Saw this very interesting episode of Sean Carroll’s Mindscape podcast, where he interviews Blaise Agüera on the emergence of replication and computation:
The paper is here: https://arxiv.org/pdf/2406.19108
Much research on OoL and ALife focuses on the life period when self-replicators are already abundant. A central question during this period is: How do variation and complexity arise from simple self-replicators? Analyses often take the form of mathematical models and simulations [21]. In ALife, researchers often focus on selection for complex behaviors [22], which may include interactions with other agents [23]. Simulations may include tens of thousands of parameters and complex virtual ecosystems [24], but they can rarely modify the means of self-replication beyond adapting the mutation rate. The two most notable exceptions use assembly-like languages as computational substrate. In Tierra [18], simple assembly programs have no goals but are given time to execute their program and access and modify nearby memory. This causes them to self-replicate and manifest limited but interesting dynamics, including the rise of “parasites” that feed off other self-replicators. Avida [19] functions similarly: assembly-like programs are left running their code for a limited time. They can also self-replicate, this time by allocating new memory, writing their program in the new space, and then splitting. Avida adds a concept of fitness, since performing auxiliary computation increases a replicator’s allotted execution time. Notably, both Tierra and Avida are seeded with a hand-crafted self-replicator, called the “ancestor”. This puts them squarely into “life” dynamics, but still allows for modification of the self-replication mechanism.
But how does life begin? How do we get from a pre-life period devoid of self-replicators to one abundant with them?
(…)
In this paper we focus on computational substrates formed atop various programming languages. Here we highlight some of the most relevant previous investigations of the pre-life period on such substrates [29, 30, 31]. In all of these investigations, and in ours as well, there is no explicit fitness function that drives complexification or self-replicators to arise. Nevertheless, complex dynamics happen due to the implicit competition for scarce
resources (space, execution time, and sometimes energy).
Blaise explains in the podcast with Sean that intriguingly they find that replicators, despite being incredibly rare among possible programs (the vast majority of which do nothing at all), appear to be attractors in the space of all possible programs.
Despite there being no explicitly defined fitness function that favors any particular result, Darwinian evolution nevertheless unavoidably results from competition among replicators, and that replicators, at least initially, quickly grow more complex over time despite this also not being explicitly defined to be favorable (which shows how it being favored is a truly emergent byproduct of the competition among replicators).
There is also a video here depicting the graphical interface that shows when the replicators emerge:
