[The introduction of post is mirrored here, but the full tutorial is on IPython Notebook Viewer.]{style="font-size: 18pt;"}

::: {#notebook .border-box-sizing tabindex="-1"} ::: {#notebook-container .container} ::: {.cell .border-box-sizing .text_cell .rendered} ::: {.inner_cell} ::: {.text_cell_render .border-box-sizing .rendered_html} Method of Reflections Explained and Exampled in Python ======================================================

See how the Method of Reflections evolves as a recursive process.

The Method of Reflection (MOR) is a algorithm first coming out of macroeconomics, that ranks nodes in a bi-partite network. This notebook should hopefully help you implement the *method of reflection* in python. To be precise, it is the modified algorithm that is proposed by Caldarelli et al., which solves some problems with the original Hidalgo-Hausmann (HH) algorithm doi:10.1073/pnas.0900943106. The main problem with (HH) is that all values converge to a single fixed point after sufficiently many iterations. The Caldarelli version solves this by adding a new term to the recursive equation - what they call a *biased random walker* (function *G*). doi: 10.1371/journal.pone.0047278 . I hadn't seen any open-source implementations of this algorithm, so I thought I'd share my naïve approach.
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Read on at http://nbviewer.ipython.org/github/notconfusing/wiki_econ_capability/blob/master/Method%20of%20Reflections%20Explained%20and%20Exampled.ipynb ::: :::