computational complexity - What is the probability a random Turing machine is isomorphic to a DFA?

Let me focus on the question of your title, and mention
that there is another quite robust way to understand what
it means to say that a random Turing machine has
such-and-such property.

Specifically, we use the concept of asymptotic density as
the size of the program increases. For any natural number
$n$, there are only finitely many Turing machine program
using $n$ states. The asymptotic density or asymptotic probability
of a set $A$ of Turing machine programs is the limit (if it
exists)

• $lim_{ntoinfty} frac{|Acap P_n|}{|P_n|}$,

where $P_n$ is finite the set of Turing machine programs
with exactly $n$ states. Thus, the asymptotic probability
of a set $A$ of Turing machine programs is simply the limit
of the proportion of $n$-state programs in $A$. In
particular, if a set $A$ has asymptotic density $1$, then
it means that more than $99%$, more than $99.9%$, of
Turing machine programs are in $A$, as close to $1$ as
desired as the number of states increases. In this case, we
would seem to be justified in saying that almost every
Turing machine program is in $A$.

One can interpret your title question this way as
inquiring: what is the asymptotic density of the set of
Turing machine programs that decide sets that are
equivalent to a DFA?

To give an elementary sample calculation, a Turing machine
program $p$ in finite alphabet $Sigma$ with states $S$
(not counting the halt state) is a function
$Sigmatimes Sto Sigmatimes (Scup{halt})times{L,R}$. For example, if the alphabet
has $2$ symbols and there are $n$ states, then there are
$(4(n+1))^{2n}$ many programs. The number of programs that
never transition to the halt state, however, is
$(4n)^{2n}$, which has proportion $(frac{n}{n+1})^{2n}$,
which goes to $frac{1}{e^2}$ as $ntoinfty$. Thus, the
density of programs that never halt at all, because they
can never transition to the halt state, is $frac{1}{e^2}$,
or about $13.5%$. Of course, all such programs decide the
empty language, which is also decided by a DFA, so this is
a lower bound on the title question.

This way of thinking is the foundation of the topic of
generic case
complexity.
A central concern of this topic is the fact that many
undecidable or unfeasible decision problems admit a black
hole, a very small region where the problem is difficult,
outside of which it is easy. For example, it is not good to
base a financial encryption scheme on a problem whose
difficulty is confined to a black hole---a robber is after
all satisfied to rob the bank even only $90%$ of the time,
or even only $1%$ of the time. Alexei Miasnikov inquired
whether the halting problem itself admits a black hole, and
it turned out that for one of the standard models of
computability, the answer is yes:

Theorem.(Hamkins+Miasnikov) For the Turing machine
model with one-way infinite tapes, there is a set of Turing
machine programs $A$ such that

• $A$ has asymptotic density $1$, so almost every program
  is in $A$.


• $A$ is polynomial time decidable.


• The halting problem is polynomial time decidable for
  programs in $A$.

Thus, for this model of computation, the halting problem is
decidable with probability $1$. The reason has to do with
the fact that for the one-way infinite tape Turing machine
model, it turns out that almost every Turing machine
program, like Polya's drunken man, falls off the tape
before repeating a state. And this is something that can be
detected in linear time. It follows that with asymptotic
probability one, a Turing machine program computes a finite
set. Since all finite sets are DFA computable, we conclude:

Corollary. For the standard one-way infinite tape
model of Turing machines, with asymptotic probability one,
a random Turing machine computes a set that is DFA
decidable.

See J. D. Hamkins, A. Miasnikov, The halting problem is
decidable on a set of asymptotic probability
one, Notre Dame Journal
of Formal Logic, Notre Dame J. Formal Logic 47 (2006),
515–524.

See also this MO
answer,
which mentions similar ideas.

• Next Localization of power series and module structure
• Previous dg.differential geometry - Variational characterization of curvature?