dessert28/evaluation-of-mathematical-modeling-papers/select-model

Select Model

Choose a defensible route before writing code. The output is a modeling argument a reader can follow, not an algorithm label.

Workflow

1. Read the complete problem; list each deliverable, object, datum, unit, objective, constraint and ambiguity.
2. Decompose the problem into subproblems and draw their input/output dependencies. Mark every input as observed, externally supplied, model-derived, or unavailable. When a decision needs a future quantity and historical observations exist, explicitly compare a causal forecasting submodel with justified non-forecast alternatives before declaring the problem blocked.
3. Read references/structure-opportunity-scan.md and inspect shared states, constraints, data, geometry, objectives and solvers. Decide explicitly whether a unified core is justified.
4. Use references/model-catalog.md to shortlist only genuinely plausible candidates for each subproblem. Compare structural fit, assumptions, identifiability, data demand, interpretability, computational cost, validation route and what would falsify each candidate; do not force a candidate count.
5. Prefer a route where analytical reasoning exposes structure and numerical methods resolve only the remaining unknowns. Treat reduction, symmetry, monotonicity and event functions as hypotheses requiring evidence.
6. Select a primary route, a simple baseline and a validation route. For threshold search, state the monotonicity evidence or select a non-monotone alternative.
7. State rejected candidates and the reason each fails this problem.
8. Treat the selected route as provisional until implementation and validation agree with its assumptions. If downstream evidence contradicts it, revise the selection and record which dependent artifacts must be regenerated.

Output

Return a concise 模型选择与结构分析 containing:

• dependency diagram and the unified-core decision;
• per-question objective, variables, units, constraints and assumptions;
• primary, baseline and independent validation methods;
• proposed formulas/outputs, critical events and anticipated figures;
• data requirements, uncertainty/limitation risks and fallback route.

Keep internal decision records machine-readable when a project uses them, but do not mistake a record for an explanation. Do not claim novelty, data availability or expected performance without evidence.

Resources

• references/model-catalog.md — candidate matrix and disqualifying conditions.
• references/structure-opportunity-scan.md — structural reasoning before algorithm choice.

Executable Contract

For repository-managed competition and audit projects, inspect the bundled contract with python <skill-directory>/scripts/_runtime/skill_contracts.py --skill select-model --profile competition (or audit) and run this Skill through scripts/execute_skill.py with every contracted input and output role. In rapid, return the Output above with a baseline and falsification plan; do not claim the route is formally verified.