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@misc{cohen-genericity-weak-genericity,
  title = {{Weakly 1-generic sets}},
  howpublished = {Lesson 2 of \emph{Cohen genericity}, Computability Theory course notes},
  url = {/courses/cohen-genericity/weak-genericity},
  note = {Unreleased working version}
}

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Lesson 2 of 5

Weakly 1-generic sets

Sets meeting every dense c.e. set of strings; Kurtz's theorem identifies their degrees with the hyperimmune degrees; weak n-genericity.

40 min3 questions
Cohen genericity · lesson 2 of 5
  1. Genericity and Baire category
  2. Weakly 1-generic sets
  3. 1-generic sets
  4. The Posner–Robinson theorem
  5. Arithmetic genericity

The first effective level of genericity asks a set to meet every dense set of strings that can be enumerated by an algorithm. These weakly 1-generic sets are not computable, and Kurtz showed that the degrees that compute one are exactly the degrees computing a function that escapes domination by every computable function. Genericity, a topological notion, thus coincides at this level with a notion of growth. This lesson proves Kurtz’s theorem and introduces the hierarchy of weak nn-genericity.

2.1 Definition

We fix a computable enumeration (We)(W_e) of the c.e. sets of strings, through codes of strings, with finite approximations We[t]W_e[t] computable from (e,t)(e, t) and increasing in tt.

Definition 2.1.#

A set GG is weakly 1-generic if it meets every dense c.e. set of strings. More generally, GG is weakly nn-generic if it meets every dense Σn0\Sigma^0_n set of strings.

There are countably many c.e. sets of strings, so weakly 1-generic sets exist, and the class of weakly nn-generic sets is co-meager for every nn (Proposition 1.6). A weakly 1-generic set is not computable: for a computable XX, the set {σ:σ⊀X}\{ \sigma : \sigma \not\prec X \} is dense and computable. Since the family of dense Σn+10\Sigma^0_{n+1} sets contains the dense Σn0\Sigma^0_n sets, weak (n+1)(n+1)-genericity implies weak nn-genericity.

2.2 Hyperimmune functions

Definition 2.2.#

A function f:N→Nf : \nat \to \nat is hyperimmune if no computable function dominates it, that is, for every computable gg there is nn with f(n)>g(n)f(n) > g(n). A set has hyperimmune degree if it computes a hyperimmune function.

Equivalently, a set has hyperimmune degree if and only if it is not computably dominated, that is, if some total function it computes is dominated by no computable function (Definition 3.3 of “Basis theorems”). A hyperimmune function exceeds every computable function at infinitely many points: if f(n)≤g(n)f(n) \leq g(n) for all n≥n0n \geq n_0, a finite modification of gg dominates ff everywhere. These functions and the corresponding sets are studied in the course on immunity and growth of functions (Immune and hyperimmune sets). Every non-computable Δ20\Delta^0_2 set has hyperimmune degree, since it computes its computation function, which no computable function dominates (Martin–Miller).

2.3 Kurtz’s theorem

Theorem 2.3 (Kurtz).#

For every set XX, the following are equivalent:

  1. XX has hyperimmune degree;
  2. XX computes a function hh that is equal to every computable function at infinitely many points;
  3. XX computes a weakly 1-generic set.
Proof of Theorem 2.3.

(3 ⇒ 2) Let G≤TXG \leq_T X be weakly 1-generic. For each mm, the set of strings with a 11 at some position at least mm is dense and computable, so GG is infinite. Let h(n)h(n) be the number of zeros of GG that follow position n−1n - 1 before the next 11: the largest kk such that (G ⁣↾n) 0k≺G(G \uh n)\, 0^k \prec G. It is GG-computable. Let ff be computable and m∈Nm \in \nat. The set {σ 0f(∣σ∣) 1:∣σ∣≥m}\{ \sigma\, 0^{f(|\sigma|)}\, 1 : |\sigma| \geq m \} is dense and computable, so GG has a prefix σ 0f(n) 1\sigma\, 0^{f(n)}\, 1 with n=∣σ∣≥mn = |\sigma| \geq m, and then h(n)=f(n)h(n) = f(n). So hh equals ff at infinitely many points.

(2 ⇒ 1) If hh equals every computable function infinitely often, then h+1h + 1 exceeds every computable function at some point.

(1 ⇒ 3) Let f≤TXf \leq_T X be hyperimmune. We build GG with oracle ff, one bit per stage: at stage ss, the prefix σs=G ⁣↾s\sigma_s = G \uh s is known. Say that ee is met at stage ss if some prefix of σs\sigma_s belongs to We[f(t)]W_e[f(t)] for some t≤st \leq s; once met, ee stays met, since σs≺σs+1\sigma_s \prec \sigma_{s+1}. The construction may have a target: a pair (τ,e)(\tau, e) with τ⪰σs\tau \succeq \sigma_s and τ∈We\tau \in W_e.

Stage ss. Look for the least e≤se \leq s such that ee is not met at stage ss and some τ⪰σs\tau \succeq \sigma_s belongs to We[f(s)]W_e[f(s)]. If there is one, and there is no current target or the current target has an index larger than ee, make (τ,e)(\tau, e) the current target, for the first such τ\tau. Then let G(s)G(s) be the bit of the current target at position ss if the target is longer than ss, and 00 otherwise. When the target is a prefix of σs+1\sigma_{s+1}, it is achieved, ee is met, and there is no current target.

The construction is computable in ff, so G≤TXG \leq_T X. We show that GG meets every dense c.e. set WeW_e.

Each index sets a target finitely often. An index ee sets a target only when it is not met; its target is then copied bit by bit, and is achieved unless it is replaced by a target of smaller index. By induction on ee: if the indices smaller than ee set targets finitely often, then after they stop and their last targets are achieved or replaced, any target of ee is achieved, after which ee is met and never sets a target again.

Dense sets are met. Suppose WeW_e is dense and ee is never met. Let s0≥es_0 \geq e be a stage after which no index smaller than ee sets a target and none of their targets is current. If at some stage s≥s0s \geq s_0 some τ⪰σs\tau \succeq \sigma_s belonged to We[f(s)]W_e[f(s)], then ee would be the least index with a candidate (a smaller index with a candidate would set a target at stage ss, since no target of smaller index is current), ee would set a target unless its own target is current, and this target would be achieved, so ee would be met. Hence, for every s≥s0s \geq s_0, no extension of σs\sigma_s belongs to We[f(s)]W_e[f(s)]. Now let g(s)g(s) be the least tt such that every string of length ss has an extension in We[t]W_e[t]; gg is computable and total, since WeW_e is dense and there are finitely many strings of length ss. Since σs\sigma_s has length ss, the previous observation gives f(s)<g(s)f(s) < g(s) for all s≥s0s \geq s_0, so a finite modification of gg dominates ff, contradicting the hyperimmunity of ff.

The construction of (1 ⇒ 3) is a finite injury argument without restraints: an index may lose its target to an index of smaller code, but only finitely often. The hyperimmune function ff serves as a bound on the search for extensions: a dense c.e. set that is never met would provide a computable bound on ff.

Corollary 2.4.#
  1. Every non-computable Δ20\Delta^0_2 set, in particular every non-computable c.e. set, computes a weakly 1-generic set.
  2. A computably dominated set computes no weakly 1-generic set; in particular, no weakly 1-generic set is computably dominated.
  3. No weakly 2-generic set is computable in ∅′\emptyset'.
Proof of Corollary 2.4.
  1. A non-computable Δ20\Delta^0_2 set has hyperimmune degree, as recalled above.
  2. By the theorem, a set computing a weakly 1-generic set has hyperimmune degree.
  3. If X≤T∅′X \leq_T \emptyset', the set {σ:σ⊀X}\{ \sigma : \sigma \not\prec X \} is dense and computable in ∅′\emptyset', hence Σ20\Sigma^0_2; a weakly 2-generic set meets it, so it is not XX.

2.4 Exercises

Exercise 2.5 ★.

Show that if GG is weakly 1-generic, then so is its complement N∖G\nat \setminus G.

Solution

For a set of strings WW, let Wˉ={σˉ:σ∈W}\bar W = \{ \bar\sigma : \sigma \in W \}, where σˉ\bar\sigma flips all bits of σ\sigma. If WW is c.e. and dense, so is Wˉ\bar W, since σ↦σˉ\sigma \mapsto \bar\sigma is a computable bijection preserving the prefix relation. GG meets Wˉ\bar W, so N∖G\nat \setminus G meets WW.

Exercise 2.6 ★★.

Show that the principal function pGp_G of a weakly 1-generic set GG is hyperimmune.

Solution

Recall from the proof of Theorem 2.3 that GG is infinite, so pG(n)p_G(n), the nn-th element of GG in increasing order (counting from 00), is defined for every nn. Let ff be computable. The set D={σ 0f(∣σ∣)+1:σ∈2<N}D = \{ \sigma\, 0^{f(|\sigma|) + 1} : \sigma \in \bstr \} is dense and computable, so GG has a prefix σ 0f(n)+1\sigma\, 0^{f(n) + 1}, with n=∣σ∣n = |\sigma|. At most nn elements of GG lie below nn, so the elements of index 0,…,n0, \dots, n in increasing order cannot all lie below nn, and pG(n)≥np_G(n) \geq n. No element of GG lies in the interval [n,n+f(n)][n, n + f(n)], so pG(n)≥n+f(n)+1>f(n)p_G(n) \geq n + f(n) + 1 > f(n). Hence no computable function dominates pGp_G.

2.5 Notes and references

Weakly 1-generic sets and the equivalence with hyperimmune degrees are due to Kurtz (1981), in his thesis on randomness and genericity; the presentation of (1 ⇒ 3) by one bit per stage follows the standard modern proofs. At the next level, Andrews, Gerdes and Miller showed that a set computes a weakly 2-generic set if and only if it computes a function dominated by no Δ20\Delta^0_2 function. Downey and Hirschfeldt’s book treats these notions in detail.

  • S. A. Kurtz, Randomness and genericity in the degrees of unsolvability, PhD thesis, University of Illinois at Urbana-Champaign, 1981.
  • U. Andrews, P. Gerdes and J. S. Miller, The degrees of bi-hyperhyperimmune sets, Annals of Pure and Applied Logic 165 (2014), 803–811.
  • R. G. Downey and D. R. Hirschfeldt, Algorithmic Randomness and Complexity, Springer, 2010.