Computer modelling for evolutionary systems consists in: (1) to store in the memory the individual features of each member of a large population; and (2) to update the whole system repeatedly, as time goes by, according to some prescribed rules (reproduction, death, ageing, etc.) where some degree of randomness is included through pseudo-random number sequences. Compared to direct observation of nature, this approach presents two distinguishing features. First, one can follow the characteristics of the system in real time, instead of only observing the current, static situation which is a long-term consequence of a remote past completely unknown except for some available fossil snapshots. In particular, one can repeat the whole dynamical process, starting from the same initial population, using the same randomness, changing only some minor contingency during the process, in order to study its long-term consequences. Second, evolution necessarily follows a critical dynamics with long-term memory characteristics, equivalent to the long-range correlations responsible for the well-known universality properties of static critical phenomena. Accordingly, some strong simplifications can be applied, allowing one to obtain many characteristics of real populations from toy models easily implementable on the computer.
