文章基本信息

标题：Generalized Benford’s Law as a Lie Detector
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作者：Nicolas Gauvrit Gauvrit ; Jean-Charles Houillon ; Jean-Paul Delahaye 等
期刊名称：Advances in Cognitive Psychology
电子版ISSN：1895-1171
出版年度：2017
卷号：13
期号：2
页码：121-127
DOI：10.5709/acp-0212-x
语种：English
出版社：Prague Development Center
摘要：The first significant (leftmost nonzero) digit of seemingly random numbers often appears to conform to a logarithmic distribution, with more 1s than 2s, more 2s than 3s, and so forth, a phenomenon known asBenford’s law. When humans try to produce random numbers, they often fail to conform to this distribution. This feature grounds the so-calledBenford analysis, aiming at detecting fabricated data. A generalized Benford’s law (GBL), extending the classical Benford’s law, has been defined recently. In two studies, we provide some empirical support for the generalized Benford analysis, broadening the classical Benford analysis. We also conclude that familiarity with the numerical domain involved as well as cognitive effort only have a mild effect on the method’s accuracy and can hardly explain the positive results provided here.
关键词：engeneralized Benford’s lawfraud detectionBenford analysis