Abstract
Random multiplicative processes \$w\_t =łambda\_1 łambda\_2 ... łambda\_t\$
(with < łambda\_j > 0 ) lead, in the presence of a boundary constraint, to a
distribution \$P(w\_t)\$ in the form of a power law \$w\_t^-(1+\mu)\$. We provide a
simple and physically intuitive derivation of this result based on a random
walk analogy and show the following: 1) the result applies to the asymptotic
(\$t ınfty\$) distribution of \$w\_t\$ and should be distinguished from the
central limit theorem which is a statement on the asymptotic distribution of
the reduced variable \$1 t(log w\_t -< log w\_t >)\$; 2) the
necessary and sufficient conditions for \$P(w\_t)\$ to be a power law are that
<log łambda\_j > < 0 (corresponding to a drift \$w\_t 0\$) and that \$w\_t\$ not
be allowed to become too small. We discuss several models, previously
unrelated, showing the common underlying mechanism for the generation of power
laws by multiplicative processes: the variable \$w\_t\$ undergoes a random
walk biased to the left but is bounded by a repulsive ''force''. We give an
approximate treatment, which becomes exact for narrow or log-normal
distributions of \$łambda\$, in terms of the Fokker-Planck equation. 3) For all
these models, the exponent \$\mu\$ is shown exactly to be the solution of
\$łambda^\mu = 1\$ and is therefore non-universal and depends
on the distribution of \$łambda\$.
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