In his papers, J. Hobbs has observed that when people make crude estimates, they usually feel reasonably comfortable choosing between alternatives which differ by a half order of magnitude (HOM). He also provided an explanation for this level of granularity based on the need for the resulting crude estimates to represent both the original data and the result of processing this data. According to this explanation, HOM are optimal -- when we limit ourselves to these first crude estimates.
In many practical situations, we do not stop with the original estimate, we refine it one or more times by using granules of smaller and smaller size. In this paper, we show that the need to optimally process such refined estimates leads to the same HOM granularity. Thus, we provide a new explanation for this level of granularity.