The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests

[...]any model that estimates coefficients can be used for out-of-sample prediction, and it makes sense to do so when assessing model performance (see, e.g., Cranmer & Desmarais 2017 for a detailed discussion). [...]when done properly, model estimation fails due to degeneracy, a problem plaguing poor theoretical specifications (Schweinberger et al. 2020). Model fit is a function of the theoretical specification of the chosen model (i.e., the set of model terms), the parameters that are attached to the theoretical specification (i.e., the estimated coefficients for the model terms given the observed data), and the theoretical elements hardwired into the technique (i.e., how the nodes are chosen to update their tie profile, the time horizon with which they evaluate their objective function, whether they make sequential or simultaneous choices, etc.—these are additional theoretical elements that may differ across models). [...]Delta \) Model fit = f(\(\Delta\) hardwired model assumptions | data; choice of specification) Any manipulations of the givens, such as the data or the specification, invalidate the research design.