 Finally, it should be noted that the lens model, like nearly all linear models used to represent human judgment and decision-making, does not scale well as a descriptive model. In multi-cue decision-making tasks involving more than three cues, people often turn to simplifying heuristics due to the complications involved in performing the necessary calculations  (section 2.1;  see also  section 4).  More generally, as we remarked in  section 2.3,  linear models involve calculating trade-offs that are difficult for people to perform. Lastly, the supposition that the environment is linear is a strong modeling assumption. Quite apart from the difficulties that arise for humans to execute the necessary computations, it becomes theoretically more difficult to justify model selection decisions as the number of features increases. The matching index G is a goodness-of-fit measure, but goodness-of-fit tests and residual analysis begin to lead to misleading conclusions for models with as five or more dimensions. Modern machine learning techniques for supervised learning get around this limitation by focusing on analogues of the achievement index, construct predictive hypotheses purely instrumentally, and dispense with matching altogether (Wheeler 2017).