This morning someone tweeted:

> "The natural & life sciences (1) may use the same words as humanities & social sciences (2) but they often *use them differently.*

>We need to get comfortable w the uncertainty of 21stC-life. We need to use language more precisely & showing clearly where we use it imprecisely

https://twitter.com/_ppmv/status/1243144909735055361

I responded that he should look into category theory. Computational structures can be categorized according to computational flow, and thus structures & algorithms used in different disciplines but with idiosyncratic names can be pattern-matched according to their structure and data flow and then reapplied elsewhere. This can potentially benefit from the work that went into the original model and so the new algorithms don't have to be reinvented.

So it's not simply about defining our terminology as one commenter recommended but defining the model unambiguosly.

I bring this up because I see this happening in this thread with the cross-disciplinary use of compartmental models in resource depletion and in contagion modeling, and with ideas shared both ways

Example 1 : No one (except for moi) seems to mention compartmental models in resource depletion but they are well-known in epidemiology.

Example 2 : In resource depletion the idea of linearizing the logistic function (via Hubbert linearization) is well known but I have no idea whether it even exists in epidemiology.

The approach is to apply [category theory to describe the compartment model](https://forum.azimuthproject.org/discussion/2499/tutorial-on-stochastic-petri-nets-with-sir-disease-model-as-example#latest) in each case and then pattern match. The equivalence in the structure at the category theory level will root out the commonality independent of the naming of the model.

This is the pattern recognition application of category theory that seems to be eluding everyone, IMO. But it is straightforward when we place it into this context, where *"roughly speaking, category theory is graph theory with additional structure to represent composition"*.

> "The natural & life sciences (1) may use the same words as humanities & social sciences (2) but they often *use them differently.*

>We need to get comfortable w the uncertainty of 21stC-life. We need to use language more precisely & showing clearly where we use it imprecisely

https://twitter.com/_ppmv/status/1243144909735055361

I responded that he should look into category theory. Computational structures can be categorized according to computational flow, and thus structures & algorithms used in different disciplines but with idiosyncratic names can be pattern-matched according to their structure and data flow and then reapplied elsewhere. This can potentially benefit from the work that went into the original model and so the new algorithms don't have to be reinvented.

So it's not simply about defining our terminology as one commenter recommended but defining the model unambiguosly.

I bring this up because I see this happening in this thread with the cross-disciplinary use of compartmental models in resource depletion and in contagion modeling, and with ideas shared both ways

Example 1 : No one (except for moi) seems to mention compartmental models in resource depletion but they are well-known in epidemiology.

Example 2 : In resource depletion the idea of linearizing the logistic function (via Hubbert linearization) is well known but I have no idea whether it even exists in epidemiology.

The approach is to apply [category theory to describe the compartment model](https://forum.azimuthproject.org/discussion/2499/tutorial-on-stochastic-petri-nets-with-sir-disease-model-as-example#latest) in each case and then pattern match. The equivalence in the structure at the category theory level will root out the commonality independent of the naming of the model.

This is the pattern recognition application of category theory that seems to be eluding everyone, IMO. But it is straightforward when we place it into this context, where *"roughly speaking, category theory is graph theory with additional structure to represent composition"*.