I was thinking about the technique you used to explore changing parameters in the predator-prey example workshop you showed us on Wed, and it occurred to me that there were a couple of tricks used in Bayesian Monte Carlo Markov Chain statistics which could be used to reduce the number of runs needed to get a decent estimate. I was wondering if you had the ability to implement them in modelling4all software.
In the workshop you looked at values for some parameter starting at a minimum value and increasing uniformly to a maxima. If you have a pretty good idea of what the value would be then you could apply a distribution to these values, something akin to taking larger steps at the extremities and smaller ones about the anticipated value. Statisticians call this using a prior distribution. Another concept which would reduce the number of runs is the use of Markov Chains (random or drunken walk). A small change is applied to a starting value. If the output is within some boundary condition, then the starting value+small change is taken as the inital value for the next loop, otherwise that loop is rejected and the starting value taken as the initial value for the next loop.
Cheers and I really enjoyed the workshop!