  Simulation models are an absolute necessity in the human and social sciences,
which can only very exceptionally use experimental science methods to construct
their knowledge. Models enable the simulation of social processes by replacing
the complex interplay of individual and collective actions and reactions with
simpler mathematical or computer mechanisms, making it easier to understand the
relationships between the causes and the consequences of these interactions and
to make predictions. As the formalism of mathematical models offering
analytical solutions is often not suitable for representing social complexity,
more and more agent-based computer models are being used. For a long time, the
limited computing capacities of computers have hampered programming models that
would take into account the interactions between large numbers of
geographically located entities (persons or territories). In principle, these
models should inform the conditions for the emergence of certain patterns
defined at a macro-geographic level from the interactions occurring at a
micro-geographic level, in systems whose behaviors are too complex to be
understood directly by a human brain. Moreover, it is also necessary to analyze
the dynamic behavior of these models with nonlinear feedback effects and verify
that they produce plausible results at all stages of their simulation. This
essential work of exploring the dynamics of modeled systems remained in its
infancy until the late 2010s. Since then, algorithms combining more
sophisticated methods, including genetic algorithms and the use of distributed
intensive computing, have made it possible to make a significant qualitative
leap forward in the exploration and validation of models. The result is an
epistemological turn for the human and social sciences, as indicated by the
latest applications realized with the help of the OpenMOLE platform presented
here.
