A New Architecture and Mechanism for Decentralized Science MetaMarkets

【Author】 Ding, Wenwen; Li, Juanjuan; Qin, Rui; Kozma, Robert; Wang, Fei-Yue

【Source】IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

【影响因子】11.471

【Abstract】The new generation of digital intelligence technology enables knowledge creation, dissemination, and application to undergoing parallel changes. Scientific systems face an increasingly uncertain, diverse, and complex environment, making adopting multidisciplinary, interdisciplinary, and transdisciplinary approaches to research issues inevitable. Existing scientific systems follow linear value streams, leading to problems, such as inefficiency, unfairness, and knowledge monopoly. Decentralized science (DeSci) is a new scientific development paradigm based on Web3, Metaverses, and decentralized autonomous organizations and operations (DAOs) technologies, that can solve organizational and management problems in scientific systems through organizing, coordinating, and executing techniques. However, new economic theories and methods are still needed to effectively solve the problem of linear value flow in scientific systems. Metaeconomics based on the parallel intelligence theory, also known as decentralized economics (DeEco), provides a new approach and idea for redesigning the economic system of scientific markets. Thus, this article proposes a research framework and core mechanisms of DeSci MetaMarkets based on parallel economic theory to provide effective and practical methodologies for scientific system governance.

【Keywords】Economics; Production; Knowledge engineering; Organizations; Complex systems; Personnel; Monopoly; Decentralized autonomous organizations and operations (DAOs); decentralized autonomous operations and organizations; decentralized science (DeSci); decentralized economics (DeEco); decentralized management (DeMana); DeSci; metaeconomics; MetaMarkets; metaverses; parallel intelligence; Web3

【发表时间】2023 2023 MAY 11

【收录时间】2023-06-04

【文献类型】理论模型

【主题类别】

区块链应用-虚拟经济-DeSci