  Chaitin's work, in its depth and breadth, encompasses many areas of
scientific and philosophical interest. It helped establish the accepted
mathematical concept of randomness, which in turn is the basis of tools that I
have developed to justify and quantify what I think is clear evidence of the
algorithmic nature of the world. To illustrate the concept I will establish
novel upper bounds of algorithmic randomness for elementary cellular automata.
I will discuss how the practice of science consists in conceiving a model that
starts from certain initial values, running a computable instantiation, and
awaiting a result in order to determine where the system may be in a future
state--in a shorter time than the time taken by the actual unfolding of the
phenomenon in question. If a model does not comply with all or some of these
requirements it is traditionally considered useless or even unscientific, so
the more precise and faster the better. A model is thus better if it can
explain more with less, which is at the core of Chaitin's "compression is
comprehension". I will pursue these questions related to the random versus
possibly algorithmic nature of the world in two directions, drawing heavily on
the work of Chaitin. I will also discuss how the algorithmic approach is
related to the success of science at producing models of the world, allowing
computer simulations to better understand it and make more accurate predictions
and interventions.
