  Why are living systems complex? Why does the biosphere contain living beings
with complexity features beyond those of the simplest replicators? What kind of
evolutionary pressures result in more complex life forms? These are key
questions that pervade the problem of how complexity arises in evolution. One
particular way of tackling this is grounded in an algorithmic description of
life: living organisms can be seen as systems that extract and process
information from their surroundings in order to reduce uncertainty. Here we
take this computational approach using a simple bit string model of coevolving
agents and their parasites. While agents try to predict their worlds, parasites
do the same with their hosts. The result of this process is that, in order to
escape their parasites, the host agents expand their computational complexity
despite the cost of maintaining it. This, in turn, is followed by increasingly
complex parasitic counterparts. Such arms races display several qualitative
phases, from monotonous to punctuated evolution or even ecological collapse.
Our minimal model illustrates the relevance of parasites in providing an active
mechanism for expanding living complexity beyond simple replicators, suggesting
that parasitic agents are likely to be a major evolutionary driver for
biological complexity.
