Pooling Beats Structure on Short Annual Panels: Parameter Sharing and the Forecast Accuracy of Evolutionary Models for Compositional Time Series

Many economic time series are compositions: educational attainment shares, waste treatment routes, energy mixes, and sectoral employment. Forecasters usually move such series to log-ratio coordinates and apply generic methods that impose no economic restriction on how the parts move. Evolutionary game theory supplies one: replicator dynamics make the growth rate of a share proportional to its payoff advantage, which is the logic of imitation, diffusion and congestion that economics itself uses to explain why shares move. We turn that restriction into a forecast function with K+1 parameters for a K-part composition; estimate it under three parameter-sharing regimes, namely country-specific, fully pooled and validation-shrunk; and race it against classical log-ratio benchmarks, a non-evolutionary Dirichlet comparator and two pooled machine-learning benchmarks in an expanding-window, rolling-origin design on three Eurostat panels. The evolutionary restriction does not buy accuracy: its best specification ties the random walk with drift on educational attainment and loses to persistence on municipal waste routes. What moves accuracy is the sharing regime. Moving from country-specific estimation to the best sharing regime cuts the mean absolute scaled error by 27.8% on attainment and by 15.3% to 19.8% on the waste panels, more than the gap between the best structural and the best statistical model, and the same ordering reappears in the Dirichlet family. The estimated congestion parameters carry the economics the restriction was built for, increasing returns in attainment and congestion in waste routes, while a simulation grid shows the sharing gain reversing once cross-country heterogeneity is appreciable. We close with a walk-forward procedure for tuning the shrinkage weight and the mutation rate.