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

Abstract

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 $$\:(\alpha\:=2)$$ , 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: $$\:2.684\to\:1.678$$ ), $$\:37.1\%$$ (Rényi- $$\:2$$ : $$\:2.218\to\:1.396$$ ), $$\:21.0\%$$ (Tsallis- $$\:2$$ : $$\:0.785\to\:0.620$$ ), and $$\:14.6\%$$ (Normalized: $$\:0.847\to\:0.723$$ ); 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.