Superforecasting Traits Tetlock Team Traits Lookup
Return the definition and team practice for a calibration, diversity, updating, decomposition, or aggregation trait.
Description
A forecasting team can improve its process through calibration, varied perspectives, updating, decomposition and aggregation.
A forecasting team can improve its process through calibration, varied perspectives, updating, decomposition and aggregation. These are practices to examine rather than guarantees of forecast accuracy.1
The returned values include Team practice, Superforecasting trait, Trait definition. The How section below explains the calculation, input rules and interpretation.
When to use Superforecasting Traits Tetlock Team Traits Lookup
Use Superforecasting Traits Tetlock Team Traits Lookup when you need to evaluate the supplied records under the calculation and evidence rules below. This is a concise teaching reference. Verify original publications, context and applicable editions before relying on it; it is not a complete framework or an authoritative evaluation.
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 Superforecasting Traits Tetlock Team Traits 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 trait using the field table and calculation rules, then run the tool.
- Read teamPractice, trait, traitDefinition. 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 |
|---|---|---|---|
| trait | Superforecasting trait | string; one of "calibration", "diverseViews", "disciplinedUpdating", "decomposition", "teamStructure" | Required |
Choose the exact enum value or supported integer in the input table and run the lookup. The tool indexes its embedded table, returns the fields shown below and copies list fields where applicable. All supported selections and their actual returned entries are included below. It performs no network request, project measurement or forecast inference.
| Selection | Returned reference entry |
|---|---|
| calibration | teamPractice: Score forecasts and compare predicted with observed ranges over time.; trait: calibration; traitDefinition: Calibration seeks accurate probabilities rather than confidence or rhetorical certainty. |
| diverseViews | teamPractice: Seek independent models before discussing the group aggregate.; trait: diverseViews; traitDefinition: Diverse views reduce premature convergence and correlated blind spots. |
| disciplinedUpdating | teamPractice: Record updates, new evidence, and reasons whenever beliefs change.; trait: disciplinedUpdating; traitDefinition: Disciplined updating changes forecasts in response to evidence rather than identity or social pressure. |
| decomposition | teamPractice: Decompose a target into drivers, dependencies, and sub-forecasts.; trait: decomposition; traitDefinition: Decomposition makes a complex target measurable and updateable. |
| teamStructure | teamPractice: Combine independent estimates before aggregation and preserve minority forecasts.; trait: teamStructure; traitDefinition: Structured aggregation combines expert judgment while retaining information from disagreement. |
exact selection → fixed embedded reference entry Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| teamPractice | Team practice | string |
| trait | Superforecasting trait | string |
| traitDefinition | Trait definition | string |
{
"input": {
"trait": "calibration"
},
"output": {
"teamPractice": "Score forecasts and compare predicted with observed ranges over time.",
"trait": "calibration",
"traitDefinition": "Calibration seeks accurate probabilities rather than confidence or rhetorical certainty."
}
}Limitations and assumptions
This is a concise teaching reference. Verify original publications, context and applicable editions before relying on it; it is not a complete framework or an authoritative evaluation.
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 Delphi Method Round Consensus Convergence Tracker: Delphi rounds can be summarized by already computed medians and spreads. This tracker defines convergence solely by the final spread. For a complementary task, use AGI Forecast Survey Median Calculator: A survey can contain a row of respondent median years for each forecast question. Pooling all entries differs from averaging the per-question medians.
References
Similar or alternative tools
- Delphi Method Round Consensus Convergence Tracker
Delphi rounds can be summarized by already computed medians and spreads. This tracker defines convergence solely by the final spread.
- AGI Forecast Survey Median Calculator
A survey can contain a row of respondent median years for each forecast question. Pooling all entries differs from averaging the per-question medians.