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

Area of Science:

• Blockchain Technology
• Computer Science
• Economics

Background:

• Designing effective blockchain reward mechanisms is complex.
• Existing methods often overlook behavioral variability among miners.
• Fairness and stability are critical for decentralized systems.

Purpose of the Study:

• To develop and evaluate a simulation framework for blockchain reward mechanisms.
• To assess reward fairness and stability using entropy as a key metric.
• To compare the performance of different reward allocation strategies.

Main Methods:

• A simulation framework was developed to model miner behavior with log-normal execution times and varying task complexity.
• Three reward strategies were evaluated: a traditional baseline, Mining-X, and Adaptive-X.
• Reward distributions were analyzed using Kernel Density Estimation (KDE) and Empirical Cumulative Distribution Functions (ECDF).
• Entropy metrics (Shannon, Rényi, Tsallis, normalized Shannon) were computed on discretized reward distributions.
• An interactive Shiny application was created for reproducible analysis.

Main Results:

• The Adaptive-X strategy demonstrated superior performance, yielding the most behavior-sensitive and equitable reward allocations.
• Adaptive-X achieved the lowest entropy across all four measured metrics compared to the baseline and Mining-X.
• Relative to the traditional baseline, Adaptive-X significantly reduced entropy, with Shannon entropy decreasing by 41.5% and Normalized Shannon entropy by 35.2%.

Conclusions:

• The proposed simulation framework provides a robust method for evaluating blockchain reward mechanisms.
• The Adaptive-X strategy emerges as a highly effective approach for enhancing fairness and stability in decentralized systems.
• This work offers an evidence-based, deployable solution for optimizing reward allocation in blockchains.