Correcting the error in getnetworkhashrateps

In the exploration of blockchain technology and its underlying mechanisms, the calculation of the total number of hashes in a given set of blocks emerges as a critical focus area. The formula for calculating this total, denoted by W, involves summing the product of the hashrate (Hi) of each block and the time it took to solve that block (ti). This method provides a direct measure of the computational effort expended over a series of blocks.

However, when contrasting the calculated number of hashes using the actual data from blocks (W) with an estimate derived from multiplying an estimated average hashrate by the total timespan (W_estimate), a discrepancy is noted. Theoretical analysis and experimental validation reveal that the estimate tends to be systematically lower by a factor of (N-1)/N, where N represents the number of blocks considered. This observation suggests that, despite the accuracy of the hashrate estimate, the estimated total number of hashes consistently underestimates the actual amount of computational work performed.

The resolution to this puzzle may lie in the nuanced understanding of observed timespans and their probabilistic implications. The first formula, representing a factual and predictive approach, assumes a straightforward accumulation of computational efforts across blocks. Conversely, the second formula, reflective of an observed outcome, incorporates the inherent uncertainty in estimating future outcomes based on past performance. This estimation essentially corrects for what could be described as "excess luck" in the occurrence of block solutions, aligning more closely with the principle that Proof of Work (PoW) should represent a verifiable record of computational work already completed rather than a speculative forecast of future work based on repetitive conditions.

This discussion underscores the complexity of accurately measuring and predicting computational efforts within blockchain networks. It highlights the need for a nuanced approach that accounts for the probabilistic nature of block solving and the importance of distinguishing between predictive models and observational data in the evaluation of network performance.