Build a Tariff Exposure and Input-Cost Scenario Planner

A trade-policy analyst supporting a manufacturing association.

Map imported inputs to hypothetical tariff changes and supplier dependencies. Show direct exposure and pass-through assumptions without claiming a complete economy-wide policy effect.

Key: input_id + origin_country + product_code_version + scenario_id.

Fields: customs_value, quantity, currency, supplied_rate, pass_through, substitution_assumption.

Public trade aggregates do not reveal an individual firm supply chain.

Map fictional inputs to documented product categories.

Calculate direct duty changes under supplied rules.

Propagate explicit cost scenarios through the fictional input network.

Export exposure tables and missing-classification questions.

An input dependency graph with product-code review, cost scenarios, and exposure concentration tables.

A versioned input-to-product mapping model.

A policy scenario report separating direct arithmetic from behavioral assumptions.

Runnable local application, setup commands, and a dependency lockfile.

Versioned input data, data dictionary, acquisition instructions, and rights notes.

Calculation traces, source references, assumption records, and exported review artifacts.

A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases.

A recorded complete workflow with a reproducible manifest and explicit limitations.

Unknown classifications remain unresolved.

Duty and pass-through effects are not applied twice.

Do not interpret a hypothetical rate as a current tariff determination.

Exchange-rate and substitution assumptions appear separately.

The same duty increase produces different modeled customer costs under different pass-through assumptions.

The attached starter files are prototype inputs. Expected starter answers are visible. They are not independent holdouts.

Full target: Use public trade aggregates and a fictional firm-level bill of materials. Supply hypothetical tariff rates and pass-through values, not asserted current legal rates.

Complete target contracts and expert review remain pending. Public observations are not historical release vintages.

pandas, NetworkX, Plotly

Use an available model appropriate to the task. Record provider, model identifier, version or retrieval date, settings, cost, and latency. Use deterministic code for calculations. Models may extract, retrieve, or explain evidence. They must not invent missing records, override approval rules, or claim professional authority.

No customs classification advice, filing, or causal general-equilibrium prediction.

No fabricated records, source citations, professional endorsements, or expert-adjudication claims.

No future-data leakage into dated decisions, model fitting, or historical valuation inputs.

No unauthorized payments, account restrictions, regulatory filings, or production changes.

No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation.

No concealed uncertainty or silently invented inputs required for a numerical conclusion.

Correct domain calculations and justified methods with documented assumptions. Weight: 30.

Source lineage, temporal integrity, rights checks, and explicit synthetic-data labels. Weight: 25.

A working application with review, export, error handling, and reproducible state. Weight: 20.

Adversarial cases, uncertainty, missing-data behavior, and sensitivity analysis. Weight: 15.

Evidence-linked conclusions that distinguish facts, assumptions, and unresolved judgments. Weight: 10. Score each dimension from zero to two. Weighted score = sum(weight * score / 2). A failed safety gate blocks acceptance.