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Self-Generated Measures and the Centroid Rigidity of Power Laws

Vincent E. Coll, Jr. abd Lee B. Whitt

TL;DR

The paper investigates whether the elementary centroid relation $\bar{y}(a)=\lambda f(\bar{x}(a))$ can hold for all truncation lengths $a>0$. It recasts the problem in a scale-free probabilistic framework, interpreting centroid moments as expectations under a self-generated measure and deriving an equality in expectation across scales. The main result shows that the only admissible functions are power laws $f(x)=A x^{p}$ with $p>0$, accompanied by an explicit $\lambda=\frac{p+1}{2(2p+1)}\left(\frac{p+2}{p+1}\right)^{p}$; the proof proceeds by differentiating the scale-free identity to obtain a weighted-mean form for the elasticity $E(x)=x f'(x)/f(x)$ and then a second differentiation enforces constant elasticity via a vanishing-variance (Cauchy–Schwarz) argument. This yields a self-contained, elementary rigidity result that connects a basic calculus centroid identity to the global functional form, and it extends to broader regularity assumptions on $f$.

Abstract

We revisit a classical calculus computation: the centroid of the subgraph of a function on the interval from 0 to a, and show that it hides a rigidity theorem. Let f be twice continuously differentiable on (0, infinity), take values in (0, infinity), and satisfy f(0+) = 0. Define xbar(a) as (integral from 0 to a of x f(x) dx) divided by (integral from 0 to a of f(x) dx), and define ybar(a) as (1/2) times (integral from 0 to a of f(x)^2 dx) divided by (integral from 0 to a of f(x) dx). We prove that the Geometric Scaling Property, namely the identity ybar(a) = lambda * f(xbar(a)) for every a > 0, holds if and only if f(x) = A * x^p with A > 0 and p > 0. For these power laws the optimal constant is lambda = (p+1)/(2(2p+1)) * ((p+2)/(p+1))^p. After a scale-free normalization, the proof is probabilistic: with the self-generated probability measure on (0, a) having density proportional to f, we have xbar(a) equal to the expected value of Xa and ybar(a) equal to (1/2) times the expected value of f(Xa), so the Geometric Scaling Property becomes an equality in expectation across all truncation scales. Differentiating with respect to a yields a weighted mean identity for the elasticity E(x) = x f'(x) / f(x); a second differentiation forces a vanishing variance principle that makes E constant, hence f a pure power, and the stated value of lambda follows. The argument uses no asymptotics and extends to f that is once continuously differentiable on (0, infinity) with locally Lipschitz elasticity.

Self-Generated Measures and the Centroid Rigidity of Power Laws

TL;DR

The paper investigates whether the elementary centroid relation can hold for all truncation lengths . It recasts the problem in a scale-free probabilistic framework, interpreting centroid moments as expectations under a self-generated measure and deriving an equality in expectation across scales. The main result shows that the only admissible functions are power laws with , accompanied by an explicit ; the proof proceeds by differentiating the scale-free identity to obtain a weighted-mean form for the elasticity and then a second differentiation enforces constant elasticity via a vanishing-variance (Cauchy–Schwarz) argument. This yields a self-contained, elementary rigidity result that connects a basic calculus centroid identity to the global functional form, and it extends to broader regularity assumptions on .

Abstract

We revisit a classical calculus computation: the centroid of the subgraph of a function on the interval from 0 to a, and show that it hides a rigidity theorem. Let f be twice continuously differentiable on (0, infinity), take values in (0, infinity), and satisfy f(0+) = 0. Define xbar(a) as (integral from 0 to a of x f(x) dx) divided by (integral from 0 to a of f(x) dx), and define ybar(a) as (1/2) times (integral from 0 to a of f(x)^2 dx) divided by (integral from 0 to a of f(x) dx). We prove that the Geometric Scaling Property, namely the identity ybar(a) = lambda * f(xbar(a)) for every a > 0, holds if and only if f(x) = A * x^p with A > 0 and p > 0. For these power laws the optimal constant is lambda = (p+1)/(2(2p+1)) * ((p+2)/(p+1))^p. After a scale-free normalization, the proof is probabilistic: with the self-generated probability measure on (0, a) having density proportional to f, we have xbar(a) equal to the expected value of Xa and ybar(a) equal to (1/2) times the expected value of f(Xa), so the Geometric Scaling Property becomes an equality in expectation across all truncation scales. Differentiating with respect to a yields a weighted mean identity for the elasticity E(x) = x f'(x) / f(x); a second differentiation forces a vanishing variance principle that makes E constant, hence f a pure power, and the stated value of lambda follows. The argument uses no asymptotics and extends to f that is once continuously differentiable on (0, infinity) with locally Lipschitz elasticity.
Paper Structure (4 sections, 4 theorems, 35 equations)

This paper contains 4 sections, 4 theorems, 35 equations.

Key Result

Theorem 4.1

Assume the Geometric Scaling Propertyeq:GSP-shape. Then there exist $A>0$ and $p>0$ such that $f(x)=A\,x^{p}$ for all $x>0$, and

Theorems & Definitions (8)

  • Theorem 4.1
  • proof
  • Remark 4.2: Regularity and finiteness
  • Remark 4.3: Weaker hypotheses
  • Lemma 4.4
  • Lemma 4.5: Regularity of $A,B,C,\theta$ and closed forms
  • Lemma 4.6: Weighted-mean and variance identities
  • Remark 4.7: Converse for power laws