How Present Bias and Memorylessness Shape Miner Behavior in Blockchain Algorithms

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A deep dive into how present bias, memoryless rules, and discount factors shape blockchain mining efficiency and algorithmic fairness.
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This section explores how present bias, memorylessness, and heterogeneous miner strategies affect optimal allocation algorithms in blockchain systems. It shows that while semi-myopic, memoryless algorithms can achieve deterministic upper bounds, their simplicity limits adaptability across varying discount factors (λ). The discussion also considers implications for miner coordination, discount interpretation, and alternative time-dependent models that could redefine blockchain efficiency and fairness.