Complex systems of moving and interacting objects are ubiquitous in the natural and social sciences. Predicting their behavior requires models that mimic these systems with sufficient accuracy, while accounting for their inherent stochasticity. Though tools exist to determine which of a set of models is best relative to the others, there is currently no generic goodness-of-fit framework for testing how close the best model is to the real complex stochastic system. We propose such a framework, using a novel application of the Earth mover's distance, also known as the Wasserstein metric. It is applicable to any stochastic process where the probability of the model's state at time t is a function of the state at previous times. It generalizes the concept of a residual, often used to analyze 1D summary statistics, to situations where the complexity of the underlying model's probability distribution makes standard residual analysis too imprecise for practical use. We give a scheme for testing the hypothesis that a model is an accurate description of a data set. We demonstrate the tractability and usefulness of our approach by application to animal movement models in complex, heterogeneous environments. We detail methods for visualizing results and extracting a variety of information on a given model's quality, such as whether there is any inherent bias in the model, or in which situations it is most accurate. This work provides a fundamental toolkit to assess the quality of generic movement models of complex systems, in an absolute rather than a relative sense.
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