How Turing parasites expand the computational landscape of digital life

TL;DR

This paper presents a computational model demonstrating how parasitic interactions can drive the evolution of increased complexity in digital life forms, highlighting parasites as key evolutionary agents.

Contribution

It introduces a simple coevolving agent-parasite model showing how parasitism can actively promote complexity beyond basic replicators.

Findings

Hosts expand computational complexity to escape parasites

Parasites evolve increased complexity in response

Complexity escalation can lead to ecological collapse

Abstract

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.