From models to simulations: how is it possible to overcome the loss of understanding?

Unlike traditional models, simulation models hardly provide a genuine understanding of the phenomena under study. On the one hand, complicated numerical methods for solving equations are embedded in the simulation models. On the other hand, simplifications and idealizations are often reduced to a minimum. In such cases, the relations between the inputs and outputs of simulations are not easily graspable by a single human mind, and we arrive at what Johannes Lenhard (2006) calls the "complexity barrier" of simulation models. Once this complexity barrier is reached, how is it possible to overcome the induced loss of understanding? A recent response claims that such a possibility can be found in developing meta-models like those designed by economists. However, there is usually no such possibility available when we want to investigate complex systems, e.g. turbulent flows, spin glasses, population genetic systems, or stock markets. So what does remain when all that we have for describing target phenomena are simulation models? Can we ever gain understanding from simulation models? In order to answer this question, I propose to examine the actual scientific practices in laboratories before and after the simulation process. These practices reveal insight into the kind of understanding the users of simulations are looking for. From their examination, I outline a pragmatic account of understanding according to which the user can understand the simulated phenomena by carrying out an investigation on the visual representations that simulations generally provide