SMIC PROB-EXPERT
Cross-impact probability

The SMIC PROB-EXPERT Method
Cross-impact probability

Aim

Cross-impact probability methods aim to define simple and conditional probabilities of hypotheses and /or events, as well as the probabilities of combinations of the latter, taking into account interactions between events or hypotheses.
The goal of these methods is not only to tease out the most plausible scenarios for decision-makers, but also to examine combinations of hypotheses that one would have initially excluded.

Description of the method

The cross impact method is a generic term for a family of techniques which attempt to evaluate changes in the probabilities of a series of events following the occurrence of one or several such events.
Here we describe here the SMIC PROB-EXPERT method (Cross-Impact Matrices and Systems). In practice, if one considers a system with n hypotheses, the SMIC PROB-EXPERT will enable one to choose - on the basis of the data provided by the experts - out of 2n possible images (hypothesis configurations) those which merit more detailed study in terms of probability of occurrence. The SMIC PROB-EXPERT, together with the PROB-EXPERT software, outlines the most probable futures which then serve as a basis for scenario building.

• Phase 1 : Formulating the hypotheses and choosing the experts

A SMIC PROB-EXPERT survey starts with five or six fundamental hypotheses and some ancillary hypotheses. It is not easy, however, to study the future of a complex system with such a limited number of hypotheses, hence the interest of structural analysis-type methods (card 7) and a reflection on actors’ strategies (card 8) which allow for a better identification of the key variables and better formulation of the basic hypotheses.

The survey is generally carried out by mail with a fairly satisfactory level of response : 25% to 30%. Around one and a half months is needed to carry out a SMIC PROB-EXPERT. The experts questioned should be chosen according to the same criteria as the Delphi method.

They are asked to do the following :

• Appraise the simple probability of a hypothesis occurring by means of a scale from 1 (very low probability) to 5 (highly probable) ;
• Appraise the conditional probability of a hypothesis if the others occur or not.

Given these questions, any expert is obliged to reveal the level of implicit coherence in his/her reasoning.

• Phase 2 : Probability of scenarios

The SMIC PROB-EXPERT program (traditional program for minimising a square law form under linear constraints) enables raw data to be analysed by :

• Correcting the experts' opinions so as to obtain clear, coherent results (i.e. that comply with standard probability axioms),
• Assigning a probability to each of the 2n possible combinations of n hypotheses.

Using the mean probability assigned to each image by the whole set of expert groups, a hierarchy can be established for the images, and, consequently, the most probable scenarios.
It is then advisable to select three or four of these scenarios, among them a reference scenario (with a high average probability of occurrence), and contrasted scenarios, whose probability can be low but whose importance for the organisation must not be neglected.
The final stage consists in writing up the scenarios, e.g., the route from the present to final images, as well as actors' behaviour. This is part of the scenario method.

Usefulness and limitations

The so-called probability interaction methods are a marked improvement on the Delphi method since they offer the advantage of taking into account interactions between events. In contrast to the Delphi method, the SMIC PROB-EXPERT takes into account the interdependence of questions asked and ensures a high degree of consistency in the answers. It is simple to implement, can be completed in a relatively short time and the results are generally easy to interpret.

Finally, it is an excellent intellectual buffer which often helps to discard certain preconceived ideas ( see chart below) and, above all, it allows one to check whether the scenarios studied cover a reasonable range of probable futures; i.e., there are at least six to seven chances out of ten that the future reality will correspond to one of these scenarios.

Care must always be taken, however to avoid an over-mechanical application of this type of method. Participants must not forget that the probabilities obtained remain subjective probabilities, i.e., they are not based on observable frequencies but on opinions.

The information gathered during a SMIC PROB-EXPERT survey is substantial as there are as many hierarchies of scenarios as there are experts questioned. There is therefore the problem of aggregating the answers provided by several experts. One solution is to draw up a typology of experts based on the closeness of their responses or to consider them in terms of actor groups. Analysing responses from the different expert groups also helps to highlight certain groups of actors' games. The raw, clear data obtained (represented most frequently in the form of histograms), enables one to identify certain consensus, to bring out schools of thought by using sensitivity analyses, and thus identify certain groups of experts or actors.

Practical conclusions

Set up by Michel Godet between 1972-1973 at the French Atomic Energy Authority (CEA), then developed by SEMA, the SMIC PROB-EXPERT has long been applied both in France and abroad. Many other methods of probability interaction have been developed since the mid-sixties in the United States as well in Europe.

The SMIC PROB-EXPERT technique can now be used on computer with the PROB-EXPERT software, developed and published by Heurisco. It is therefore possible to drive a SMIC PROB-EXPERT in real time with a group of experts (over one day, for example). This does not, however, preclude a more traditional application of the method, i.e., using traditional or E-Mail.

Bibliography

• BENASSOULI P., MONTI R., La planification par scenarios, le cas Axa France Cahiers du LIPSOR - Scenarios and strategies : a toolbox for scenario planning 88 2005, Futuribles, n°203, November 1995.
• DUCOS G., “Two complementary cross-impact models : MIP1 and MIP2”, Futures, October 1980.
• GODET M., “Smic : a new cross impact method”, Futures, August 1975.
• GODET M., From anticipation to action, Unesco, 1993.
• GODET M., Impacts croisés : exemples d'applications, Futuribles, n°71, November 1983.
• GODET M., CHAPUY P., COMYN G., “Global scenarios of the international context on the horizon 2000”, Futures, April 1994.
• HELMER O., Looking forward : a guide to futures research, Sage publications, 1983.
• MARTINO J.P., Technological forecasting for decision making, Mac Graw Hill, 1993.

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