  Is there a relationship between computing costs and the confidence people
place in the behavior of computing systems? What are the tuning knobs one can
use to optimize systems for human confidence instead of correctness in purely
abstract models? This report explores these questions by reviewing the
mechanisms by which people build confidence in the match between the physical
world behavior of machines and their abstract intuition of this behavior
according to models or programming language semantics. We highlight in
particular that a bottom-up approach relies on arbitrary trust in the accuracy
of I/O devices, and that there exists clear cost trade-offs in the use of I/O
devices in computing systems. We also show various methods which alleviate the
need to trust I/O devices arbitrarily and instead build confidence
incrementally "from the outside" by considering systems as black box entities.
We highlight cases where these approaches can reach a given confidence level at
a lower cost than bottom-up approaches.
