Agent-based models (ABMs) are widely used to analyze coupled natural and human systems. Descriptive models require careful calibration with observed data. However, ABMs are often not calibrated in a formal sense. Here we examine the impact of data record size and aggregation on the calibration of an ABM for housing abandonment in the presence of flood risk. Using a perfect model experiment, we examine (i) model calibration and (ii) the ability to distinguish a model with inter-agent interactions from one without. We show how limited data sets may not adequately constrain a model with just four parameters and relatively minimal interactions. We also illustrate how limited data can be insufficient to identify the correct model structure. As a result, many ABM-based inferences and projections rely strongly on prior distributions. This emphasizes the need for utilizing independent lines of evidence to select sound and informative priors.

基于代理的模型（ABM）被广泛用于分析耦合的自然系统和人类系统。描述性模型需要使用观察到的数据进行仔细校准。但是，ABM通常没有经过正式的校准。在这里，我们检查了数据记录的大小和汇总对存在洪水风险的房屋被遗弃的ABM校准的影响。通过使用完美的模型实验，我们研究（i）模型校准和（ii）区分具有代理人交互作用的模型与没有代理人交互作用的模型的能力。我们展示了有限的数据集可能如何不足以仅用四个参数和相对较少的交互来约束模型。我们还将说明有限的数据如何不足以识别正确的模型结构。结果，许多基于ABM的推论和预测强烈依赖于先前的分布。