Recent works dedicated to tackling the difficulties of agent-based models (ABM) calibration and assessment have more and more engaged in what could be called a Massively Computer-Aided Modeling-Process (MaCAMP): (Schmitt et al., 2015), (Reuillon et al., 2015), (Cottineau et al., 2015). This is a computer-aided modeling process which - thanks to platforms like OPENMole - massively uses computations operated via grids to “allow a global exploration of the capabilities” of a given ABM in geosimulation (Schmitt et al., 2015). Thanks to this process, almost every free parameters combination of values in the different mechanisms hypothesized in the model is tested in its outcome against the intended output. The result is that this massive, computer controlled and systematic calibration process is no more exposed to the risk of not being a real optimum by being a local one only. Indeed, this risk is high when free parameters are numerous, not easily interpretable and when their estimation relies on incomplete because partially human controlled trial-and-error processes (Schmitt et al., 2015). In front of this trend towards calibration through massive computations, a question arises: what’s the epistemic role of the models’ parsimony if there remains any? Why not get rid of this apparently out-of-age limitation? The surprising fact is that the research works developing this approach still invoke the extreme importance of simplicity, parsimony and controlled complexification for their model building, even if their models finally are complex. Surprisingly enough, parsimony still has an epistemic value in the context of MaCAMP. But what is it? In this talk, I will defend three claims: 1. The use of parsimony is still there but it is not exactly the same as it was in non massively computer aided modeling processes. As a consequence, it appears that the epistemic values of parsimony in geosimulation are diverse and changing; 2. These different values of parsimony can be related to its different use at different levels - or for different aspects - of each of the different mechanisms represented in the model. Sometimes parsimony is sought for assuring genericity, sometimes for improving understanding, sometimes for assuring the strict incremental nature of model complexification, sometimes for enabling interpretation and sometimes for establishing the explaining power of some mechanism of the model. 3. Nevertheless, one can discern a general trend: in the case of MaCAMP, parsimony is less sought for global understanding of the model or via the model that it is for the interpretation of mechanisms or for the establishing of some partial explanation of the target system’s behavior via mechanisms. In a first section dedicated to some definitions, I will assume the distinctive meanings of “understanding” and “explanation” that are most frequently used: “By ‘explanation’, I mean the intelligible representation (i.e. by concepts) of a system of interactions or a mechanism (elements + actions) that are assumed to be the cause of a phenomenon […] By ‘comprehension’ or ‘understanding’, I mean a unifying conceptual representation that can be mobilized by an unassisted human mind. We understand a phenomenon that is composed of a variety of sub-phenomena when we can, by means of a single mental (mathematical or logical) operation, reconstruct the gist of the structure of that variety” (Varenne, 2018, p. 165). By “interpretation” of a model or of some part of it, I mean the opinion of what it means or of what it refers to. Interpretation seems necessary for explanation and understanding. But the reverse does not hold. In the second section, relying on an analysis of some seminal passages of the System of Logic by Stuart Mill, I will recall the traditional reasons why parsimony is authorized and sought for not only in natural sciences but also in theoretical and quantitative social sciences. From this viewpoint, the relevance of a parsimonious theory was related to our desire to both understand and explain. In the third section, taking the examples of Hägerstrand’s theory and models of diffusion (Hägerstrand, 1967) and of Pumain’s evolutionary theory of cities (Pumain, 1997), I will emphasize the difference between theories conceived as sets of principles and theories conceived as sets of hypotheses and mechanisms such as the ones implemented by geosimulation. The latter may gather different mechanisms that affect different entities as much as different aspects of the same entities. When tackling these theories via MaCAMP, the search for parsimony could be reduced to the search for a “minimal set of mechanisms”. But, in fact, as the last section will show through an analysis of the quoted papers, many trade-offs between different - and sometimes contradictory - needs of parsimony appear to be necessary if one wants to assure interpretability and/or explainability at different levels of the models and during its conception. Most of the time, an overall understanding has to be sacrificed but to the benefit of a distributed explanation.