Design of blockchain applications and its impact on user behaviors

Network structure is essential for understanding the interactions and overall behavior of complex systems. Over the past decade, blockchain networks have attracted over 3 trillion dollars in assets, with hundreds of millions of users worldwide. This research seeks to explore the factors driving...

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書目詳細資料
主要作者: Chen, Erdong
其他作者: Wen Yonggang
格式: Thesis-Doctor of Philosophy
語言:English
出版: Nanyang Technological University 2024
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在線閱讀:https://hdl.handle.net/10356/181233
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總結:Network structure is essential for understanding the interactions and overall behavior of complex systems. Over the past decade, blockchain networks have attracted over 3 trillion dollars in assets, with hundreds of millions of users worldwide. This research seeks to explore the factors driving such widespread adoption, particularly in the context of decentralized applications. It addresses key questions: What motivates users to adopt blockchain technology? How does trust propagate through these networks? And how can we better understand and predict the value dynamics within these rapidly evolving ecosystems? Additionally, the study examines how decentralized systems influence user behavior across various platforms. The thesis also investigates trader behavior in perpetual futures contracts through a Systematization of Knowledge (SoK) approach. In 2023, the 24-hour trading volume of perpetual futures contracts across all exchanges exceeded 100 billion dollars, accounting for more than half of total trading volume across blockchain products and exchanges. This raises an important question: Why do users trust decentralized applications with such significant sums of money? The research examines trader dynamics on centralized (CEXs) and decentralized exchanges (DEXs), proposing four models to classify exchange operations. Empirical analysis shows that the relationship between market volatility and trader activity varies significantly based on the exchange’s mechanical design. Moreover, it reveals that in Virtual Automated Market Making (VAMM) models, open interest impacts price volatility differently for long and short positions.