How to characterize mathematical models for comparison

[tez369]

TL;DR

identifying components of a model

I am reviewing and comparing a wide range of mathematical models that are being applied to a specific realm of wildlife biology. For the comparison of these models, and to weigh advantages/disadvantages of different aspects with regard to application, I need to characterize each model. As I do not yet have a great amount of experience working with models I am unsure of essential model aspects that can be used to characterize them. Examples that I have thought of are statistical vs dynamical, linear or nonlinear, heavy or low data requirement...
Can you provide a list of aspects you would use to characterize a model, to be used for comparison?

 

[fresh_42]

Any model is probably a system of differential equations. To list them is basically impossible as there are so many. Famous examples are Lotka-Volterra for predator prey models, or the SIR model for epidemics.

 

[FactChecker]

There are a great many aspects to model and you have not specified what is being modeled. You might start with this article and the links in it and go from there to the specific type of model that you are interested in.

 

[fresh_42]

There are a great many aspects to model and you have not specified what is being modeled.

I remember a thread in which someone asked about a list of differential equations vs. applications, like a lexicon. I started and searched a few on the internet only to find out, that - if added valuable descriptions of both, model and application - this would turn into a job of decades! But it would certainly be of value.

 

[Stephen Tashi]

For the comparison of these models, and to weigh advantages/disadvantages of different aspects with regard to application, I need to characterize each model.

Specifically, what "advantages/disadvantages" are important "with regard to application" in your field of study?