The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the ne...The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’attention.First,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is adopted.Then,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as followers.Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing.Moreover,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is demonstrated.Finally,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different conditions.This study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.展开更多
From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovati...From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified style.We further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ChatGPT.Second,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.展开更多
Despite the increase in the number of blockchain-based Decentralized Autonomous Organizations(DAOs),there is no consensus on what constitutes a DAO.This paper provides an in-depth study of DAOs by analyzing their defi...Despite the increase in the number of blockchain-based Decentralized Autonomous Organizations(DAOs),there is no consensus on what constitutes a DAO.This paper provides an in-depth study of DAOs by analyzing their definitions,characteristics,and emerging developments.Existing definitions in the literature hardly recognize common functionalities and intermingle coded DAOs,DAO deployment platforms,and blockchain DAOs.We developed a comprehensive DAO definition by reviewing the literature and empirically analyzing 1,859 DAOs.The findings show that many DAOs were inactive and that a threshold of 20 tokenholders is a tipping point for DAOs to survive over time and maintain sustained levels of activity.Finally,based on an empirical analysis of 9,845 perceived DAOs,we identified the emerging development of off-chain voting.This emerging development challenges the autonomous nature of DAOs.We recommend further research to investigate the effect of governance structures on their long-term sustainability and viability for both on-chain and off-chain DAOs.展开更多
Context:Decentralized autonomous organizations are a new form of smart contract-based governance.Decentralized autonomous organization platforms,which support the creation of such organizations,are becoming increasing...Context:Decentralized autonomous organizations are a new form of smart contract-based governance.Decentralized autonomous organization platforms,which support the creation of such organizations,are becoming increasingly popular,such as Aragon and Colony.Selecting the best fitting platform is challenging for organizations,as a significant number of decision criteria,such as popularity,developer availability,governance issues and consistent documentation of such platforms,should be considered.Additionally,decision-makers at the organizations are not experts in every domain,so they must continuously acquire volatile knowledge regarding such platforms.Objective:Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this study.We aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision model.Method:This study presents a decision model for the decentralized autonomous organization platform selection problem.The decision model captures knowledge regarding such platforms and concepts systematically.This model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software production.Results:We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers.The case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and effort.Conclusion:We observe in the empirical evidence from the case studies that decision-makers can make more rational,efficient,and effective decisions with the decision model.Furthermore,the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be employed by other researchers in their future investigations.展开更多
以ChatGPT为代表的新一轮人工智能技术浪潮正推动人类社会全面变革,科学研究范式正加速转换,一场人工智能驱动的科学研究(AI for Science,AI4S)革命正在到来。分析了AI4S的基本概念和特点,从数学、物理、生物、材料等角度简要综述了AI4...以ChatGPT为代表的新一轮人工智能技术浪潮正推动人类社会全面变革,科学研究范式正加速转换,一场人工智能驱动的科学研究(AI for Science,AI4S)革命正在到来。分析了AI4S的基本概念和特点,从数学、物理、生物、材料等角度简要综述了AI4S的发展现状。大力发展AI4S对提高国家竞争力、发展社会经济、加强技术储备都具有十分重要的意义。为更好地推动我国AI4S的发展,以下两点十分关键:一是变革当代的教学与教育,倡导AI for Education和Education for AI;二是以DAOs和DeSci为基础建立适应“新科学研究范式”的“新组织方式”和“新科研生态”,为AI4S研究提供公开、公平、公正的可持续性支持。展开更多
基金supported by the National Natural Science Foundation of China(62103411)the Science and Technology Development Fund of Macao SAR(0093/2023/RIA2,0050/2020/A1)。
文摘The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’attention.First,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is adopted.Then,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as followers.Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing.Moreover,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is demonstrated.Finally,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different conditions.This study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.
基金This work was supported by the National Key Research and Development Program of China(2020YFB2104001)the National Natural Science Foundation of China(62271485,61903363,U1811463)Open Project of the State Key Laboratory for Management and Control of Complex Systems(20220117).
文摘From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified style.We further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ChatGPT.Second,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.
文摘Despite the increase in the number of blockchain-based Decentralized Autonomous Organizations(DAOs),there is no consensus on what constitutes a DAO.This paper provides an in-depth study of DAOs by analyzing their definitions,characteristics,and emerging developments.Existing definitions in the literature hardly recognize common functionalities and intermingle coded DAOs,DAO deployment platforms,and blockchain DAOs.We developed a comprehensive DAO definition by reviewing the literature and empirically analyzing 1,859 DAOs.The findings show that many DAOs were inactive and that a threshold of 20 tokenholders is a tipping point for DAOs to survive over time and maintain sustained levels of activity.Finally,based on an empirical analysis of 9,845 perceived DAOs,we identified the emerging development of off-chain voting.This emerging development challenges the autonomous nature of DAOs.We recommend further research to investigate the effect of governance structures on their long-term sustainability and viability for both on-chain and off-chain DAOs.
基金funded in part by the HTSM HiTMaT Grant entitled"SearchSECO"。
文摘Context:Decentralized autonomous organizations are a new form of smart contract-based governance.Decentralized autonomous organization platforms,which support the creation of such organizations,are becoming increasingly popular,such as Aragon and Colony.Selecting the best fitting platform is challenging for organizations,as a significant number of decision criteria,such as popularity,developer availability,governance issues and consistent documentation of such platforms,should be considered.Additionally,decision-makers at the organizations are not experts in every domain,so they must continuously acquire volatile knowledge regarding such platforms.Objective:Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this study.We aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision model.Method:This study presents a decision model for the decentralized autonomous organization platform selection problem.The decision model captures knowledge regarding such platforms and concepts systematically.This model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software production.Results:We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers.The case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and effort.Conclusion:We observe in the empirical evidence from the case studies that decision-makers can make more rational,efficient,and effective decisions with the decision model.Furthermore,the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be employed by other researchers in their future investigations.
文摘以ChatGPT为代表的新一轮人工智能技术浪潮正推动人类社会全面变革,科学研究范式正加速转换,一场人工智能驱动的科学研究(AI for Science,AI4S)革命正在到来。分析了AI4S的基本概念和特点,从数学、物理、生物、材料等角度简要综述了AI4S的发展现状。大力发展AI4S对提高国家竞争力、发展社会经济、加强技术储备都具有十分重要的意义。为更好地推动我国AI4S的发展,以下两点十分关键:一是变革当代的教学与教育,倡导AI for Education和Education for AI;二是以DAOs和DeSci为基础建立适应“新科学研究范式”的“新组织方式”和“新科研生态”,为AI4S研究提供公开、公平、公正的可持续性支持。
