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We adopt the Shapley value approach to examine the fair allocation of the depreciation charges among the time periods of the asset's useful life. Essentially, the allocation under the Shapley value solution rewards each time period of the asset's useful life with a share of the earnings that corresponds to its "responsibility" in the earnings-generating process. The latter is thus consistent with the developments in accounting standards, which maintain that the depreciation and amortization methods should reflect the pattern in which the asset's economic benefits are consumed by the enterprise. We show that the Shapley solution always conforms to a set of fundamental accounting requirements such as the matching principle and the impairment test. Moreover, unless the asset is associated with constant revenues and/or extremely profitable investments, the Shapley value solution can never coincide with the prevalent straight-line depreciation method. Finally, we identify the family of earnings patterns for which the Shapley solution coincides with the equal surplus and the economic depreciation methods.

AbstractModel 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.

The Shapley value for an n-person game is decomposed into a 2n × 2n value matrix giving the value of every coalition to every other coalition. The cell ϕIJ(v, N) in the symmetric matrix is positive, zero, or negative, dependent on whether row coalition I is beneficial, neutral, or unbeneficial to column coalition J. This enables viewing the values of coalitions from multiple perspectives. The n × 1 Shapley vector, replicated in the bottom row and right column of the 2n × 2n matrix, follows from summing the elements in all columns or all rows in the n × n player value matrix replicated in the upper left part of the 2n × 2n matrix. A proposition is developed, illustrated with an example, revealing desirable matrix properties, and applicable for weighted Shapley values. For example, the Shapley value of a coalition to another coalition equals the sum of the Shapley values of each player in the first coalition to each player in the second coalition. ; publishedVersion