Posted by =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <
jean-jacques.lauble@wanadoo.fr> Hi everybody
The idea of deciding which modelling technique is better adapted to a problem, while being a fantastic idea may be practically very difficult to realize.
The first reason is that there is no method to characterize a 'problem'.
The second reason is that many methods can solve problems equally well.
The third reason, is that how the method works depends highly on the people who apply it. And there is generally for most methods no way to characterize the competence of somebody relative to them.
The fourth reason is that finding the team of 'independent and competent' people that will do the job will be hard. You will not find at the same time competent and independent people as the fact of being competent in a domain lessens the independence from it.
But to be positive I propose something else that is still hard but that may help.
Did you ever read a book about any methodology that in its preferably first chapters explains precisely and fully the conditions that must be met, for a problem to be solved with it?
Is SD doing this? And why not work in this direction so as to be sure not to deceive any customer and be sure that he will get more benefit from the model than the cost of building it? This approach does not exist in SD to date or is extremely vague. I did not see any book that explains fully these conditions. The advantage would be to concentrate on topics that can be really solved with SD and to generate more satisfied customers.
I have for instance spent 3000 hours on a subject without having solved anything serious and 4 hours on another having got a lot of insight from it. I have tried to understand why. The difference between the two subjects is that one is very well fitted for SD the second not, even if it has a lot of dynamic in it.
To detect if a subject is well suited for SD is a question of coherence. There must be a coherence between the objective and the model that will be built to resolve it:
1 The expected profit from the model must be greater than the expected cost of it. Different objectives may be considered keeping the one that best comply to the conditions.
2 Is it possible to construct a model with boundaries broad enough to consider all the the realities that need to be modelled but not to broad so as to keep it manageable?
3. How many factors are involved? are they independent from one another? Are some of them dominant so as to ignore the others and keep the model simple?
4. Are the data needed available? Are they unbiased? how much will it cost to get them?
5. Once the boundaries are settled will the exogenous factors be easily predicted? There is an opposition between the endogenous variables calculated by the model and the exogenous variables not modelled and calculated by traditional techniques. The ideal is that the model has no exogenous variables, which is generally rarely the case.
6. How will the outside world (the exogenous factors) react to the policies? For instance in my model setting prices, the pricing of the competitors was exogenous, but obviously if I modified my prices, the competitors would react to it. So it seemed necessary to include the pricing policy of the competitors in the model. Is it possible to have a reasonably sized model where you do not have the risk of policies influencing the exogenous data?
7. Is it possible to aggregate the data so as to have a model
simple
enough without having the results completely biased by the aggregation?
8. Can the policies proposed by the model be applied? This to
avoid
theoretical models whose policies are never applied.
I stop here the list but there are many others things to be considered.
Without being too systematic it is possible to weight all the conditions, to note them and to have a general note of compatibility that could give an idea of the chances the project has to be a good one.
Somebody prudent would then decide to go on only if the note
is high and the project very well fitted.
Of course the list I proposed is certainly not the best one, and lacks many other conditions I am not an SD specialist.
But knowing by advance that the technique fits the problem, would certainly increase the number of potential customers and ease the building of the model too, the principal difficulties having been already studied.
Of course to follow such a path, it is necessary to accept the idea that there are other methods than SD that are more effective for some problems even if they are simple and often because they are simple.
Regards to everybody and happy Christmas.
J.J. Laublé Allocar
Strasbourg France.
Posted by =?iso-8859-1?Q?Jean-Jacques_Laubl=E9?= <
jean-jacques.lauble@wanadoo.fr> posting date Sat, 24 Dec 2005 17:04:36 +0100