论文标题
扩展下模型预测的鲁棒性
Robustness of Model Predictions under Extension
论文作者
论文摘要
现实世界的数学模型是复杂系统的简化表示。使用数学模型的警告是，在模型扩展下，预测的因果效应和条件独立性可能不健壮，从而限制了此类模型的适用性。在这项工作中，我们考虑将两个模型组合在一起时保留定性模型预测的条件。在温和的假设下，我们展示了如何使用因果秩序的技术来有效评估定性模型预测的鲁棒性。我们还表征了一大批模型扩展，以保留定性模型预测。对于平衡处的动态系统，我们演示了新颖的见解如何有助于选择适当的模型扩展，并推理出反馈回路的存在。我们使用具有免疫反应的病毒感染模型来说明我们的想法。
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.
