The generic modeling fallacy: Average biomechanical models often produce non-average results!

Computational biomechanics models constructed using nominal or average input parameters are often assumed to produce average results that are representative of a target population of interest. To investigate this assumption a stochastic Monte Carlo analysis of two common biomechanical models was conducted. Consistent discrepancies were found between the behavior of average models and the average behavior of the population from which the average models׳ input parameters were derived. More interestingly, broadly distributed sets of non-average input parameters were found to produce average or near average model behaviors. In other words, average models did not produce average results, and models that did produce average results possessed non-average input parameters. These findings have implications on the prevalent practice of employing average input parameters in computational models. To facilitate further discussion on the topic, the authors have termed this phenomenon the "Generic Modeling Fallacy". The mathematical explanation of the Generic Modeling Fallacy is presented and suggestions for avoiding it are provided. Analytical and empirical examples of the Generic Modeling Fallacy are also given.