The following is taken from Sornette's book.

Consider a random variable, ${\displaystyle X}$ , that takes on values ${\displaystyle x_{i}}$  with probability ${\displaystyle p_{i}}$ . Furthermore, let’s assume for another parameter ${\displaystyle r_{i}}$ 

${\displaystyle x_{i}=r_{i}^{-\beta }}$ 

for some fixed ${\displaystyle \beta }$ . We then want to minimize

${\displaystyle L=\sum _{i=0}^{N-1}p_{i}x_{i}}$ 

subject to the constraint

${\displaystyle \sum _{i=0}^{N-1}r_{i}=\kappa }$ 

Using Lagrange multipliers, this gives

${\displaystyle p_{i}\propto x_{i}^{-(1+1/\beta )}}$ 

giving us a power law. The global optimization of minimizing the energy along with the power law dependence between ${\displaystyle x_{i}}$  and ${\displaystyle r_{i}}$  gives us a power law distribution in probability.

• self-organized criticality

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