Abstract models are simplified representations of complex systems used for analysis and prediction. They distill essential components and relationships, omitting extraneous details to facilitate understanding of underlying mechanisms within digital asset ecosystems. These conceptual frameworks aid in dissecting market dynamics, protocol designs, and economic incentives that influence cryptocurrency behavior.
Context
The discourse surrounding abstract models in finance and technology frequently centers on their utility in forecasting systemic risks and evaluating the potential impact of new protocols or regulatory shifts. Discussions often highlight the trade-offs between model fidelity and tractability, as well as the challenges in calibrating these representations to real-world data for actionable insights into digital asset markets.
This research establishes a foundational, abstract model for Maximal Extractable Value, enabling rigorous security proofs and advancing blockchain integrity.
This research establishes a formal theory of Maximal Extractable Value, providing a robust framework for understanding and mitigating economic attacks on blockchains.
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