Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.

中文翻译：

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寻求复杂计算机模型的可靠统计推断

生态学家越来越依赖复杂的计算机模拟来预测生态系统。为了使这种预测准确，必须减少模型参数和结构的不确定性，并将其正确传播到模型输出中。但是，天真地使用标准统计技术来完成此任务可能会导致参数和预测的不确定性产生偏见和低估。在这里，我们解释了为什么会出现这些问题，并提出了使用复杂计算机仿真进行可靠推理的框架。由于复杂的非线性和互连性，模型误差在复杂的计算机仿真中更为重要之后，我们将讨论可能的解决方案，即在校准过程中或之后，对数据进行重新平衡并在模型输出或过程中添加偏差校正。我们使用动态植被模型说明了案例研究中的方法。我们得出的结论是，开发更好的方法来对复杂的计算机模拟进行可靠的推断对于生成可靠的生态系统响应预测至关重要。