Does model-free forecasting really outperform the true model?
Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state–space solutions to this problem may provide biased parameter estimates when the underlying dynamics are chaotic (1). Consequently, forecasts based on these estimates showed poor predictive accuracy compared with simple “model-free” methods, which lead Perretti et al. to conclude that “Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data.” However, a simple modification of the statistical methods also suffices to remove the bias and reverse their results.