  Many scientific investigations of photometric galaxy surveys require redshift
estimates, whose uncertainty properties are best encapsulated by photometric
redshift (photo-z) posterior probability density functions (PDFs). A plethora
of photo-z PDF estimation methodologies abound, producing discrepant results
with no consensus on a preferred approach. We present the results of a
comprehensive experiment comparing twelve photo-z algorithms applied to mock
data produced for The Rubin Observatory Legacy Survey of Space and Time (LSST)
Dark Energy Science Collaboration (DESC). By supplying perfect prior
information, in the form of the complete template library and a representative
training set as inputs to each code, we demonstrate the impact of the
assumptions underlying each technique on the output photo-z PDFs. In the
absence of a notion of true, unbiased photo-z PDFs, we evaluate and interpret
multiple metrics of the ensemble properties of the derived photo-z PDFs as well
as traditional reductions to photo-z point estimates. We report systematic
biases and overall over/under-breadth of the photo-z PDFs of many popular
codes, which may indicate avenues for improvement in the algorithms or
implementations. Furthermore, we raise attention to the limitations of
established metrics for assessing photo-z PDF accuracy; though we identify the
conditional density estimate (CDE) loss as a promising metric of photo-z PDF
performance in the case where true redshifts are available but true photo-z
PDFs are not, we emphasize the need for science-specific performancemetrics.
