How to present a model which is based on the result not yet discussed? I am facing the following problem while writing a paper. I have a model which contains a function whose explicit form I derived empirically from the data presented in the paper itself. 

However, I would like to present the model during the introduction and thus before the result. However, to justify the shape of that function, I have no other way than to anticipate the result (saying something like: “Since in the result of the experiment we found X, we empirically assumed X in the model“). I am not completely happy with this, because I would like to present the result directly in the result session, not earlier.

A solution would be to move the explanation of the model after the results. I do not like this either because I want to fit the real data with the model itself, and I don’t think it is wise to start fitting something without explaining it first. 

So, what it your advice in this matter? What would you do? 

Also, any papers with example of something similar are welcome.
You have data S, and a model M={H, f(H), g(H)}, where H are the set of assumptions of the model, and {f(H), g(H)} are components of the model that of course depend on the assumptions in some way. 

Using the data, you estimate a functional form for f(H), for lack of mathjax denote it empf(S,H)

Then you apply your model to the data and obtain a measure of fit, expressed here in abstract as a distance measure between model and data

{M,S}->d(M,S)*   or **{[H, empf(S,H), g(H)], S}->d([H, empf(S,H), g(H)],S)  

One (not all) components of your model had a chance to "fit with the data"  prior to applying the whole model, and so it has increased the chances of your model as a whole to fit the data. 

So the "distance" is not purely between model and data -it is a distance between a model that has already come "closer to the data", through empf(S,H), and the data.  

In a rather known dialect, "you trained (partly) your model, using the same data set on which you are going to test it". I would not consider this as scientifically acceptable. 

The way out of course, is to estimate f by using a different data set, a data set that should, according to your theory, be able also to be represented by the same model. And this also guides you as to how you could go about writing your paper.