Robustness of Model Predictions under Extension
This work addresses the problem of ensuring the reliability of model predictions for researchers and practitioners who combine or extend existing mathematical models, which is an incremental improvement.
This paper investigates the robustness of qualitative model predictions when two mathematical models are combined. It introduces a method using causal ordering to efficiently assess prediction robustness and characterizes a class of model extensions that preserve these predictions.
Mathematical models of the real world are simplified representations of complex systems. A caveat to using mathematical models is that predicted causal effects and conditional independences may not be robust under model extensions, limiting applicability of such models. In this work, we consider conditions under which qualitative model predictions are preserved when two models are combined. Under mild assumptions, we show how to use the technique of causal ordering to efficiently assess the robustness of qualitative model predictions. We also characterize a large class of model extensions that preserve qualitative model predictions. For dynamical systems at equilibrium, we demonstrate how novel insights help to select appropriate model extensions and to reason about the presence of feedback loops. We illustrate our ideas with a viral infection model with immune responses.