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Optimal choice of granularity in commonsense estimation: Why half‐orders of magnitude?
Author(s) -
Hobbs Jerry R.,
Kreinovich Vladik
Publication year - 2006
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20164
Subject(s) - granularity , computer science , scale (ratio) , magnitude (astronomy) , order (exchange) , artificial intelligence , physics , economics , geography , cartography , finance , astronomy , operating system
It has been observed that when people make crude estimates, they feel comfortable choosing between alternatives that differ by a half‐order of magnitude (e.g., were there 100, 300, or 1000 people in the crowd?) and less comfortable making a choice on a more detailed scale, with finer granules, or on a coarser scale (like 100 or 1000). In this article, we describe two models of choosing granularity in commonsense estimates, and we show that for both models, in the optimal granularity, the next estimate is three to four times larger than the previous one. Thus, these two optimization results explain the commonsense granularity. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 843–855, 2006.