Futures Wheel First Second Third Order Spokes
Assign caller-supplied futures-wheel spokes to first-, second-, or third-order effects and report counts and uncovered orders.
Description
A futures wheel distinguishes immediate implications from later-order effects.
A futures wheel distinguishes immediate implications from later-order effects. This coverage check uses supplied order assignments.1
The returned values include All orders represented, Missing order codes, Order counts, Spoke count. The How section below explains the calculation, input rules and interpretation.
When to use Futures Wheel First Second Third Order Spokes
Use Futures Wheel First Second Third Order Spokes when you need to evaluate the supplied records under the calculation and evidence rules below. No effect generation, causal linkage or diagram drawing is performed.
Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
How to use Futures Wheel First Second Third Order Spokes and understand its results
- Define the question, evidence and scenario assumptions first. Supply the chosen score scales, time horizons or reference selection explicitly. Interpret generated text as a discussion template and screen labels as the stated rules.
- Enter orderAssignments, spokes using the field table and calculation rules, then run the tool.
- Read complete, missingOrders, orderCounts, spokeCount. The explanation below defines the units, signs, ordering and any flags or placeholder values.
- If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.
| Input field | Meaning | Value type; see rules below | Requirement |
|---|---|---|---|
| orderAssignments | One order code 1, 2, or 3 for each spoke. | list of number | Required |
| spokes | Spoke labels | list of string | Required |
Require aligned nonempty nonblank spokes and safe-integer orders1–3. Return counts, missing orders, spoke count and complete flag when all orders occur.
counts=count orders1,2,3 Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| complete | All orders represented | boolean |
| missingOrders | Missing order codes | list of number |
| orderCounts | Order counts | list of number |
| spokeCount | Spoke count | integer; minimum: 1 |
{
"input": {
"orderAssignments": [
1,
2,
1,
3
],
"spokes": [
"Automation",
"Climate",
"Trade",
"Energy"
]
},
"output": {
"complete": true,
"missingOrders": [],
"orderCounts": [
2,
1,
1
],
"spokeCount": 4
}
}Limitations and assumptions
No effect generation, causal linkage or diagram drawing is performed.
Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Core numeric and string lists are limited to 100,000 entries; text entries are subject to the general large-text limit. Follow any smaller calculation limits above. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors. A successfully returned false flag is an evaluated outcome; its meaning is explained above.
Alternative or Complementary methods
Use Urbanization Driver Implication Chain Builder: A driver graph records directed implications. Reachability identifies which listed drivers can be reached from a chosen root. For a complementary task, use Causal Layered Analysis Litany to Metaphor Depths: Causal layered analysis separates surface accounts, social/system causes, worldviews and myths/metaphors. Each layer supports a different kind of inquiry.
References
Similar or alternative tools
- Urbanization Driver Implication Chain Builder
A driver graph records directed implications. Reachability identifies which listed drivers can be reached from a chosen root.
- Causal Layered Analysis Litany to Metaphor Depths
Causal layered analysis separates surface accounts, social/system causes, worldviews and myths/metaphors. Each layer supports a different kind of inquiry.