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Titlebook: Mathematical Research for Blockchain Economy; 4th International Co Panos Pardalos,Ilias Kotsireas,Stefanos Leonardos Conference proceedings

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發(fā)表于 2025-3-21 18:24:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Mathematical Research for Blockchain Economy
副標(biāo)題4th International Co
編輯Panos Pardalos,Ilias Kotsireas,Stefanos Leonardos
視頻videohttp://file.papertrans.cn/627/626572/626572.mp4
概述Provides mathematical perspectives on blockchain research problems.Bridges the gaps between practice and theory.Collects state-of-the-art research in the areas of blockchain and cryptocurrencies
叢書(shū)名稱(chēng)Lecture Notes in Operations Research
圖書(shū)封面Titlebook: Mathematical Research for Blockchain Economy; 4th International Co Panos Pardalos,Ilias Kotsireas,Stefanos Leonardos Conference proceedings
描述.This book presents the best papers from the 4th International Conference on Mathematical Research for Blockchain Economy (MARBLE) 2023, held in London, UK. While most blockchain conferences and forums are dedicated to business applications, product development, or Initial Coin Offering (ICO) launches, this conference focused on the mathematics behind blockchain to bridge the gap between practice and theory...The book spans the divide between theoretical promise and practical reality in blockchain technology and explores the challenges hindering its real-world integration across diverse sectors, offering comprehensive insights into issues like scalability, security, and privacy.?.
出版日期Conference proceedings 2023
關(guān)鍵詞Blockchain; Cryptocurrencies; Bitcoin; Ethereum; Decentralized Finance; Game Theory; Cryptoeconomics; Smart
版次1
doihttps://doi.org/10.1007/978-3-031-48731-6
isbn_softcover978-3-031-48733-0
isbn_ebook978-3-031-48731-6Series ISSN 2731-040X Series E-ISSN 2731-0418
issn_series 2731-040X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,Deep Reinforcement Learning-Based Rebalancing Policies for?Profit Maximization of?Relay Nodes in?Pation of the node’s fortune over time over all rebalancing policies, and approximate the optimal solution by designing a Deep Reinforcement Learning (DRL)-based rebalancing policy. We build a discrete event simulator of the system and use it to demonstrate the DRL policy’s superior performance under
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,Game-Theoretic Randomness for?Proof-of-Stake,ndom result. However, in this case, there is no guarantee that the numbers provided by the parties are uniformly random and there is no incentive for the parties to honestly generate uniform randomness. Most random beacons have both limitations. In this work, we provide a protocol for distributed ge
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Characterizing Common Quarterly Behaviors in DeFi Lending Protocols,ccounts in Aave and the highest-risk kinds of behavior on the platform. Additionally, we use the temporal aspect of the clusters to track how common behaviors change through time and how usage has shifted in the wake of major events that impacted the crypto market, and we show that there seem to be
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An Automated Market Maker Minimizing Loss-Versus-Rebalancing,ol and block producer in a way that is proven to remain incentive compatible for the block producer. Given the participation of competing arbitrageurs to capture LVR, LVR is minimized in Diamond. We formally prove this result, and detail an implementation of Diamond. We also provide comparative simu
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,Exploring Decentralized Governance: A Framework Applied to?Compound Finance, a comprehensive understanding of the entire protocol. To demonstrate this method, this paper uses the Compound Finance protocol as a case study. The first sub-system that this research discusses is the delegated and self-delegated wallets which are the only entities that can participate in the voti
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