Median fairness, in computational contexts, refers to a criterion where the median performance or outcome across a set of participants is prioritized or used as a benchmark for equitable distribution. This concept aims to mitigate the impact of extreme outliers, ensuring that the typical participant experiences a just result. It is applied in resource allocation or reward distribution mechanisms within decentralized systems.
Context
The concept of median fairness is gaining relevance in the design of decentralized protocols, particularly in areas like validator selection, transaction ordering, and resource distribution within blockchain networks. Discussions focus on how to mathematically define and implement fairness metrics that prevent undue advantage or disadvantage to specific participants. Ongoing research aims to develop robust algorithms that can achieve median fairness efficiently, thereby enhancing the perceived impartiality and stability of decentralized systems.
This work introduces a Decentralized Clock Network that separates transaction ordering from consensus, using timestamp agreement to enforce δ-Median Fairness and mitigate front-running.
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