Benford behavior resulting from stick and box fragmentation processes
Bruce Fang, Steven J. Miller

TL;DR
This paper investigates how stick and box fragmentation processes naturally lead to Benford's law, providing conditions for strong Benford behavior and confirming conjectures about high-dimensional box models using advanced mathematical tools.
Contribution
It establishes necessary and sufficient conditions for strong Benford behavior in fragmentation models and confirms a conjecture on high-dimensional box fragmentation.
Findings
Stick fragmentation lengths converge to strong Benford behavior under certain conditions.
High-dimensional box faces exhibit volume distributions following Benford's law.
Quantifies discrepancy from uniformity using irrationality exponents.
Abstract
Benford's law is the statement that in many real world data sets, the probability of having digit in base as the first digit is \log_{B}\!\left(\frac{d+1}{d}\right) for all . We sometimes refer to this as weak Benford behavior, and we say that a data set satisfies strong Benford behavior in base if the probability of having significand at most is \log_{B}\!\left(s\right) for all , . We examine Benford behaviors in two different probabilistic models: stick and box fragmentation models. Building on the work arXiv:1309.5603 on the single proportion stick fragmentation model, we employ combinatorial identities on multinomial coefficients to reduce the multi-proportion stick fragmentation model to the single proportion model. We then provide a necessary and sufficient condition for the lengths of the stick fragments to converge to strong Benford…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBenford’s Law and Fraud Detection · Academic integrity and plagiarism
