Suppose we have three  sets $A, B,C$ and a span $S := A \leftarrow C \rightarrow B$.  There is a special case when the data of the span $S$ exactly specifies a function $f: A  \rightarrow B$.  In every other case, we have some data that may be used to define a function.  It's a bit like a probabilistic model of a function or "data" about a function.  What I am wondering is whether or not we can do something like a regression that takes spans to functions in some precise way, the same way one might regress a linear function between two data columns.  I have gone back and forth as to whether this is possible.  Since each arm of the span can be seen as a multiset, we might have a notion of addition over the range elements like this:

$  \{ (a,b), (a,c),(a,b),(a,c) \} \rightarrow \{ 2(a,b), 2(a,c) \} $

Or

$ \{ (a,b), (a,c),(a,b),(a,c) \} \rightarrow  (a, \{ 2b, 2c \} )$

What we don't have is a way to map a mixture of ordered pairs to a single ordered pair thus producing the critical regression.

This is reminiscent of the Kleisli category of the multiset monad which is what I was thinking about before landing on this specific question in this post.  There is a natural transformation from the mulitset monad to the monad of measures of finite support, which itself has a Kleisli category.  Perhaps there is a way to push the notion of averaging from the measures monad Kleisli Cat down to the Kleisli Cat of the multiset monad.

Edit:
A span does not have to give data about every element of the domain, and since there is no information like a metric or topology to extend local data to neighbouring elements, it seems like this is impossible.  However, perhaps we can define a partial function from the data based on just those elements in the domain that the span addresses.