Blurb::
Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm
Description::
In the multilevel method a variance of the ``allocation_target`` is computed for each of the responses and their levels :math:`Y^i_\ell, i = 1,..., R, \ell = 0,..., L` . Setting ``qoi_aggregation`` describes the rule on how to aggregate those variances over multiple response functions. Supported options are ``sum`` (default) and ``max``. For ``sum``, the variances are aggregated and a single sample allocation is computed. For ``max``, an individual sample allocation for each response using the respective variances over levels is computed and the maximum over all responses for each level is taken (worst case scenario allocation).

*Default Behavior*
"sum"
Topics::

Examples::
The following method block

.. code-block::

    method,
     model_pointer = 'HIERARCH'
            multilevel_sampling
       pilot_samples = 20 seed = 1237
       convergence_tolerance = .01
       allocation_target = mean
          qoi_aggregation = sum


uses the sum rule to aggregate the variance over the qois.
Theory::

Faq::

See_Also::
