Future of Algorithmic Organization: Large-Scale Analysis of Decentralized Autonomous Organizations (DAOs)

• URL: http://arxiv.org/abs/2410.13095v1
• Date: Wed, 16 Oct 2024 23:45:10 GMT
• Title: Future of Algorithmic Organization: Large-Scale Analysis of Decentralized Autonomous Organizations (DAOs)
• Authors: Tanusree Sharma, Yujin Potter, Kornrapat Pongmala, Henry Wang, Andrew Miller, Dawn Song, Yang Wang,
• Abstract summary: Decentralized Autonomous Organizations (DAOs) resemble early online communities, particularly those centered around open-source projects. In just a few years, the deployment of governance tokens surged with a total of $24.5 billion and 11.1M governance token holders collectively managing decisions across over 13,000s as of 2024. We examine factors such as voting power, participation, and characteristics dictating the level of decentralization, thus, the efficiency of management structures.
• Score: 45.02792904507959
• License:
• Abstract: Decentralized Autonomous Organizations (DAOs) resemble early online communities, particularly those centered around open-source projects, and present a potential empirical framework for complex social-computing systems by encoding governance rules within "smart contracts" on the blockchain. A key function of a DAO is collective decision-making, typically carried out through a series of proposals where members vote on organizational events using governance tokens, signifying relative influence within the DAO. In just a few years, the deployment of DAOs surged with a total treasury of $24.5 billion and 11.1M governance token holders collectively managing decisions across over 13,000 DAOs as of 2024. In this study, we examine the operational dynamics of 100 DAOs, like pleasrdao, lexdao, lootdao, optimism collective, uniswap, etc. With large-scale empirical analysis of a diverse set of DAO categories and smart contracts and by leveraging on-chain (e.g., voting results) and off-chain data, we examine factors such as voting power, participation, and DAO characteristics dictating the level of decentralization, thus, the efficiency of management structures. As such, our study highlights that increased grassroots participation correlates with higher decentralization in a DAO, and lower variance in voting power within a DAO correlates with a higher level of decentralization, as consistently measured by Gini metrics. These insights closely align with key topics in political science, such as the allocation of power in decision-making and the effects of various governance models. We conclude by discussing the implications for researchers, and practitioners, emphasizing how these factors can inform the design of democratic governance systems in emerging applications that require active engagement from stakeholders in decision-making.

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