CrossImpactAnalysen

Cross-Impact Analysis, also known in German as CrossImpactAnalysen, is a method used in foresight and scenario planning to study how events and variables influence one another and to identify plausible future developments. It aims to reveal interdependencies, feedback loops, and how combinations of events can alter outcomes.

In practice, researchers define a set of relevant events or drivers and construct a cross‑impact matrix in which each event appears as both a row and a column. The entries record the perceived influence of one event on another, typically on a qualitative or quantitative scale. The approach highlights dependencies, clustering of interactions, and potential leverage points for policy or strategy.

The typical workflow includes specifying the events, filling the matrix with expert judgments, and iterating to improve consistency. The matrix can then be used to derive scenarios, assess overall likelihoods, or identify strategic options and policy levers. Variants range from qualitative matrices to more formal probabilistic or dynamic models, sometimes in combination with scenario planning or Bayesian methods.

Cross-Impact Analysis finds application in national and corporate foresight, technology forecasting, risk assessment, and strategic planning, where understanding interdependencies among drivers improves risk identification and decision support. It is valued for its structured treatment of interdependencies but remains limited by subjective judgments, potential biases, and sensitivity to the chosen set of events. The method abstracts complex systems into pairwise influences and may miss indirect or nonlinear effects, so results are best used alongside complementary approaches.

Example: in energy policy, a surge in oil prices, a breakthrough in energy storage, and new emission regulations may influence investment, consumption, and targets, illustrating how interdependencies shape possible futures.