to have a clear vision of what is researched and by whom to design effective policies. A major challenge to be faced in this context is the difficulty in accessing the relevant data and in combining information coming from different sources. Here, we present a proof-of-concept study of the use of Open Resources to map the research landscape on the Sustainable Development Goal (SDG) 13-Climate Action, for an entire country, Denmark, and we map it on the 25 ERC panels.
  arXiv  Detail & Related papers  (2022-09-19T10:59:39Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification [75.49600684537117]
  Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness. We contribute a broad analysis of 13 fair classification approaches and additional variants, over their correctness, fairness, efficiency, scalability, and stability. Our analysis highlights novel insights on the impact of different metrics and high-level approach characteristics on different aspects of performance.
  arXiv  Detail & Related papers  (2021-01-18T22:55:40Z)
• Going Paperless -- Main Challenges in EDRMS Implementation -- Case of Georgia [0.0]
  This study is to inquire Electronic Documents and Records Management Systems (EDRMS) in the context of eGovernment. The centre of the investigation is howMS could raise efficiency in public service delivery. Different ICT adoption theories and case study examples were analysed, among which the Estonian case was taken as a successful model.
  arXiv  Detail & Related papers  (2020-10-07T06:37:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.