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This section employs Monte Carlo simulations to evaluate blockchain reputation systems. It introduces the technique, explores its application in structural reliability, and details scenarios with uniform and power-law credit point distributions. Results reveal dynamics in credit point variations, stake-to-reward relationships, and the impact of adaptive learning models. The simulations also consider skipping rounds, non-random consumer selection, and contribution incentive mechanisms, providing comprehensive insights into the intricacies of the proposed reputation system.

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