MMP.Preface:7 - Consequences, biases and limits

The language makes intermediate modeling results available for subsequent work: an admissible representation, a reporting law, a recoverable target, an inferential conclusion, a repaired prediction, a feasible instruction, a reduced evolution law or a family retaining known relations. Their explicit conditions help collaborators divide the work and change one contribution while retaining others.

Construction costs time and mathematical effort. A familiar adequate model or direct calculation can be sufficient. A reusable model family or derivation becomes valuable when the work needs several cases, revisions or explanations. A remaining ambiguity can also be useful if it tells the team which proposed consequence is unresolved.

The worked cases favour small constructions whose reasoning is inspectable. Larger instances can require specialist mathematics, computational resources and subject knowledge. A proof of existence, a practical obtaining procedure and a reliable implementation have different requirements. Questions involving additional causal structures or specialist statistical constructions can require further methods and subject knowledge.

Prediction, explanation and intervention can also favour different accounts. Use C.2.8 and Explanation Design (EXD) when the recipient’s recoverable understanding is at issue. A predictive success supplies the performance it demonstrates; explaining a phenomenon or changing a mechanism requires the corresponding content. Several models can contribute to the same project, with comparison and improvement supplied by the existing FPF methods.