Mortality Models Ensemble via Shapley Value

TL;DR

This paper introduces a game-theoretic method using Shapley values to optimally weight models in ensemble longevity forecasting, improving accuracy and interpretability of combined predictions.

Contribution

It proposes a novel Shapley value-based approach for model weighting in actuarial ensemble forecasts, enhancing accuracy and understanding of individual model contributions.

Findings

Shapley value weighting improves forecast accuracy.

The method clarifies each model's contribution to ensemble predictions.

Application demonstrates better performance over traditional weighting methods.

Abstract

Model averaging techniques in the actuarial literature aim to forecast future longevity appropriately by combining forecasts derived from various models. This approach often yields more accurate predictions than those generated by a single model. The key to enhancing forecast accuracy through model averaging lies in identifying the optimal weights from a finite sample. Utilizing sub-optimal weights in computations may adversely impact the accuracy of the model-averaged longevity forecasts. By proposing a game-theoretic approach employing Shapley values for weight selection, our study clarifies the distinct impact of each model on the collective predictive outcome. This analysis not only delineates the importance of each model in decision-making processes, but also provides insight into their contribution to the overall predictive performance of the ensemble.