Abstract 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 \: (n=100) with log-normal execution times, Laplace-distributed noise, and tercile-based complexity classes: a classical execution-time baseline, “Mining \: 2. 0 ” (penalizing miner noise and task complexity), and “Adaptive \: 2. 0 ” (Mining \: 2. 0 with exponential time decay). Reward distributions are summarized via KDE and ECDF and scored using Shannon, Rényi \: (\: =2), Tsallis \: (q=2), and normalized Shannon entropies computed on discretized rewards (\: 20 bins). An interactive Shiny application accompanies the method for reproducible exploration without programming. Across simulations, Adaptive \: 2. 0 yields the most behavior-sensitive and equitable allocations, achieving the lowest entropy on all four metrics. Quantitatively, relative to the Traditional baseline, Adaptive \: 2. 0 reduces entropy by \: 37. 5\% (Shannon: \: 2. 684\: 1. 678), \: 37. 1\% (Rényi- \: 2: \: 2. 218\: 1. 396), \: 21. 0\% (Tsallis- \: 2: \: 0. 785\: 0. 620), and \: 14. 6\% (Normalized: \: 0. 847\: 0. 723) ; Mining \: 2. 0 achieves intermediate improvements of \: 29. 8\%, \: 31. 7\%, \: 17. 2\%, and \: 13. 9\%, respectively. These results provide an evidence-based, deployable framework for evaluating reward fairness in decentralized systems.
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