Not only is C.S. Lewis's argument valid, but it is also finding its quantification and articulation in computer science.

**Unsupervised Learning**

The closest approximation that we can come to Lewis' idea of an accident giving "a correct account of all the other accidents" is in the field of machine learning. Within that field, there is a distinction made between supervised and unsupervised learning based on whether or not training data is utilized to prepare the computer for the actual test data. In the case of unsupervised learning, the computer is presented with the test data with no preparation. Although this may appear to be learning from a Lockean *tabula rasa*, the fact is that even in unsupervised learning, certain assumptions are always incorporated to make it possible for the computer to process the data:

"In the classical literature, most unsupervised learning algorithms
are essentially algorithms for performing on-line clustering of the
data. They are based on the assumption that clusters are likely to
correspond to categories — an instance of the general epistemological
assumption that 'nature is not a cryptographer.' This important
assumption, which has extensive empirical support even if its
philosophical status is not entirely clear (Wigner 1960), provides
justification for the study of unsupervised learning algorithms. The
need for such assumptions however is not restricted to the
unsupervised learning paradigm - assumptions that nature is 'simple'
are also necessary for the theory of supervised learning. The major
problem in supervised learning is that of interpolating from the
categories of the data in the training set to the categories of novel
inputs. Such interpolation can only be based on assumptions about the
simplicity of the natural process that generates the data." (Michael
I. Jordan and Robert A. Jacobs, "Modularity, Unsupervised Learning,
and Supervised Learning")

Peter Dayan of MIT also comments on the need for *a priori* information as one of the prerequisites for unsupervised learning:

"The only things that unsupervised learning methods have to work with
are the observed input patterns xi, which are often assumed to be
independent samples from an underlying unknown probability
distribution PI [x], and some explicit or implicit *a priori*
information as to what is important." (Peter Dayan, "Unsupervised
Learning")

Any type of algorithm that is used for machine learning starts with assumptions with respect to the existence and value of data. These assumptions are programmed into the algorithm, and the computer is given all the tools that are necessary to collected data and perform various statistical operations on it. Far from being accidental, unsupervised machine learning involves sophisticated statistical operations and computation.

**No Free Lunch**

In spite of the fact that machine learning has *a priori* knowledge as its starting point, not all algorithms are created equal and no given algorithm is successful for all types of data. The study of this particular problem resulted what is known as the "No Free Lunch" (NFL) theorem, published in 1997 by David H. Wolpert and William G. Macready.

"Roughly speaking, we show that for both static and timedependent
optimization problems, the average performance of any pair of
algorithms across all possible problems is identical." (Wolpert and
Macready, "No Free Lunch Theorems for Optimization")

Even though the average performance of algorithms is identical when tested with all types of data, a given algorithm may be especially suited for a particular type of data. However, the NFL theorem also implies that its performance will suffer when tested with data for which is not suited.

"[T]he main message of the NFL theorems may be summarized as follows:
If there is no restriction on how the past (already visited points)
can be related to the future (not yet explored search points),
efficient search and optimization is impossible." (Christian Igels,
"No Free Lunch Theorems: Limitations and Perspectives of
Metaheuristics")

Succinctly expressed, the NFL theorem holds that not only are assumptions made when designing algorithms but their success is fully dependent on those assumptions:

"[I]f the practitioner has knowledge of problem characteristics but
does not incorporate them [...], the NFL theorems establish that there
are no formal assurances that the algorithm chosen will be at all
effective." (Wolpert and Macready, "No Free Lunch Theorems for
Optimization")

"[Y]ou can't make a clustering algorithm without making some
assumptions about the nature of those clusters." (David Robinson)

Even if it were possible for a genetic mutation to accidentally equip an organism with the ability to perform the biological equivalent of statistical calculations on its own physiological states, the practical utilization of those calculations has been demonstrated to be useless without some sort of *a priori* guidance. For this reason, the idea of accidental knowledge is and always will remain theoretically implausible.

If anyone cares for context, here it is.

– J.Todd – 2016-04-18T01:32:23.5031I fail to see, for example, how our accidental nature, or rather an apparent absence of universal teleological goals invalidates, for example, first order logic. And police and crime scene investigators perform deductive acts directly analogous to the milk jug example on a daily basis, and such deductions are often later proved true (by fired-hand confessions, eyewitness testimony corroborating the deductions, video which is later uncovered, etc). – Dan Bron – 2016-04-18T02:49:03.857

Can you make clearer what specifically is the problem. Right now it seems like this (1) here's an argument I found somewhere on the internet. (2) tell me if it is any good. – virmaior – 2016-04-18T02:52:50.310

3@virmaior It's a philosophical argument by C.S. Lewis. Is it not acceptable here to ask about the validity of a deeply philosophical argument made by a prominent historical figure? It's a very interesting argument, I just can't tell whether or not it's a valid one. I'm genuinely curious, so if there's any way i can modify my question I'll be happy to. – J.Todd – 2016-04-18T03:20:12.927

So you want someone to elucidate the structure of the argument? Or reveal its structure? – virmaior – 2016-04-18T04:15:34.443

1The philosophic question is still not clear. There are probably well-known philosophers that would both agree and rebut the paragraph. – James Kingsbery – 2016-04-18T04:46:57.387

Elucidate, I had to look that one up. Well, I'm not totally sure I understand what you mean by "structure", but put simply, I would like someone to disect and explain the argument, and based on logic and fact, determine whether it makes sense based on commonly accepted fact, or if it's using invalid logic. – J.Todd – 2016-04-18T04:47:39.870

@JamesKingsbery could I specify that I'm interested in answers based on observable fact? For example, as a comment above mentions? Would that help? – J.Todd – 2016-04-18T04:49:07.893

The first part "dissect and explain the argument" can be done objectively and is perfectly fine as a question. The second part is going to invite opinion-based answers and is not a good fit for SE since we're not here to tell you what to believe but rather to help you with problems you're having in understanding phillosophy. – virmaior – 2016-04-18T13:46:57.670

1@virmaior Did my edit satisfy you? I'm trying to meet the requirements that you explained. – J.Todd – 2016-04-18T18:53:01.847