  The development of new earthquake forecasting models is often motivated by
one of the following complementary goals: to gain new insights into the
governing physics and to produce improved forecasts quantified by objective
metrics. Often, one comes at the cost of the other. Here, we propose a
question-driven ensemble (QDE) modeling approach to address both goals. We
first describe flexible ETAS models in which we relax the assumptions of
parametrically defined aftershock productivity and background earthquake rates
during model calibration. Instead, both productivity and background rates are
calibrated with data such that their variability is optimally represented by
the model. Then we consider 64 QDE models in pseudo-prospective forecasting
experiments for Southern California and Italy. QDE models are constructed by
combining model parameters of different ingredient models, where the rules for
how to combine parameters are defined by questions about the future seismicity.
The QDE models can be interpreted as models which address different questions
with different ingredient models. We find that certain models best address the
same issues in both regions, and that QDE models can substantially outperform
the standard ETAS and all ingredient models. The best performing QDE model is
obtained through the combination of models allowing flexible background
seismicity and flexible aftershock productivity, respectively, where the former
parameterizes the spatial distribution of background earthquakes and the
partitioning of seismicity into background events and aftershocks, and the
latter is used to parameterize the spatio-temporal occurrence of aftershocks.
