  Relatedness is a quantification of how much two human activities are similar
in terms of the inputs and contexts needed for their development. Under the
idea that it is easier to move between related activities than towards
unrelated ones, empirical approaches to quantify relatedness are currently used
as predictive tools to inform policies and development strategies in
governments, international organizations, and firms. Here we focus on
countries' industries and we show that the standard, widespread approach of
estimating Relatedness through the co-location of activities (e.g. Product
Space) generates a measure of relatedness that performs worse than trivial
auto-correlation prediction strategies. We argue that this is a consequence of
the poor signal-to-noise ratio present in international trade data. In this
paper we show two main findings. First, we find that a shift from two-products
correlations (network-density based) to many-products correlations (decision
trees) can dramatically improve the quality of forecasts with a corresponding
reduction of the risk of wrong policy choices. Then, we propose a new
methodology to empirically estimate Relatedness that we call Continuous
Projection Space (CPS). CPS, which can be seen as a general network embedding
technique, vastly outperforms all the co-location, network-based approaches,
while retaining a similar interpretability in terms of pairwise distances.
