  Bayesian maxent lets one integrate thermal physics and information theory
points of view in the quantitative study of complex systems. Since net
surprisal (a free energy analog for measuring "departures from expected")
allows one to place second law constraints on mutual information (a
multi-moment measure of correlations), it makes a quantitative case for the
role of reversible thermalization in the natural history of invention, and
suggests multiscale strategies to monitor standing crop as well. It prompts one
to track evolved complexity starting from live astrophysically-observed
processes, rather than only from evidence of past events. Various gradients and
boundaries that play a role in availability flow, ranging from the edge of a
wave-packet to the boundary between idea-pools, allow one to frame wide-ranging
correlations (including that between a phenomenon and its explanation) as
delocalized {\em physical} structures.
