Competitive Revenue Extraction from Time-Discounted Transactions in the Semi-Myopic Regime
<|reference_start|>Competitive Revenue Extraction from Time-Discounted Transactions in the Semi-Myopic Regime: Decentralized cryptocurrencies are payment systems that rely on aligning the incentives of users and miners to operate correctly and offer a high quality of service to users. Recent literature studies the mechanism design problem of the auction serving as a cryptocurrency's transaction fee mechanism (TFM). We find that a non-myopic modelling of miners falls close to another well-known problem: that of online buffer management for packet switching. The main difference is that unlike packets which are of a fixed size throughout their lifetime, in a financial environment, user preferences (and therefore revenue extraction) may be time-dependent. We study the competitive ratio guarantees given a certain discount rate, and show how existing methods from packet scheduling, which we call "the undiscounted case", perform suboptimally in the more general discounted setting. Most notably, we find a novel, simple, memoryless, and optimal deterministic algorithm for the semi-myopic case, when the discount factor is up to ~0.770018. We also present a randomized algorithm that achieves better performance than the best possible deterministic algorithm, for any discount rate.<|reference_end|>
@article{gafni2024competitive,
    title={Competitive Revenue Extraction from Time-Discounted Transactions in the
  Semi-Myopic Regime},
    author={Yotam Gafni and Aviv Yaish},
    journal={arXiv preprint arXiv:2402.08549},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08549},
    primaryClass={cs.GT econ.TH}
}