MICMAC Structural Analysis
Also known as: MICMAC, Structural Analysis, Cross-Impact Matrix Multiplication Applied to a Classification, Matrice d'Impacts Croises
MICMAC — Matrice d'Impacts Croises Multiplication Appliquee a un Classement, or Cross-Impact Matrix Multiplication Applied to a Classification — is the structural-analysis tool at the front of Michel Godet's la prospective method. Developed by Godet with Jean-Claude Duperrin, it starts from a square matrix in which experts record the direct influence of each system variable on every other, then raises that matrix to successive powers to uncover the indirect influences that propagate along chains of variables. Summing the rows and columns of the iterated matrix yields each variable's overall influence and dependence, and plotting variables on the influence-dependence plane sorts them into driving (key) variables, dependent (result) variables, relay variables, and autonomous variables. The purpose is not prediction but diagnosis: to reveal which hidden variables truly drive the system, so that later scenario work focuses on the factors that matter.
Key highlights
- Exposes indirect and hidden driving variables that a reading of direct influences alone would miss, via matrix iteration.
- Reduces a sprawling list of interacting factors to a clear influence-dependence map that focuses subsequent analysis.
- The matrix-filling process itself is a valuable structured discussion that surfaces unspoken assumptions about the system.
- Provides a transparent, reproducible classification of variables into driving, relay, dependent, and autonomous roles.
Intuition
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How it works
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When to use it
Use MICMAC at the diagnostic, structuring stage of a foresight or strategy study, when you have a long list of interacting variables and need to discover which of them really drive the system before investing in scenario construction. It is well suited to complex problems with many interdependent factors where intuition about importance is unreliable and where a panel of experts can credibly judge pairwise direct influences. As the first analytical module of la prospective, it pairs naturally with downstream actor analysis (MACTOR) and morphological scenario building. It is less appropriate when the system has only a handful of obviously central variables, when no qualified group can assess the influence matrix, or when the goal is quantitative forecasting rather than qualitative diagnosis — MICMAC reveals structural roles and relationships, not magnitudes or probabilities of future outcomes.
Strengths & limitations
- Exposes indirect and hidden driving variables that a reading of direct influences alone would miss, via matrix iteration.
- Reduces a sprawling list of interacting factors to a clear influence-dependence map that focuses subsequent analysis.
- The matrix-filling process itself is a valuable structured discussion that surfaces unspoken assumptions about the system.
- Provides a transparent, reproducible classification of variables into driving, relay, dependent, and autonomous roles.
- The entire analysis rests on subjective expert judgements of pairwise influence, so garbage in yields garbage out.
- The ordinal influence scale and equal weighting of links impose strong simplifications on genuinely graded relationships.
- It is static, capturing the system's structure at one moment without modelling how relationships themselves evolve.
- Filling an n-by-n matrix grows quadratically with the number of variables, becoming tedious and error-prone for large lists.
Common pitfalls
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Applications
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Frequently asked
Why multiply the influence matrix by itself?
Because direct influence tells only part of the story. A variable may exert little direct influence yet shape the system powerfully through chains — influencing a factor that in turn influences many others. Multiplying the direct-influence matrix counts the two-step paths of influence, multiplying again counts the three-step paths, and continuing reveals the cumulative indirect reach of each variable. As the power rises the ranking of variables by influence and dependence stabilises, often promoting hidden drivers that the direct matrix understated. This propagation of indirect effects is exactly what distinguishes MICMAC from simply reading the raw matrix.
What do the four variable categories mean?
Plotting variables by influence and dependence yields four quadrants. Driving (key) variables are high-influence, low-dependence: they steer the system and are the priority for action. Relay or stake variables are high in both: acting on them is risky because effects loop back through the system. Dependent (result) variables are low-influence, high-dependence: they reflect the system's outcomes rather than cause them. Autonomous variables are low in both: largely disconnected and safe to set aside. The classification tells the analyst where leverage lies and which variables merely register or sit outside the dynamics.
Does MICMAC predict the future?
No. MICMAC is a structural-analysis and diagnostic tool, not a forecasting method. It does not produce probabilities, magnitudes, or timed predictions; it produces a map of the structural roles variables play in a system. Its job within la prospective is to focus the study by revealing which variables genuinely drive the system, so that the later scenario-building, actor-analysis, and probabilistic stages concentrate their effort where it matters. Anyone expecting numerical forecasts from MICMAC has mistaken its purpose; it answers 'what governs this system?' not 'what will happen?'.
Sources
- 1.Godet, M. (2006). Creating Futures: Scenario Planning as a Strategic Management Tool (2nd ed.). Economica.ISBN 9782717852448
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Cite this page
ScholarGate. (2026, June 23). MICMAC Structural Analysis. ScholarGate. https://scholargate.app/futures-foresight-studies/micmac-structural-analysis