  Observations are an essential component of the simulation based studies on
artificial-evolutionary systems (AES) by which entities are identified and
their behavior is observed to uncover higher-level "emergent" phenomena.
Because of the heterogeneity of AES models and implicit nature of observations,
precise characterization of the observation process, independent of the
underlying micro-level reaction semantics of the model, is a difficult problem.
Building upon the multiset based algebraic framework to characterize
state-space trajectory of AES model simulations, we estimate bounds on
computational resource requirements of the process of automatically discovering
life-like evolutionary behavior in AES models during simulations. For
illustration, we consider the case of Langton's Cellular Automata model and
characterize the worst case computational complexity bounds for identifying
entity and population level reproduction.
