It's the findings, stupid, not the assumptions.(Editorial)

Observing reality is especially valuable. However, without models, every situation at every time on every variable would be unpredictable. Assumptions allow models and theories to assert constancy. Assumptions distill and simplify reality by dismissing the conspicuous but irrelevant. Criticizing assumptions as unrealistic is absurd. Abstraction is the precise virtue of an assumption. For example, seldom are we prisoners facing interrogation, yet the prisoner's dilemma remains relevant. The adage "A bird in the hand is worth two in the bush" is relevant for more than birds. Unrealistic assumptions that deny current beliefs breed great new theories.

Assumptions are analogous to the basic ingredients in a gourmet recipe. Only the final product of the recipe dictates whether the ingredients suffice. Similarly, assumptions are realistic when they produce good theories, satisfactory predictions, valuable implications, and correct recommendations. Output matters far more than input. Realism is only an issue when creatively diagnosing poorly performing models, not when judging model performance.

Assumptions are the source of value in empirical analyses. If data sets were truly the source of value, empirical research studies would only greatly devalue the raw data by dramatically reducing rich observations to a few meager summary statistics or estimated parameters. Most empirical research makes a contribution by ignoring (assuming away) most information in the data.

We must dramatically shift our attention far away from the hopeless pursuit and sophistry of realistic assumptions to the contribution those assumptions produce. There are scientific methods for evaluating model output (i.e., predictions, findings, implications, recommendation) on criteria such as accuracy, reliability, validity, robustness, and so on. No corresponding objective scientific methods exist for evaluating realism. Realism depends only on personal taste.

Key words: models; mathematical models; realistic assumptions; instrumentalism; empirical research

********* Why Study Assumptions?

The role of explicit assumptions in building models is widely misunderstood. In fact, observations, from the peer review process at scholarly journals, chronicle widespread misunderstandings, causing mistakes and inconsistencies in the peer review process. Misguided evaluators sadly focus on evaluating assumptions (i.e., the input) rather than the findings (i.e., the output) or the process (i.e., the logic creating the output). Dismissing models based on only the realism of their early assumptions is facile and invalid compared with the correct approach of carefully evaluating the entire model development, the novelty of the findings, and the contribution to the literature. Given that all assumptions are unrealistic per se, the claim of unrealistic assumptions is empty. Assessing the realism of the assumptions might be important for creatively diagnosing a poorly performing model, but it is an inappropriate measure of performance.