Next-generation protocol design for blockchain rewards with web-tool

Designing fair and efficient blockchain reward mechanisms requires going beyond raw execution time to account for behavioral variability. We present a simulation framework for evaluating BCRPs using entropy as a systems-level indicator of reward fairness and stability. Three strategies are assessed on simulated miner profiles [Formula: see text] with log-normal execution times, Laplace-distributed noise, and tercile-based complexity classes: a classical execution-time baseline, "Mining [Formula: see text]" (penalizing miner noise and task complexity), and "Adaptive [Formula: see text]" (Mining [Formula: see text] with exponential time decay). Reward distributions are summarized via KDE and ECDF and scored using Shannon, Rényi [Formula: see text], Tsallis [Formula: see text], and normalized Shannon entropies computed on discretized rewards ([Formula: see text] bins). An interactive Shiny application accompanies the method for reproducible exploration without programming. Across simulations, Adaptive [Formula: see text] yields the most behavior-sensitive and equitable allocations, achieving the lowest entropy on all four metrics. Quantitatively, relative to the Traditional baseline, Adaptive [Formula: see text] reduces entropy by [Formula: see text] (Shannon: [Formula: see text]), [Formula: see text] (Rényi-[Formula: see text]: [Formula: see text]), [Formula: see text] (Tsallis-[Formula: see text]: [Formula: see text]), and [Formula: see text] (Normalized: [Formula: see text]); Mining [Formula: see text] achieves intermediate improvements of [Formula: see text] and [Formula: see text] respectively. These results provide an evidence-based, deployable framework for evaluating reward fairness in decentralized systems.