Posted by Jean-Jacques Laublé <email@example.com> Hi Tobi There have been in the last conferences some papers about multi-methodology and the advantages and drawbacks of different methods. You can consult the different papers on the SD association www.systemdynamics.org
look at the conference button and loop at the past papers. There are too CD's on the last conference papers. It takes some time to consult the papers because there have been many conferences and a lot of papers by conference. I think that you can too consult the papers looking for a word. You must browse through the Association web site to find your way through.
About aggregating the time I mean that instead of considering each single event and the distinct time occurred, SD chooses to aggregated all the events in a single number and to make them happen in a single time period. So for me the aggregation is with the events and the time. But the periods of time in SD are discreet and the time event occurs in DES in a continuous time scale. It explains that the difference in both paradigms is confusing. So you are too right when you describe both SD and DES as you did it.
I too think that hybrid methods is very relative to the kind of problem. Unless one takes a very concrete example, it is very difficult to explore the differences between would be hybrid or not hybrid and how much hybrid. When speaking about the difficulty to use both paradigms I can only refer to my own case.
My problem, adjusting pricing and investments could be explored in a discreet or continuous manner. One can either consider events (generally in a day to day basis) or consider for example month or even year periods using aggregate values.
The method is very different. The first one, due to the fact that there is no aggregation of the inputs obliges you to a very close respect of reality. For instance if I decided that each event was the same and taking place at a regular time step it would generate a model that would be so far away from reality that it would be unusable. So I tried this approach using SD, taking days periods and being obliged to use massive subscripting not to mention the necessity to generate the numbers in a stochastic way with lots of problems if you want to optimize. I did in Vensim, but I finished with models so highly complex that they where no more understandable. My problem was not to generate a day by day simulation but to fix prices and investments and the day by day simulation could have helped in theory to do that but it was practically unusable.
So I changed the method and considered longer time periods, aggregating all the values which delivers interesting results and gives a good idea of the overall behaviour. I think that if you are considering general policies you have better use SD but if you need to have detailed results it is better to use DES or agent based if you can do it. For instance I want to know if considering the competition I have better to invest less and increase my prices or invest more and decrease my prices.
If I find the solution I will be able to take global decisions: for instance if I invest less I will reduce the hiring of people etc.. I feel it is eventually better to know global policies first. But knowing that I will not be able to fix the price for a determined category of vehicle for a certain duration, for a certain number of kilometres. If I want to calculate this it is better for me to use marketing segmentation methods than simulation. This does not mean that it is not possible to do it. But it is a question of capacity and time and cost. But knowing already the first global better strategy will help me fix individual prices because I will know the general direction I have to work towards. Regards. Jean-Jacques Laublé Allocar Strasbourg France Posted by Jean-Jacques Laublé <firstname.lastname@example.org> posting date Fri, 6 Jan 2006 16:28:51 +0100