Systems Iceberg Events Patterns Structures Models Lookup

Organize caller-supplied events, patterns, structures, and models into an evidence-traceable systems iceberg.

Description

The systems iceberg organizes events, recurring patterns, structures and mental models.

The systems iceberg organizes events, recurring patterns, structures and mental models. Each deeper explanation needs supporting evidence.^[1]

The returned values include Layer assignments, Layer questions, Recommended trace. The How section below explains the calculation, input rules and interpretation.

When to use Systems Iceberg Events Patterns Structures Models Lookup

Use Systems Iceberg Events Patterns Structures Models Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. The tool formats submitted explanations rather than detecting patterns or proving causation.

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 Systems Iceberg Events Patterns Structures Models Lookup 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 modelDescription, observation, patternDescription, structureDescription using the field table and calculation rules, then run the tool.
Read layerAssignments, layerQuestions, recommendedTrace. 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
modelDescription	Mental model description	string	Required
observation	Event observation	string	Required
patternDescription	Pattern description	string	Required
structureDescription	System structure description	string	Required

Require nonblank strings for all four layers, trim them and return labeled descriptions in the stated order, fixed layer questions and a fixed evidence-tracing recommendation.

outputLayers=[event,pattern,structure,model]

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
layerAssignments	Layer assignments	list of string
layerQuestions	Layer questions	list of string
recommendedTrace	Recommended trace	string

json

{
  "input": {
    "modelDescription": "A model that treats capacity as independent of demand",
    "observation": "Waiting times rose on peak days",
    "patternDescription": "Waiting times recur during demand peaks",
    "structureDescription": "Capacity is scheduled independently of peak demand"
  },
  "output": {
    "layerAssignments": [
      "events: Waiting times rose on peak days",
      "patterns: Waiting times recur during demand peaks",
      "structures: Capacity is scheduled independently of peak demand",
      "models: A model that treats capacity as independent of demand"
    ],
    "layerQuestions": [
      "events — What happened, and when?",
      "patterns — What recurring changes are visible across the events?",
      "structures — What relationships and constraints produce those patterns?",
      "models — Which mental models and assumptions make the structures visible or invisible?"
    ],
    "recommendedTrace": "Trace from events to patterns, then structures, then models; verify each inference with evidence rather than assuming one causes the next."
  }
}

Limitations and assumptions

The tool formats submitted explanations rather than detecting patterns or proving causation.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Extremely large text inputs are rejected. Missing fields without a declared default, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

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. For a complementary task, use Causal Loop Diagram Reinforcing Balancing Mapper: The sign product of a closed causal loop distinguishes reinforcing from balancing feedback. Link signs must already be combined by the caller.

References

Systems Iceberg Events Patterns Structures Models Lookup — tool.io

Similar or alternative tools

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.
Causal Loop Diagram Reinforcing Balancing Mapper
The sign product of a closed causal loop distinguishes reinforcing from balancing feedback. Link signs must already be combined by the caller.