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 $$\:(n=100)$$ 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 \:(α\:=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.