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Trend-modeling pack

The trend-modeling pack is a methodology dimension. It applies three-valued logic — INC (increasing), DEC (decreasing), CONST (constant) — to analyze markets when precise numerical data is unavailable. It enables meaningful directional analysis with minimal information and produces a complete enumeration of consistent scenarios.

For control-plane mechanics see Packs and Plugins.


Version: 0.4.1 | Kind: methodology | Dimension: trend

Source: packs/trend-modeling/trend-modeling/

Models market or system behavior by assigning INC / DEC / CONST to a set of variables and systematically generating all scenarios consistent with the declared relationships between them. A Mermaid stateDiagram-v2 transitional scenario graph is required.

The three values extend with acceleration modifiers when the data supports them: AG (accelerating growth), DG (decelerating growth), AD (accelerating decline), DD (decelerating decline). Pairwise relationships are expressed as INC(X, Y) (X and Y move together) or DEC(X, Y) (X and Y move oppositely).

Use trend-modeling when:

  • Data is scarce or unreliable
  • Relationships between variables are qualitative rather than quantitative
  • Uncertainty is high and quick directional insight is needed
  • Scenario planning is required but numerical constants or parameters are unavailable

Six required output sections:

SectionContent
VariablesVariable name, current state, trend, confidence
Relationship MatrixINC / DEC pairwise relationships between all variables
Generated ScenariosAll consistent variable assignments; terminal flag per scenario
Transitional GraphMermaid stateDiagram-v2 showing scenario transitions
Terminal Scenario AnalysisEquilibrium conditions, trade-offs, recommendation
Trade-offsMulti-objective conflicts at terminal scenarios

None beyond the core engine. The transitional graph is emitted as a Mermaid stateDiagram-v2 code block (plain text), so the pack runs with no extra tools. Mermaid tooling is optional and only needed to render that block into an image (for example in PDF or HTML output).

  • Three-valued logic produces a complete list of all consistent futures without requiring numerical parameters — the full scenario space is enumerable from qualitative inputs alone
  • Terminal scenario identification surfaces equilibrium states automatically, so planners know where the system converges rather than guessing
  • INC / DEC / CONST notation integrates directly with the same trend vocabulary used by competitive-analysis, financial-analysis, market-sizing, and regulatory-review, making cross-dimension synthesis coherent
  • Transitional graph makes scenario paths and branching points visible, so the transitions between scenarios receive as much attention as the endpoints
TierBasis
HighInputs validated by 3+ independent dimension findings
MediumInputs from 2 dimensions with reasonable assumptions
LowSpeculative or single-dimension basis

Alert conditions: a scenario with >50% probability of adverse outcome, a bifurcation point within the planning horizon, or a terminal scenario that invalidates core business assumptions.

  • Ships disabled; enable with scripts/pack-toggle.sh trend-modeling on.
  • Formal notation is limited to three values (INC / DEC / CONST) plus optional acceleration modifiers — no numerical parameters are accepted.
  • Mermaid CLI is optional; the stateDiagram-v2 block is emitted as plain text and only needs Mermaid tooling to render it into an image.
  • All input variables and declared relationships must be grounded in the findings corpus; speculative relationships must be documented explicitly.
  • Produce a complete, enumerated list of all internally consistent scenarios from qualitative variable assignments.
  • Identify and flag terminal scenarios — equilibrium states where the system converges — automatically.
  • Emit a stateDiagram-v2 transitional graph that makes scenario paths and branching points explicit.
  • Deliver multi-objective trade-off analysis at each terminal scenario with a priority-aligned recommendation.
  • Assign a confidence tier (High / Medium / Low) scaled to the number of independent dimension findings supplying the input variables.
Terminal window
scripts/pack-toggle.sh trend-modeling on