Note: This is not for the practical user. It is rather for academics.

Because many people ask for an explanation for the calculations of our Insight Matrix I explain as much as possible without telling you how our algorithm solved a series of technical challenges.

Basically the Insight Matrix calculates the effects that an impulse of 1 of one factor would have onto the factor we see the Insight Matrix of. This impulse goes along all the paths within the model and through all the loops from this one factor to the other.

The weighting (e.g. the weak, middle and strong impacts) simply means that the impact is multiplied by 0.1, by 0,17, by 0.25 and so forth.

A loop is calculated as if there was a level factor within it, which means, that loops lead to an increase or a decrease of an effect. This is expressed with the y-value of a factor. It is a bit tricky, but to show a significant impact the value of a loop is taken two times thus you can assume that the impulse of a factor is calculated for the time steps 1, 3 and 5 for the short, medium and long term values shown in the Insight Matrix.

Also put into the y-value is the effect of delays. If there is a delay of an impact it is divided by two in case of a medium term impact and by 4 in case of a long term impact.

I recommend to build yourself a small example using just three or so factors and a weak weighting so you can follow the calculations without a pocket calculator.

This explanation clearly shows what we always emphasize: the position of a factor in the Insight Matrix shows only its potential impact in comparison to other factors. It doesn't show to what extent something develops. That is what the quantitative modeling is for. If we doubt the concrete position of a factor we should pay a close look to all the connections and the question whether the loops would really contain a level factor. Also we should consider to quantify the model, especially if we want to see what a combination of impulses would mean.

Interestingly though is that we have several examples where the qualitative model provided the same results as the quantitative model.

Finally the important difference between qualitative and quantitative models:

A qualitative model shows you the potentially most effective measures and the biggest obstacles. If all your assumptions are correct, the model is correct. If you have included the decisive factors (hence the KNOW WHY Method) the model should also be useful. It answers the question: what to do?

A quantitative model shows the potential development of something over time, expressed in its concrete values. If you use the more realistic Monte Carlo simulations it is the a bandwidth of possible outcomes. It then shows you the values of a factor at what time to what extent with what likelihood. Therefore it answers the question how something will develop.

Currently I am working on an interactive iBook, possible a print version to follow and some accompanying video. This kind of background information will be included. Let's hope it will be a wonderful summer with lots of time for me to finish these projects. However, I can promise already that the iMODELER will look even cooler than today so its worth waiting …

Happy modeling

Kai