Cross-Impact Analysis
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Cross-Impact Analysis, developed in the 1960s by Theodore Gordon and Olaf Helmer for long-range forecasting, builds a matrix in which each pair of future events is scored for how the occurrence of one raises or lowers the probability of the other. Its core proposition is that future events are not independent, so isolated probability estimates systematically mislead. The key qualifier is calibration: the influence ratings come from judgment, so the matrix is a structured way to surface assumptions rather than an objective probability engine.
SCAFFOLDING EFFECT
Reduce cognitive load
- Event inventory: list the future events you are forecasting along with their initial base probabilities. - Influence matrix: score how each event promotes or inhibits every one of the other listed events. - Lever identification: find the events whose occurrence shifts the largest number of the other probabilities.
Anchor fast decisions
Treating events as independent makes a scenario the product of separate odds, which understates both cascades and dampening. Mapping pairwise influence forces each interaction to be stated and then fed back into the probabilities, so the revised estimates reflect how outcomes cluster. The matrix also reveals which few events carry most of the leverage, which is where attention belongs.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Cross_impact_analysisverified
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