In a recent article, Edward B. Deakin and Michael H. Granof " demonstrated how regression analysis, coupled with Bayesian statistical procedures, can be used to provide the auditor with assistance in selecting those accounts for investigation that are most likely to result in significant audit findings. The article encourages audit model development and experimentation by independent auditors with the application of these and other techniques which make use of additional information. The independent auditor's ultimate concern with respect to a reported account balance of a client, is whether it is "fairly presented." Suppose that the auditor has evaluated the design of an internal control subsystem and has conducted tests of compliance of system operation with system design. Substantive tests include analytical review of significant ratios and trends and resulting investigation of unusual fluctuations and questionable items and tests of details of transactions and balances. Without the refinement, the sample sizes required after using regression analysis to revise priors often will be substantially smaller than the 163 required under classical, unrestricted random sampling.

This paper provides a brief review of the salient economic, behavioral and quantitative literature in accounting and transfer pricing. Further, it relates this review to a relatively new programming formulation, suggesting the greater descriptive implications potential inherent in the new technique generalized goal decomposition programming. The paper does not develop new ideas, but attempts to relate existing material in a manner that will bring the reader's attention to the potential of the mathematical programming models in hierarchical organizations. The expanding literature in this area clearly is deserving of attention as it provides a means by which the behavioral and algorithmic approaches to resource transfer problems may be integrated. It is a step in the direction of models integration and thus, a step toward more realistic mathematical formulation of the decision-making environment. One rely on the scholar T. Ruefli model in this presentation as it provided a relatively easily explained formulation. Interested readers will find on pursuing this literature, that certain of the Ruefli limitations have been overcome in alternative algorithms.