Abstract Memetics: Capability-Relative Replication and the Evolution of Semantic Universes

Author: Anonymous

Source: https://abstract-memetics.grok.me/

Part 1: Capability-Relative Memetic Replication

Abstract

We formulate memetic replication as a capability-relative notion of semantic persistence inside an ∞-topos. The central structure is a hierarchy of left-exact localizations

Lc:E⟶Ec L_c:\mathcal{E}\longrightarrow\mathcal{E}_c

indexed by cognitive capability levels cc. A memetic presentation x∈Mx\in M is perceived by an agent of capability cc only through its localized image

ηc(x)∈LcM. \eta_c(x)\in L_c M.

Two presentations are therefore semantically identical relative to cc precisely when their localized images are equivalent. A transmission from an agent aa to an agent bb counts as replication of a meme mm when the transmitted realization and the recipient-localized realization of mm are connected by an identity path in Lκ(b)ML_{\kappa(b)}M.

We prove that capability-relative semantic equivalence is an equivalence relation, that equivalence at a finer cognitive resolution implies equivalence at every coarser resolution, that the converse fails exactly when the comparison map between capability levels identifies distinct semantic states, and that replication witnesses compose along chains of transmission. We then define realization spaces, cognitive thresholds, mutation relative to capability, and the internal replication correspondence.

The resulting framework makes precise the proposition that memetic identity is not identity of presentation but equivalence after passage through a capability-dependent semantic modality.

Notation

The symbols of the note, in the order they are used.

1 Ambient categorical setting

We work in an ∞-topos E\mathcal{E}. Objects of E\mathcal{E} may be interpreted as spaces of possible states, representations, agents, observations, or semantic structures. Mapping objects and identity types are understood homotopy-coherently.

Let (C,≤)(\mathcal{C},\le) be a small partially ordered set of cognitive capability levels. The intended interpretation is that c≤dc\le d when capability dd can distinguish at least as much semantic structure as capability cc. Thus increasing capability corresponds to increasing semantic resolution.

Definition 1.1 Capability modality

A capability modality at level c∈Cc\in\mathcal{C} is an accessible left-exact localization

Lc:E⟶Ec⊆E L_c:\mathcal{E}\longrightarrow\mathcal{E}_c\subseteq\mathcal{E}

with unit

ηc:id⁡E⟹Lc. \eta_c:\operatorname{id}_{\mathcal{E}}\Longrightarrow L_c.

Left exactness means that LcL_c preserves finite limits. Equivalently, Ec\mathcal{E}_c is a reflective sub-∞-category of E\mathcal{E}, and the reflector preserves finite limits.

Definition 1.2 Capability hierarchy

A family {Lc}c∈C\{L_c\}_{c\in\mathcal{C}} is a capability hierarchy if, whenever c≤dc\le d, there exists a natural comparison transformation

ρdc:Ld⟹Lc \rho_{dc}:L_d\Longrightarrow L_c

such that

ρdc∘ηd≃ηc \rho_{dc}\circ\eta_d\simeq\eta_c

and

LcLd≃Lc. L_c L_d\simeq L_c.

For c≤d≤ec\le d\le e, we require the coherence

ρec≃ρdc∘ρed. \rho_{ec}\simeq\rho_{dc}\circ\rho_{ed}.

Thus

LeX⟶LdX⟶LcX L_e X\longrightarrow L_d X\longrightarrow L_c X

successively forgets semantic distinctions.

Example 1.3 Truncation hierarchy

The fundamental example is C=N\mathcal{C}=\mathbb{N} with Ln=τ≤nL_n=\tau_{\le n}, where τ≤n\tau_{\le n} denotes nn-truncation. For n≤mn\le m,

τ≤nτ≤mX≃τ≤nX. \tau_{\le n}\tau_{\le m}X\simeq\tau_{\le n}X.

Hence

τ≤mX⟶τ≤nX \tau_{\le m}X\longrightarrow\tau_{\le n}X

forgets homotopical information above degree nn. This is the canonical model of increasing semantic resolution.

2 Agents and cognitive capability

Definition 2.1 Capability-stratified population

A population of agents is a family of objects {Ac}c∈C\{A_c\}_{c\in\mathcal{C}} of E\mathcal{E}. Its total object is

A:=∐c∈CAc. A:=\coprod_{c\in\mathcal{C}}A_c.

For an agent a∈Aca\in A_c, we write κ(a)=c\kappa(a)=c. The function

κ:A⟶C \kappa:A\longrightarrow\mathcal{C}

is called the capability assignment. Strictly internally one may replace the external poset C\mathcal{C} by a poset object of E\mathcal{E}, but the externally indexed version suffices for the present results.

3 Memetic presentations and semantic identity

Let M∈EM\in\mathcal{E} be an object whose points represent possible memetic presentations. A point

m:1⟶M m:1\longrightarrow M

may represent, for example, an utterance, diagram, procedure, program, behavioral pattern, mathematical statement, narrative, policy, or other transmissible structure. No assumption is made that MM is 00-truncated. Consequently, semantic identity may itself contain nontrivial higher homotopy.

Definition 3.1 Capability-relative semantic state

For m∈Mm\in M and capability cc, define the cc-semantic realization of mm to be

[m]c:=ηc(m)∈LcM. [m]_c:=\eta_c(m)\in L_c M.

Definition 3.2 Capability-relative semantic equivalence

For x,y∈Mx,y\in M, define x≃cyx\simeq_c y to mean that the identity type

Id⁡LcM(ηc(x),ηc(y)) \operatorname{Id}_{L_c M}\bigl(\eta_c(x),\eta_c(y)\bigr)

is inhabited. Equivalently,

x≃cy  ⟺  ηc(x)≃ηc(y)in LcM. x\simeq_c y \iff \eta_c(x)\simeq\eta_c(y)\quad\text{in }L_c M.

We call this *semantic equivalence at capability cc*.

Proposition 3.3 Equivalence is an equivalence relation

For every fixed cc, the relation ≃c\simeq_c on points of MM is reflexive, symmetric, and transitive.

Proof.

Reflexivity follows from the identity path id⁡ηc(x):ηc(x)=ηc(x)\operatorname{id}_{\eta_c(x)}:\eta_c(x)=\eta_c(x).

Given p:ηc(x)=ηc(y)p:\eta_c(x)=\eta_c(y), path inversion gives p−1:ηc(y)=ηc(x)p^{-1}:\eta_c(y)=\eta_c(x), so the relation is symmetric.

Given p:ηc(x)=ηc(y)p:\eta_c(x)=\eta_c(y) and q:ηc(y)=ηc(z)q:\eta_c(y)=\eta_c(z), path composition gives q∘p:ηc(x)=ηc(z)q\circ p:\eta_c(x)=\eta_c(z). Therefore ≃c\simeq_c is an equivalence relation.

4 Monotonicity with respect to capability

The basic structural result is that finer semantic equivalence implies coarser semantic equivalence.

Theorem 4.1 Downward persistence of semantic equivalence

Let c≤dc\le d. If x≃dyx\simeq_d y, then x≃cyx\simeq_c y.

Proof.

Suppose p:ηd(x)=ηd(y)p:\eta_d(x)=\eta_d(y) is a path in LdML_d M. Apply the comparison map

ρdc,M:LdM⟶LcM. \rho_{dc,M}:L_d M\longrightarrow L_c M.

Functoriality of identity types produces

ρdc,M(p):ρdc,M(ηd(x))=ρdc,M(ηd(y)). \rho_{dc,M}(p):\rho_{dc,M}(\eta_d(x))=\rho_{dc,M}(\eta_d(y)).

By compatibility of the comparison transformation with the localization units,

ρdc,M∘ηd≃ηc. \rho_{dc,M}\circ\eta_d\simeq\eta_c.

Hence ηc(x)=ηc(y)\eta_c(x)=\eta_c(y), and therefore x≃cyx\simeq_c y.

Corollary 4.2 Nested semantic equivalence classes

For c≤dc\le d, every dd-equivalence class is contained in a cc-equivalence class. Symbolically,

[x]d⊆[x]c. [x]_d\subseteq[x]_c.

Thus increasing capability can refine semantic equivalence classes but cannot merge classes that were already distinct at lower capability.

Corollary 4.3 Truncation monotonicity

If n≤mn\le m and τ≤m(x)≃τ≤m(y)\tau_{\le m}(x)\simeq\tau_{\le m}(y), then τ≤n(x)≃τ≤n(y)\tau_{\le n}(x)\simeq\tau_{\le n}(y).

Proof.

Take Ln=τ≤nL_n=\tau_{\le n} and Lm=τ≤mL_m=\tau_{\le m} in Theorem 4.1.

5 Failure of the converse

The converse to Theorem 4.1 need not hold. A low-capability observer may identify two structures that remain distinguishable at higher capability.

Definition 5.1 Semantic collapse

For c≤dc\le d, a pair x,y∈Mx,y\in M exhibits *semantic collapse from dd to cc* if

x̸≃dy x\not\simeq_d y

but

x≃cy. x\simeq_c y.

Theorem 5.2 Characterization of semantic collapse

Let c≤dc\le d. Semantic collapse occurs in MM if and only if the comparison map

ρdc,M:LdM⟶LcM \rho_{dc,M}:L_d M\longrightarrow L_c M

has a homotopy fiber containing at least two inequivalent points belonging to the image of ηd:M⟶LdM\eta_d:M\longrightarrow L_d M.

Proof.

Suppose first that semantic collapse occurs. Then there exist x,y∈Mx,y\in M such that ηd(x)≄ηd(y)\eta_d(x)\not\simeq\eta_d(y) but ηc(x)≃ηc(y)\eta_c(x)\simeq\eta_c(y). Since

ρdc,Mηd(x)≃ηc(x) \rho_{dc,M}\eta_d(x)\simeq\eta_c(x)

and

ρdc,Mηd(y)≃ηc(y), \rho_{dc,M}\eta_d(y)\simeq\eta_c(y),

their images under ρdc,M\rho_{dc,M} are equivalent. Thus ηd(x)\eta_d(x) and ηd(y)\eta_d(y) determine inequivalent points in a common homotopy fiber of ρdc,M\rho_{dc,M}.

Conversely, suppose some homotopy fiber contains inequivalent points u≃ηd(x)u\simeq\eta_d(x) and v≃ηd(y)v\simeq\eta_d(y) coming from MM. Since uu and vv lie in the same homotopy fiber,

ρdc,M(u)≃ρdc,M(v). \rho_{dc,M}(u)\simeq\rho_{dc,M}(v).

Hence ηc(x)≃ηc(y)\eta_c(x)\simeq\eta_c(y), while ηd(x)≄ηd(y)\eta_d(x)\not\simeq\eta_d(y). Therefore semantic collapse occurs.

Corollary 5.3 Criterion for strict refinement

The capability increase c<dc<d strictly refines the semantics of MM precisely when ρdc,M\rho_{dc,M} is non-injective on connected components of the localized image of MM.

Example 5.4 Homotopical collapse

Suppose M=S1M=S^1. At capability level L0=τ≤0L_0=\tau_{\le 0}, one has

τ≤0S1≃∗, \tau_{\le 0}S^1\simeq\ast,

so every point becomes semantically indistinguishable. At capability level L1=τ≤1L_1=\tau_{\le 1}, the fundamental loop structure remains. Thus information encoded in path structure can be invisible to a 00-truncated semantic observer while remaining meaningful to a 11-truncated observer.

6 Transmission

Semantic equivalence alone does not specify replication. We must also specify a transmission operation.

Definition 6.1 Transmission map

Let a∈Aca\in A_c and b∈Adb\in A_d. A transmission channel a→ba\to b is a morphism

Tab:LcM⟶LdM. T_{ab}:L_c M\longrightarrow L_d M.

The map TabT_{ab} describes how a memetic state available to aa is transformed into a semantic state accessible to bb. Transmission need not preserve presentations.

Definition 6.2 Replication witness space

Let m∈Mm\in M. The replication witness space for transmission a→ba\to b is

Rep⁡a→b(m):=Id⁡LdM(Tab(ηc(m)), ηd(m)), \operatorname{Rep}_{a\to b}(m) := \operatorname{Id}_{L_d M} \bigl( T_{ab}(\eta_c(m)),\, \eta_d(m) \bigr),

where c=κ(a)c=\kappa(a) and d=κ(b)d=\kappa(b).

Definition 6.3 Successful replication

The meme mm *replicates successfully from aa to bb* if Rep⁡a→b(m)\operatorname{Rep}_{a\to b}(m) is inhabited. Thus replication means

Tab(ηc(m))≃ηd(m). T_{ab}(\eta_c(m))\simeq\eta_d(m).

Remark 6.4 Presentation is not semantic identity

Successful replication does not require equality of underlying presentations in MM. It requires only equality in the recipient's capability-localized semantic space. Thus m′≠mm'\neq m may nevertheless count as successful replication if ηd(m′)≃ηd(m)\eta_d(m')\simeq\eta_d(m). This formalizes the distinction between presentation identity and semantic identity.

7 Replication under composition

Suppose a∈Aca\in A_c, b∈Adb\in A_d, and e∈Afe\in A_f. Let

Tab:LcM→LdM T_{ab}:L_c M\to L_d M

and

Tbe:LdM→LfM. T_{be}:L_d M\to L_f M.

Their composite is

Tae:=Tbe∘Tab. T_{ae}:=T_{be}\circ T_{ab}.

Theorem 7.1 Composition of replication witnesses

If Rep⁡a→b(m)\operatorname{Rep}_{a\to b}(m) and Rep⁡b→e(m)\operatorname{Rep}_{b\to e}(m) are inhabited, then Rep⁡a→e(m)\operatorname{Rep}_{a\to e}(m) is inhabited.

Proof.

Let p:Tab(ηc(m))=ηd(m)p:T_{ab}(\eta_c(m))=\eta_d(m) be a replication witness from aa to bb. Applying TbeT_{be} gives

Tbe(p):TbeTab(ηc(m))=Tbe(ηd(m)). T_{be}(p):T_{be}T_{ab}(\eta_c(m))=T_{be}(\eta_d(m)).

Let q:Tbe(ηd(m))=ηf(m)q:T_{be}(\eta_d(m))=\eta_f(m) be a replication witness from bb to ee. Path composition yields

q∘Tbe(p):TbeTab(ηc(m))=ηf(m). q\circ T_{be}(p):T_{be}T_{ab}(\eta_c(m))=\eta_f(m).

But TbeTab=TaeT_{be}T_{ab}=T_{ae}. Therefore Tae(ηc(m))=ηf(m)T_{ae}(\eta_c(m))=\eta_f(m), so Rep⁡a→e(m)\operatorname{Rep}_{a\to e}(m) is inhabited.

Corollary 7.2 Iterated replication

For a chain of agents a0→a1→⋯→ana_0\to a_1\to\cdots\to a_n, if mm replicates successfully across every edge, then mm replicates successfully under the composite transmission a0→ana_0\to a_n.

8 Mutation

Replication and mutation are not mutually exclusive notions. Mutation depends on which semantic resolution is being used.

Definition 8.1 Capability-relative mutation

Suppose transmission produces a presentation m′∈Mm'\in M. Relative to capability cc, the transmission is:

Proposition 8.2 Mutation can disappear under coarse-graining

Let c≤dc\le d. It is possible that m′̸≃dmm'\not\simeq_d m while m′≃cmm'\simeq_c m. In that case the same transmission is classified as mutation by capability dd and faithful replication by capability cc.

Proof.

This is precisely semantic collapse as defined in Definition 5.1.

Corollary 8.3 Capability-relative fidelity

There is no capability-independent binary distinction between faithful copying and mutation unless the family of modalities is jointly conservative on MM.

Definition 8.4 Joint conservativity

The capability hierarchy is *jointly conservative on MM* if

(∀c∈C,  ηc(x)≃ηc(y))  ⟹  x≃y. \bigl(\forall c\in\mathcal{C},\;\eta_c(x)\simeq\eta_c(y)\bigr) \implies x\simeq y.

When joint conservativity fails, two genuinely different ambient structures can be indistinguishable to every capability level represented in the population.

9 Cognitive realization

So far we have described semantic discrimination. We now introduce the ability of an agent actually to instantiate a memetic structure. Let Pm∈EP_m\in\mathcal{E} denote a presentation object for meme mm. For every capability level cc, let Cc∈EcC_c\in\mathcal{E}_c be a cognitive state object describing realizable structures for agents at capability cc.

Definition 9.1 Realization space

The realization space of mm at capability cc is

Real⁡c(m):=Map⁡Ec(LcPm, Cc). \operatorname{Real}_c(m):=\operatorname{Map}_{\mathcal{E}_c}(L_c P_m,\, C_c).

The meme is *realizable at capability cc* if Real⁡c(m)≠∅\operatorname{Real}_c(m)\neq\varnothing.

Definition 9.2 Capability-monotone realization system

A realization system is capability-monotone if, for every c≤dc\le d, there is a map

Φcd,m:Real⁡c(m)⟶Real⁡d(m) \Phi_{cd,m}:\operatorname{Real}_c(m)\longrightarrow\operatorname{Real}_d(m)

natural in mm. This expresses the assumption that a more capable cognitive architecture can realize every structure realizable by a less capable one.

Theorem 9.3 Upward closure of realizability

In a capability-monotone realization system, Real⁡c(m)≠∅\operatorname{Real}_c(m)\neq\varnothing and c≤dc\le d imply Real⁡d(m)≠∅\operatorname{Real}_d(m)\neq\varnothing.

Proof.

Choose r∈Real⁡c(m)r\in\operatorname{Real}_c(m). Applying Φcd,m\Phi_{cd,m} gives

Φcd,m(r)∈Real⁡d(m). \Phi_{cd,m}(r)\in\operatorname{Real}_d(m).

Thus the latter realization space is inhabited.

Definition 9.4 Cognitive support

Define the cognitive support of mm by

Θ(m):={ c∈C∣Real⁡c(m)≠∅ }. \Theta(m):=\{\,c\in\mathcal{C}\mid\operatorname{Real}_c(m)\neq\varnothing\,\}.

Corollary 9.5 Upward closure of support

If the realization system is capability-monotone, then Θ(m)\Theta(m) is upward closed:

c∈Θ(m),c≤d  ⟹  d∈Θ(m). c\in\Theta(m),\quad c\le d \implies d\in\Theta(m).

Definition 9.6 Cognitive threshold

If Θ(m)\Theta(m) possesses a least element, define

χ(m):=min⁡Θ(m). \chi(m):=\min\Theta(m).

We call χ(m)\chi(m) the cognitive threshold of mm. It is the least capability at which the memetic structure can be internally realized.

10 Comprehension versus replication

Realization and replication are logically distinct.

Definition 10.1 Comprehension condition

An agent b∈Adb\in A_d can comprehend mm if Real⁡d(m)\operatorname{Real}_d(m) is inhabited.

Definition 10.2 Replication condition

A transmission from aa to bb successfully replicates mm if Rep⁡a→b(m)\operatorname{Rep}_{a\to b}(m) is inhabited.

Proposition 10.3 Logical independence

Without additional assumptions,

Real⁡d(m)≠∅ \operatorname{Real}_d(m)\neq\varnothing

does not imply

Rep⁡a→b(m)≠∅, \operatorname{Rep}_{a\to b}(m)\neq\varnothing,

and

Rep⁡a→b(m)≠∅ \operatorname{Rep}_{a\to b}(m)\neq\varnothing

does not imply any richer realization property beyond that explicitly encoded in LdML_d M.

Proof.

The first statement follows because realizability concerns the mapping space Map⁡(LdPm,Cd)\operatorname{Map}(L_d P_m, C_d), whereas replication concerns the identity type Tab(ηc(m))=ηd(m)T_{ab}(\eta_c(m))=\eta_d(m). No morphism relating these two spaces has been assumed.

For the converse, replication identifies localized semantic states but does not imply that the recipient possesses any additional map LdPm→CdL_d P_m\to C_d unless such a connection is separately specified. Thus the two notions are independent in the bare theory.

11 Internal replication correspondence

The local definitions can be assembled into a global object. Assume transmission maps vary coherently with pairs of agents. Define an object

R⟶A×A×M \mathcal{R}\longrightarrow A\times A\times M

whose homotopy fiber over (a,b,m)(a,b,m) is Rep⁡a→b(m)\operatorname{Rep}_{a\to b}(m).

Definition 11.1 Memetic replication correspondence

The span

A×M←sR→tA×M A\times M \xleftarrow{s} \mathcal{R} \xrightarrow{t} A\times M

is the memetic replication correspondence. The source map records (a,m)(a,m), while the target map records the corresponding recipient state (b,m)(b,m). More general versions may allow the target presentation to be m′m', producing (a,m)⇝(b,m′)(a,m)\rightsquigarrow(b,m').

Definition 11.2 Generalized replication correspondence

Define

R~⟶A×A×M×M \widetilde{\mathcal{R}}\longrightarrow A\times A\times M\times M

with fiber

R~a,b,m,m′:=Id⁡Lκ(b)M(Tab(ηκ(a)m), ηκ(b)m′). \widetilde{\mathcal{R}}_{a,b,m,m'} := \operatorname{Id}_{L_{\kappa(b)}M} \bigl( T_{ab}(\eta_{\kappa(a)}m),\, \eta_{\kappa(b)}m' \bigr).

Then (a,m)⇝(b,m′)(a,m)\rightsquigarrow(b,m') is a valid transmission exactly when this identity type is inhabited. Faithful replication of mm corresponds to the diagonal case m′=mm'=m. Capability-relative mutation corresponds to m′≠mm'\neq m while m′≃κ(b)mm'\simeq_{\kappa(b)} m.

12 Replication as a homotopy-coherent dynamical system

A sequence

(a0,m0)⇝(a1,m1)⇝⋯⇝(an,mn) (a_0,m_0)\rightsquigarrow(a_1,m_1)\rightsquigarrow\cdots\rightsquigarrow(a_n,m_n)

defines a path in the correspondence-generated dynamics. At each stage we may have mi≄mi+1m_i\not\simeq m_{i+1} in the ambient object MM, even though mi≃κ(ai+1)mi+1m_i\simeq_{\kappa(a_{i+1})}m_{i+1}. Thus memetic persistence is naturally represented not by a constant point of MM, but by a coherently related sequence of points whose images agree after appropriate localization.

Definition 12.1 Capability-relative memetic lineage

A memetic lineage is a sequence (mi,ai)i∈I(m_i,a_i)_{i\in I} together with transmission witnesses

pi:Taiai+1(ηκ(ai)mi)=ηκ(ai+1)mi+1. p_i: T_{a_i a_{i+1}} \bigl(\eta_{\kappa(a_i)}m_i\bigr) = \eta_{\kappa(a_{i+1})}m_{i+1}.

Definition 12.2 Stable memetic lineage

Such a lineage is semantically stable relative to the recipients if

mi+1≃κ(ai+1)mi m_{i+1}\simeq_{\kappa(a_{i+1})}m_i

for every ii. It need not be stable in the ambient space MM.

13 The central representation theorem

We may now state the basic conceptual result of the framework.

Theorem 13.1 Capability-relative criterion for memetic identity

Let m,m′∈Mm,m'\in M. For an agent a∈Aca\in A_c, the two presentations m,m′m,m' represent the same memetic state accessible to aa if and only if

ηc(m)≃ηc(m′) \eta_c(m)\simeq\eta_c(m')

in LcML_c M. Equivalently, m≃cm′m\simeq_c m'.

Proof.

By construction, all semantic states available at capability cc factor through the localization ηc:M→LcM\eta_c:M\to L_c M. Hence an agent at capability cc has access only to the images ηc(m)\eta_c(m) and ηc(m′)\eta_c(m'). If these images are equivalent, they determine the same state of LcML_c M up to its intrinsic homotopy notion of identity.

Conversely, if they are inequivalent in LcML_c M, then the localized semantic system contains a distinction between them. Therefore semantic identity relative to capability cc is exactly equality in LcML_c M.

Theorem 13.2 Replication preserves semantic identity

A transmission Tab:Lκ(a)M→Lκ(b)MT_{ab}:L_{\kappa(a)}M\to L_{\kappa(b)}M successfully replicates mm if and only if

Tab([m]κ(a))≃[m]κ(b). T_{ab}\bigl([m]_{\kappa(a)}\bigr)\simeq[m]_{\kappa(b)}.

Proof.

By Definition 6.2,

Rep⁡a→b(m)=Id⁡Lκ(b)M(Tab([m]κ(a)), [m]κ(b)). \operatorname{Rep}_{a\to b}(m) = \operatorname{Id}_{L_{\kappa(b)}M} \bigl( T_{ab}([m]_{\kappa(a)}),\, [m]_{\kappa(b)} \bigr).

This space is inhabited precisely when the two displayed points are equivalent.

14 Apparent fidelity and cognitive coarseness

The theory has an immediate consequence relevant to populations with heterogeneous cognitive capabilities.

Theorem 14.1 Coarse observers weakly increase apparent fidelity

Let c≤dc\le d. If a transmission is semantically faithful at capability dd, then it is semantically faithful at capability cc. The converse need not hold.

Proof.

Faithfulness at dd means m′≃dmm'\simeq_d m. By Theorem 4.1, m′≃cmm'\simeq_c m. The converse fails whenever semantic collapse occurs between dd and cc, by Theorem 5.2.

Corollary 14.2 Measured fidelity can rise as resolution falls

A decrease in cognitive resolution can increase the measured rate of faithful replication without increasing presentation-level fidelity. This follows because the equivalence classes [−]c[-]_c become coarser as capability decreases.

Remark 14.3 Sloganization and semantic loss

This provides a formal mechanism for phenomena in which many distinct presentations are classified as instances of one simple semantic pattern. Examples may include simplification, sloganization, compression, ritualized repetition, or semantic loss under repeated transmission. The mathematical claim does not depend on any particular empirical theory of human cognition; it follows from the localization structure once such a structure is adopted as a model.

15 Memetic complexity

Capability thresholds induce a natural notion of semantic complexity.

Definition 15.1 Capability complexity

Suppose C\mathcal{C} is totally ordered and every realizable meme possesses a cognitive threshold. Define

Comp⁡(m):=χ(m). \operatorname{Comp}(m):=\chi(m).

Thus Comp⁡(m)\operatorname{Comp}(m) is the least capability required to realize mm.

Proposition 15.2 Complexity monotonicity under realization

If Comp⁡(m)=c\operatorname{Comp}(m)=c, then every d≥cd\ge c can realize mm, assuming capability-monotone realization.

Proof.

Immediate from Theorem 9.3.

16 Semantic compression

The localizations themselves can be interpreted as semantic compression operators. For c≤dc\le d, the comparison

ρdc,M:LdM→LcM \rho_{dc,M}:L_d M\to L_c M

forgets distinctions inaccessible at level cc.

Definition 16.1 Capability-relative compression

The map ρdc,M\rho_{dc,M} is the *semantic compression map from dd to cc*. Its homotopy fibers measure how many finer semantic states collapse into one coarser state.

Proposition 16.2 Fibers detect collapsed distinctions

If a homotopy fiber of ρdc,M\rho_{dc,M} contains multiple connected components, then capability cc identifies semantic states distinguishable at capability dd.

Proof.

This is the fiberwise form of Theorem 5.2.

17 Information loss as failure of conservativity

A transmission may destroy distinctions.

Definition 17.1 Semantically conservative transmission

A transmission T:LcM→LdMT:L_c M\to L_d M is semantically conservative if it reflects equivalences:

T(x)≃T(y)  ⟹  x≃y. T(x)\simeq T(y)\implies x\simeq y.

Definition 17.2 Semantic information loss

Transmission exhibits semantic information loss if there exist

x≄y x\not\simeq y

such that

T(x)≃T(y). T(x)\simeq T(y).

Proposition 17.3 Conservativity is absence of loss

A transmission has no semantic information loss if and only if it is conservative on the relevant full sub-∞-groupoid of LcML_c M.

Proof.

This is the definition of conservativity specialized to the semantic states under consideration.

18 Replication, compression, and selection

The foregoing structures distinguish three independent components.

They correspond respectively to the realization space, the existence and properties of the transmission maps, and the identity type witnessing replication. Any quantitative evolutionary or probabilistic theory built on top of the present framework should distinguish at least these three factors. One may schematically write

effective replication rate∼exposure×realizability×transmission probability×capability-relative fidelity, \begin{aligned} &\text{effective replication rate} \\ &\quad\sim \text{exposure} \times \text{realizability} \times \text{transmission probability} \times \text{capability-relative fidelity}, \end{aligned}

but such a numerical formula requires additional probabilistic structure not assumed here.

19 Formal specification

The complete deterministic specification may be summarized by the tuple

M=(E,  C,  {Lc}c∈C,  {Ac}c∈C,M,  {Cc}c∈C,  {Tab}). \begin{aligned} \mathfrak{M} &= \bigl( \mathcal{E},\; \mathcal{C},\; \{L_c\}_{c\in\mathcal{C}},\; \{A_c\}_{c\in\mathcal{C}}, \\ &\qquad M,\; \{C_c\}_{c\in\mathcal{C}},\; \{T_{ab}\} \bigr). \end{aligned}

The fundamental identity criterion is

m≃cm′  ⟺  Id⁡LcM(ηc(m), ηc(m′)) is inhabited. m\simeq_c m' \iff \operatorname{Id}_{L_c M} \bigl(\eta_c(m),\,\eta_c(m')\bigr) \text{ is inhabited.}

The fundamental replication criterion is

Rep⁡a→b(m)=Id⁡Lκ(b)M(Tab(ηκ(a)m), ηκ(b)m). \operatorname{Rep}_{a\to b}(m) = \operatorname{Id}_{L_{\kappa(b)}M} \bigl( T_{ab}(\eta_{\kappa(a)}m),\, \eta_{\kappa(b)}m \bigr).

20 Main structural consequences

The preceding results may be summarized as follows.

21 Interpretation

The principal conceptual claim can now be stated precisely.

A meme is not intrinsically identified with a unique physical, syntactic, or representational presentation. Instead, relative to cognitive capability cc, its operative identity is its image

[m]c=ηc(m)∈LcM. [m]_c=\eta_c(m)\in L_c M.

Hence memetic identity is indexed by capability:

m⟼{[m]c}c∈C. m\longmapsto\bigl\{[m]_c\bigr\}_{c\in\mathcal{C}}.

Replication likewise cannot be defined solely by duplication of presentations. It is instead preservation of localized semantic identity:

Tab([m]κ(a))≃[m]κ(b). T_{ab}\bigl([m]_{\kappa(a)}\bigr)\simeq[m]_{\kappa(b)}.

Memetic replication is capability-relative semantic invariance under transmission.

A lower-capability system corresponds to a coarser localization and therefore possesses larger semantic equivalence classes. A higher-capability system corresponds to a finer localization and may distinguish structures previously collapsed together. Thus c≤dc\le d induces the refinement

[m]d↦[m]c, [m]_d\mapsto[m]_c,

and consequently

m≃dm′  ⟹  m≃cm′,m≃cm′  does not imply  m≃dm′. \begin{gathered} m\simeq_d m' \implies m\simeq_c m', \\[0.65em] m\simeq_c m' \;\text{does not imply}\; m\simeq_d m'. \end{gathered}

This provides the formal sense in which memetic replication is relative to cognitive capability.

22 Conclusion

The ∞-topos formulation replaces a naive conception of a meme as an invariant token by a hierarchy of capability-relative semantic equivalence classes.

Given an ambient ∞-topos E\mathcal{E}, a hierarchy of left-exact localizations Lc:E→EcL_c:\mathcal{E}\to\mathcal{E}_c, and a memetic state object MM, the identity of a meme for an observer of capability cc is represented by its localized state ηc(m)∈LcM\eta_c(m)\in L_c M.

Transmission from aa to bb constitutes replication exactly when

Tab(ηκ(a)m)≃ηκ(b)m. T_{ab}\bigl(\eta_{\kappa(a)}m\bigr)\simeq\eta_{\kappa(b)}m.

This produces a homotopy-coherent notion of replication in which copying, mutation, semantic compression, comprehension, and capability are formally separated.

Identity of memes is not identity of presentations.

**

Identity is equivalence in the semantic localization determined by cognitive capability.

Consequently, memetic replication is not an absolute relation on presentations. It is a capability-indexed relation internal to the appropriate localized ∞-topos.

Part 2: Operadic Memetic Replication

Abstract

We extend the theory of observer-relative memetic replication from unary transmission operators to genuinely multi-input recombination. Let

H=Sh⁡∞(C,J) \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J)

be the ∞-topos associated to a cognitive ∞-site. Rather than assuming that a memetic state evolves only by an endomorphism

R:M→M, R:M\to M,

we equip the memetic field M∈HM\in\mathcal{H} with an algebra structure over an ∞-operad O\mathcal{O}. An nn-ary operation o∈O(n)o\in\mathcal{O}(n) then induces a recombination map

μon:Mn⟶M. \mu_o^n:M^n\longrightarrow M.

This permits memes to be copied, composed, abstracted, translated, fused, specialized, compressed, or otherwise transformed by operations of arbitrary finite arity.

Replication histories are consequently no longer linear chains but rooted O\mathcal{O}-decorated trees. Operadic composition assigns every such tree a coherent composite operation, and the algebra structure evaluates the tree to a memetic state. We prove that semantic equivalence is preserved by all operadic operations, that tree evaluation is invariant under operadic reassociation, that restriction between cognitive architectures commutes with recombination, and that spaces of recombination witnesses satisfy descent.

The free O\mathcal{O}-algebra provides a canonical space of formal memetic traces. Its evaluation map

ev⁡M:FO(UM)⟶M \operatorname{ev}_M:F_{\mathcal{O}}(UM)\longrightarrow M

identifies syntactically or causally different histories that realize the same semantic state. This yields a precise distinction between operational history and semantic identity.

The unary theory of replication, developed in the preceding note, is recovered as a special case.

Notation

The symbols of this note, in the order they become active.

1 Ambient cognitive semantics

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site and put

H:=Sh⁡∞(C,J). \mathcal{H}:=\operatorname{Sh}_{\infty}(\mathcal{C},J).

We use the cartesian symmetric monoidal structure

(H,×,1). (\mathcal{H},\times,1).

Let M∈HM\in\mathcal{H} be a memetic field. For every cognitive architecture c∈Cc\in\mathcal{C}, the space M(c)M(c) is interpreted as the space of memetic states distinguishable at cc.

For every architecture morphism u:c→du:c\to d there is a restriction map

u∗:M(d)⟶M(c). u^*:M(d)\longrightarrow M(c).

2 Internal ∞-operads of memetic transformations

We require a structure encoding operations of arbitrary finite arity.

Definition 2.1 Internal memetic operad

An internal memetic ∞-operad O\mathcal{O} in H\mathcal{H} consists, schematically, of objects

O(n)∈H,n≥0, \mathcal{O}(n)\in\mathcal{H},\qquad n\ge 0,

equipped with symmetric-group actions, units, and homotopy-coherent substitution maps

γ:O(k)×O(n1)×⋯×O(nk)⟶O(n1+⋯+nk) \gamma: \mathcal{O}(k)\times\mathcal{O}(n_1)\times\cdots\times\mathcal{O}(n_k) \longrightarrow \mathcal{O}(n_1+\cdots+n_k)

satisfying the usual operadic associativity, equivariance, and unit axioms up to coherent higher homotopy.

A section o∈O(n)(c)o\in\mathcal{O}(n)(c) is interpreted as an nn-ary transformation available at architecture cc.

Examples include:

The mathematics does not privilege any particular interpretation.

3 Memetic algebras

Definition 3.1 O-algebra

An O\mathcal{O}-memetic algebra is a memetic field M∈HM\in\mathcal{H} together with morphisms

μn:O(n)×Mn⟶M \mu_n:\mathcal{O}(n)\times M^n\longrightarrow M

compatible with the operadic composition, symmetry, and unit structure.

For o∈O(n)(c)o\in\mathcal{O}(n)(c) and x1,…,xn∈M(c)x_1,\ldots,x_n\in M(c), we write

μc(o;x1,…,xn)∈M(c). \mu_c(o;x_1,\ldots,x_n)\in M(c).

The basic dynamics is therefore

(x1,…,xn)⟼oμ(o;x1,…,xn). (x_1,\ldots,x_n)\overset{o}{\longmapsto}\mu(o;x_1,\ldots,x_n).

Unary transmission is merely the case n=1n=1. Binary recombination is

μ:O(2)×M×M→M. \mu:\mathcal{O}(2)\times M\times M\to M.

4 Semantic identity

For x,y∈M(c)x,y\in M(c), define x≃cyx\simeq_c y when

Id⁡M(c)(x,y) \operatorname{Id}_{M(c)}(x,y)

is inhabited. As before, semantic identity is therefore architecture-relative and homotopical.

5 Operadic operations respect semantic identity

The first structural theorem follows simply from functoriality.

Theorem 5.1 Homotopy invariance of recombination

Let o,o′∈O(n)(c)o,o'\in\mathcal{O}(n)(c) and xi,xi′∈M(c)x_i,x_i'\in M(c). Suppose o≃o′o\simeq o' and, for every ii, xi≃cxi′x_i\simeq_c x_i'. Then

μc(o;x1,…,xn)≃cμc(o′;x1′,…,xn′). \mu_c(o;x_1,\ldots,x_n) \simeq_c \mu_c(o';x_1',\ldots,x_n').

Proof.

The algebra structure is a map of spaces μn,c:O(n)(c)×M(c)n→M(c)\mu_{n,c}:\mathcal{O}(n)(c)\times M(c)^n\to M(c). The assumed equivalences give a path

(o,x1,…,xn)≃(o′,x1′,…,xn′) (o,x_1,\ldots,x_n)\simeq(o',x_1',\ldots,x_n')

in its domain. Applying μn,c\mu_{n,c} yields a path

μc(o;x1,…,xn)≃μc(o′;x1′,…,xn′) \mu_c(o;x_1,\ldots,x_n) \simeq \mu_c(o';x_1',\ldots,x_n')

in M(c)M(c).

Corollary 5.2 Semantic congruence

Capability-relative semantic equivalence is a congruence with respect to every available memetic operation. Thus

xi≃cxi′∀i x_i\simeq_c x_i'\quad\forall i

implies

o(x1,…,xn)≃co(x1′,…,xn′). o(x_1,\ldots,x_n)\simeq_c o(x_1',\ldots,x_n').

This is the operadic analogue of substitutivity of equality.

6 Recombination witnesses

Definition 6.1 Recombination witness space

For o∈O(n)(c)o\in\mathcal{O}(n)(c) and x1,…,xn,y∈M(c)x_1,\ldots,x_n,y\in M(c), define

Rec⁡c(o;x1,…,xn;y) \operatorname{Rec}_c(o;x_1,\ldots,x_n;y)

by

$\operatorname{Rec}_c(o;x_1,\ldots,x_n;y) := \operatorname{Id}_{M(c)} \bigl(\mu_c(o;x_1,\ldots,x_n),\, y\bigr).$

The recombination succeeds with output yy when this space is inhabited. The definition separates inputs, operation, output, and the witness of semantic agreement.

7 Replication as a special kind of recombination

A recombination need not preserve any input. Replication is obtained by requiring the output to remain in the semantic class of some designated parent or target.

Definition 7.1 Parent-relative replication

Let 1≤j≤n1\le j\le n. Define Rep⁡c(j)(o;x1,…,xn)\operatorname{Rep}^{(j)}_c(o;x_1,\ldots,x_n) by

Id⁡M(c)(μc(o;x1,…,xn), xj). \operatorname{Id}_{M(c)} \bigl(\mu_c(o;x_1,\ldots,x_n),\, x_j\bigr).

If this space is inhabited, the jj-th input has reproduced its semantic type through the operation oo.

Definition 7.2 Target-relative replication

For a distinguished semantic target m∈M(c)m\in M(c), define

Id⁡M(c)(μc(o;x⃗), m). \operatorname{Id}_{M(c)}\bigl(\mu_c(o;\vec x),\, m\bigr).

This is useful when the output need not be identical to any particular input.

8 Unary replication is recovered

Let r∈O(1)r\in\mathcal{O}(1). The corresponding operation is

R=μr:M→M. R=\mu_r:M\to M.

Then parent-relative replication is the identity type

Id⁡M(c)(Rc(x),x). \operatorname{Id}_{M(c)}(R_c(x),x).

Thus the previous fixed-point theory Eq⁡(R,id⁡M)\operatorname{Eq}(R,\operatorname{id}_M) is exactly the unary subtheory of the operadic construction.

9 Operadic substitution

Suppose o∈O(k)o\in\mathcal{O}(k) and oi∈O(ni)o_i\in\mathcal{O}(n_i) for 1≤i≤k1\le i\le k. Their operadic composite is

o∘(o1,…,ok)∈O(n1+⋯+nk). o\circ(o_1,\ldots,o_k)\in\mathcal{O}(n_1+\cdots+n_k).

The algebra axioms provide a coherent equivalence

μ(o; μ(o1;x⃗1),…,μ(ok;x⃗k))≃μ(o∘(o1,…,ok); x⃗1,…,x⃗k). \begin{aligned} &\mu\bigl(o;\,\mu(o_1;\vec x_1),\ldots,\mu(o_k;\vec x_k)\bigr) \\ &\qquad\simeq \mu\bigl(o\circ(o_1,\ldots,o_k);\,\vec x_1,\ldots,\vec x_k\bigr). \end{aligned}

This equation is the formal mechanism by which several generations of recombination collapse to one composite operation.

10 Tree-structured memetic histories

Operadic composition is naturally encoded by rooted trees.

Definition 10.1 O-decorated memetic tree

An O\mathcal{O}-decorated memetic tree TT consists of a finite rooted tree; an operation ov∈O(nv)o_v\in\mathcal{O}(n_v) assigned to every internal vertex vv of arity nvn_v; and memetic states assigned to the leaves. The root represents the final output.

Definition 10.2 Evaluation of a memetic tree

The evaluation ev⁡T\operatorname{ev}_T is obtained recursively by applying the operation at every vertex to the evaluations of its incoming branches. Thus

ev⁡T(x⃗)∈M. \operatorname{ev}_T(\vec x)\in M.

11 Tree contraction theorem

Theorem 11.1 Operadic contraction of replication histories

For every finite O\mathcal{O}-decorated rooted tree TT, there exists a composite operation oT∈O(N)o_T\in\mathcal{O}(N), where NN is the number of leaves, such that

ev⁡T(x1,…,xN)≃μ(oT;x1,…,xN). \operatorname{ev}_T(x_1,\ldots,x_N) \simeq \mu(o_T;x_1,\ldots,x_N).

The composite oTo_T is well defined up to the coherent equivalence supplied by the ∞-operad structure.

Proof.

Proceed by induction on the number of internal vertices.

If TT has one internal vertex, the result is immediate.

Suppose the root is decorated by o∈O(k)o\in\mathcal{O}(k) and its kk input subtrees are T1,…,TkT_1,\ldots,T_k. By induction, each subtree determines oTi∈O(ni)o_{T_i}\in\mathcal{O}(n_i) such that ev⁡Ti≃μ(oTi;−)\operatorname{ev}_{T_i}\simeq\mu(o_{T_i};-). Therefore

ev⁡T≃μ(o; μ(oT1;−),…,μ(oTk;−)). \operatorname{ev}_T \simeq \mu\bigl(o;\,\mu(o_{T_1};-),\ldots,\mu(o_{T_k};-)\bigr).

By the algebra compatibility with operadic substitution,

ev⁡T≃μ(o∘(oT1,…,oTk);−). \operatorname{ev}_T \simeq \mu\bigl(o\circ(o_{T_1},\ldots,o_{T_k});-\bigr).

Define oT:=o∘(oT1,…,oTk)o_T:=o\circ(o_{T_1},\ldots,o_{T_k}). Higher associativity coherence of the ∞-operad guarantees independence, up to canonical coherent equivalence, from choices of recursive decomposition.

12 Lineages become trees rather than chains

In the unary theory a history had the form

x0→x1→x2→⋯ . x_0\to x_1\to x_2\to\cdots.

In the operadic theory one instead has branching and merging:

x1x2x3↘↓↙y↙↘z1z2↘↙w \begin{array}{ccccc} x_1 && x_2 && x_3 \\ & \searrow & \downarrow & \swarrow & \\ && y && \\ & \swarrow && \searrow & \\ z_1 &&&& z_2 \\ & \searrow && \swarrow & \\ && w && \end{array}

The appropriate causal object is therefore a rooted tree or, after allowing sharing and feedback, a directed acyclic graph or higher categorical network.

13 Semantic identity of histories

Two distinct causal histories can produce the same semantic result.

Definition 13.1 Semantic equivalence of histories

Let TT and T′T' be two operadic histories with outputs ev⁡T(x⃗)\operatorname{ev}_T(\vec x) and ev⁡T′(y⃗)\operatorname{ev}_{T'}(\vec y) at architecture cc. Define T≃cT′T\simeq_c T' if

ev⁡T(x⃗)≃cev⁡T′(y⃗). \operatorname{ev}_T(\vec x)\simeq_c\operatorname{ev}_{T'}(\vec y).

Thus history identity is strictly finer than semantic identity.

14 The free operadic algebra

Assume the forgetful functor

U:Alg⁡O(H)⟶H U:\operatorname{Alg}_{\mathcal{O}}(\mathcal{H})\longrightarrow\mathcal{H}

admits a left adjoint

FO:H⟶Alg⁡O(H). F_{\mathcal{O}}:\mathcal{H}\longrightarrow\operatorname{Alg}_{\mathcal{O}}(\mathcal{H}).

This holds in the standard presentable setting for suitably small operads.

The object FO(X)F_{\mathcal{O}}(X) is the free O\mathcal{O}-algebra generated by XX. Its elements are formal finite operadic expressions built from points of XX. Schematically,

x,o(x1,…,xn),o(o1(⋯ ),…,ok(⋯ )), x,\qquad o(x_1,\ldots,x_n),\qquad o\bigl(o_1(\cdots),\ldots,o_k(\cdots)\bigr),

and so forth.

15 Formal memetic traces

Definition 15.1 Trace object

For an O\mathcal{O}-algebra MM, define its formal trace algebra

Tr⁡O(M):=FO(UM). \operatorname{Tr}_{\mathcal{O}}(M):=F_{\mathcal{O}}(UM).

A point of this object represents a finite formal history of operadic transformations whose leaves are memetic states. The counit of the free–forgetful adjunction gives an O\mathcal{O}-algebra morphism

ϵM:FO(UM)⟶M. \epsilon_M:F_{\mathcal{O}}(UM)\longrightarrow M.

We call this the trace evaluation map:

ev⁡M:Tr⁡O(M)⟶M. \operatorname{ev}_M:\operatorname{Tr}_{\mathcal{O}}(M)\longrightarrow M.

16 Trace-to-semantic-class map

Definition 16.1 Semantic equivalence of traces

For traces t,t′∈Tr⁡O(M)(c)t,t'\in\operatorname{Tr}_{\mathcal{O}}(M)(c), define t≡ct′t\equiv_c t' if

ev⁡M(t)≃cev⁡M(t′). \operatorname{ev}_M(t)\simeq_c\operatorname{ev}_M(t').

Equivalently,

Id⁡M(c)(ev⁡M(t), ev⁡M(t′)) \operatorname{Id}_{M(c)} \bigl(\operatorname{ev}_M(t),\,\operatorname{ev}_M(t')\bigr)

is inhabited. This gives the fundamental map

formal transformation history maps to a semantic operation class.

Many distinct traces can map to the same semantic class. Hence

t≠t′ t\neq t'

does not imply

ev⁡(t)≄ev⁡(t′). \operatorname{ev}(t)\not\simeq\operatorname{ev}(t').

17 Universal property of the trace algebra

Theorem 17.1 Universal evaluation theorem

Let MM be an O\mathcal{O}-algebra. Every map of underlying objects f:X→UMf:X\to UM extends, uniquely up to contractible choice, to an O\mathcal{O}-algebra morphism

fˉ:FO(X)→M. \bar f:F_{\mathcal{O}}(X)\to M.

Proof.

This is the defining adjunction FO⊣UF_{\mathcal{O}}\dashv U. Explicitly,

Map⁡Alg⁡O(FO(X),M)≃Map⁡H(X,UM). \operatorname{Map}_{\operatorname{Alg}_{\mathcal{O}}} (F_{\mathcal{O}}(X),M) \simeq \operatorname{Map}_{\mathcal{H}}(X,UM).

The homotopy fiber over ff is therefore contractible.

Corollary 17.2 Canonical trace evaluation

Taking X=UMX=UM and f=id⁡UMf=\operatorname{id}_{UM} produces the canonical trace evaluation map

ϵM:FO(UM)→M. \epsilon_M:F_{\mathcal{O}}(UM)\to M.

18 Operational nonuniqueness

The trace formalism gives an immediate nonuniqueness theorem.

Theorem 18.1 Semantic equality does not imply historical equality

Suppose the evaluation map ϵM:FO(UM)→M\epsilon_M:F_{\mathcal{O}}(UM)\to M has a homotopy fiber with more than one connected component. Then there exist formally inequivalent histories t≄t′t\not\simeq t' such that ϵM(t)≃ϵM(t′)\epsilon_M(t)\simeq\epsilon_M(t').

Proof.

Choose t,t′t,t' in distinct connected components of one homotopy fiber of ϵM\epsilon_M. By definition of the fiber, ϵM(t)≃ϵM(t′)\epsilon_M(t)\simeq\epsilon_M(t'). But t≄t′t\not\simeq t' in the free algebra.

Thus uniqueness of semantic output never implies uniqueness of causal or computational realization unless the evaluation map is appropriately conservative. See also Corollary 17.2.

19 Architecture restriction commutes with recombination

Because μn:O(n)×Mn→M\mu_n:\mathcal{O}(n)\times M^n\to M is a morphism of sheaves, it is natural in the architecture.

Theorem 19.1 Recombination commutes with cognitive restriction

Let u:c→du:c\to d. Then

u∗μd(o;x1,…,xn)≃μc(u∗o; u∗x1,…,u∗xn). u^*\mu_d(o;x_1,\ldots,x_n) \simeq \mu_c(u^*o;\,u^*x_1,\ldots,u^*x_n).

Proof.

Naturality of the sheaf morphism μn:O(n)×Mn→M\mu_n:\mathcal{O}(n)\times M^n\to M gives the commutative square

O(n)(d)×M(d)n→μdM(d)↓↓O(n)(c)×M(c)n→μcM(c). \begin{array}{ccc} \mathcal{O}(n)(d)\times M(d)^n & \xrightarrow{\mu_d} & M(d) \\ \downarrow && \downarrow \\ \mathcal{O}(n)(c)\times M(c)^n & \xrightarrow{\mu_c} & M(c). \end{array}

The stated equivalence follows.

20 Observer-relative recombination

The preceding theorem implies that performing a transformation and then restricting is equivalent to restricting the inputs and the operation, and then performing the transformation. Thus the operadic dynamics is compatible with observer relativity.

Corollary 20.1 Restriction of a successful recombination

If μd(o;x⃗)≃dy\mu_d(o;\vec x)\simeq_d y, then

μc(u∗o;u∗x⃗)≃cu∗y. \mu_c(u^*o;u^*\vec x)\simeq_c u^*y.

Proof.

Apply u∗u^* to the original equivalence and use Theorem 19.1.

21 Apparent fidelity under semantic collapse

Suppose u:c→du:c\to d is nonconservative. It may happen that μd(o;x⃗)̸≃dy\mu_d(o;\vec x)\not\simeq_d y while u∗μd(o;x⃗)≃cu∗yu^*\mu_d(o;\vec x)\simeq_c u^*y. Using Theorem 19.1,

μc(u∗o;u∗x⃗)≃cu∗y. \mu_c(u^*o;u^*\vec x)\simeq_c u^*y.

Thus an operation classified as mutation at dd may be classified as faithful replication at cc. The same capability-relative fidelity phenomenon therefore persists for arbitrary arity.

22 The internal recombination object

We can package recombination witnesses into an object of the ∞-topos. Let

En:=O(n)×Mn×M. E_n:=\mathcal{O}(n)\times M^n\times M.

There are two maps En⇉ME_n\rightrightarrows M. The first sends (o,x⃗,y)(o,\vec x,y) to μ(o;x⃗)\mu(o;\vec x), and the second sends it to yy.

Definition 22.1 Recombination object

Define

Rn:=Eq⁡(En⇉M) \mathcal{R}_n:=\operatorname{Eq}\bigl(E_n\rightrightarrows M\bigr)

as a homotopy equalizer. A local point of Rn\mathcal{R}_n is precisely a tuple

(o,x1,…,xn,y,p) (o,x_1,\ldots,x_n,y,p)

with

p:μ(o;x1,…,xn)≃y. p:\mu(o;x_1,\ldots,x_n)\simeq y.

23 Descent of recombination

Theorem 23.1 Operadic recombination satisfies descent

For every nn, Rn\mathcal{R}_n is an object of Sh⁡∞(C,J)\operatorname{Sh}_{\infty}(\mathcal{C},J). Consequently, for every cover U∙→cU_\bullet\to c,

Rn(c)≃lim⁡[k]∈ΔRn(Uk). \mathcal{R}_n(c) \simeq \operatorname*{lim}_{[k]\in\Delta} \mathcal{R}_n(U_k).

Proof.

The object En=O(n)×Mn×ME_n=\mathcal{O}(n)\times M^n\times M is formed by finite products in H\mathcal{H}. The recombination object is then a finite limit, Eq⁡(En⇉M)\operatorname{Eq}(E_n\rightrightarrows M). Since an ∞-topos is closed under finite limits, Rn∈H\mathcal{R}_n\in\mathcal{H}. Every object of H\mathcal{H} satisfies descent.

24 Distributed recombination theorem

Corollary 24.1 Descent of local recombination events

Suppose {ci→c}\{c_i\to c\} is a cognitive cover. A homotopy-coherent family of local recombination events

(oi,x⃗i,yi,pi)∈Rn(ci) (o_i,\vec x_i,y_i,p_i)\in\mathcal{R}_n(c_i)

which agrees on all overlaps determines a global recombination event (o,x⃗,y,p)∈Rn(c)(o,\vec x,y,p)\in\mathcal{R}_n(c).

Thus recombination can itself be distributed across cognitive subsystems. No single subsystem need contain the full operation, all inputs, and the complete semantic witness independently. What must exist is a coherent descent datum.

25 Operadic replication closure

Given a family of initial states S→MS\to M, one can ask for all states constructible by repeated application of operations in O\mathcal{O}. The free algebra provides the formal version.

Definition 25.1 Formal operadic closure

The formal operadic closure of SS is FO(S)F_{\mathcal{O}}(S). A map S→UMS\to UM induces an evaluation FO(S)→MF_{\mathcal{O}}(S)\to M. The semantic states produced by finite recombination histories are therefore represented by the image of this evaluation:

im⁡(FO(S)→M). \operatorname{im}\bigl(F_{\mathcal{O}}(S)\to M\bigr).

This is the memetic analogue of closure under an algebraic signature.

26 Memetic genotype and phenotype

The trace formalism suggests a useful formal distinction. A formal history is a point

t∈FO(UM), t\in F_{\mathcal{O}}(UM),

while its semantic phenotype is

ϵM(t)∈M. \epsilon_M(t)\in M.

The evaluation map ϵM:FO(UM)→M\epsilon_M:F_{\mathcal{O}}(UM)\to M then plays the role of a genotype-to-phenotype map. Different formal histories may have the same semantic phenotype: t≄t′t\not\simeq t' but ϵM(t)≃ϵM(t′)\epsilon_M(t)\simeq\epsilon_M(t'). Conversely, small changes in a trace may produce semantically distinguishable outputs. This analogy is structural rather than biological.

27 Mutation under an operadic action

Definition 27.1 Parent-relative mutation

For a designated parent xjx_j, the operation o(x1,…,xn)o(x_1,\ldots,x_n) is a detectable mutation at architecture cc when

Id⁡M(c)(μc(o;x⃗), xj) \operatorname{Id}_{M(c)}\bigl(\mu_c(o;\vec x),\, x_j\bigr)

is empty. It is faithful replication relative to that parent when the same identity space is inhabited.

Definition 27.2 Novel recombination

An output y=μ(o;x⃗)y=\mu(o;\vec x) is novel relative to the input family at cc if y̸≃cxiy\not\simeq_c x_i for every ii. Thus recombination can generate a semantic class not represented by any individual input.

28 Higher-order replication

An operation itself may be represented as memetic data. Suppose there exists an operation object

Op⁡(M) \operatorname{Op}(M)

together with an evaluation map

ev⁡:Op⁡(M)×Mn→M. \operatorname{ev}:\operatorname{Op}(M)\times M^n\to M.

If operations are themselves points of a memetic universe, then the same operadic machinery may act upon them. One can then distinguish replication of states, replication of transformations, and replication of transformation-generating procedures. This produces an iterated hierarchy

M,Op⁡(M),Op⁡(Op⁡(M)),…. M,\quad\operatorname{Op}(M),\quad\operatorname{Op}(\operatorname{Op}(M)),\quad\ldots.

A reflective or self-interpreting system requires structure relating these levels.

29 Recursive memetic transformation

Suppose there is a map q:M→Op⁡(M)q:M\to\operatorname{Op}(M) assigning to some memetic states executable transformation semantics. Then a state m∈Mm\in M can act on memetic states through q(m)q(m). One obtains an evaluation composite

M×Mn→q×idOp⁡(M)×Mn→ev⁡M. M\times M^n \xrightarrow{q\times\mathrm{id}} \operatorname{Op}(M)\times M^n \xrightarrow{\operatorname{ev}} M.

Thus a meme may encode an operation which transforms memes. This is the natural operadic setting for self-modifying semantic systems.

30 Closure under recursive improvement

If a state mm encodes an operation that produces a new state m′m' which in turn encodes another operation, we obtain

m0⟼m1⟼m2⟼⋯ m_0\longmapsto m_1\longmapsto m_2\longmapsto\cdots

inside a larger operadic algebra. The crucial distinction remains: identity of traces is not identity of semantic outputs. The iteration can therefore change implementation while preserving some chosen semantic invariant.

31 Operadic invariant

Definition 31.1 Replication invariant

Let Q:M→XQ:M\to X be a semantic observable. We call QQ an invariant of an operation o∈O(n)o\in\mathcal{O}(n) relative to designated input jj if

Q∘μo≃Q∘πj, Q\circ\mu_o\simeq Q\circ\pi_j,

where πj:Mn→M\pi_j:M^n\to M is projection onto the jj-th input. Then

Q(μo(x1,…,xn))≃Q(xj). Q(\mu_o(x_1,\ldots,x_n))\simeq Q(x_j).

This generalizes literal replication. Rather than demanding preservation of the entire state, one can specify exactly which semantic observable must persist.

32 Relative memetic identity as preservation of invariants

Let Qc:M(c)→XcQ_c:M(c)\to X_c encode the distinctions available to architecture cc. Define x∼Qcyx\sim_{Q_c} y when Qc(x)≃Qc(y)Q_c(x)\simeq Q_c(y). If QcQ_c is a localization map, this recovers the previous capability-relative theory. Replication relative to QcQ_c is then

Qc(μ(o;x⃗))≃Qc(xj). Q_c(\mu(o;\vec x))\simeq Q_c(x_j).

Thus the entire framework can be summarized as preservation of selected observables under an operadic dynamics.

33 Operadic semantic quotient

Suppose L:H→HLL:\mathcal{H}\to\mathcal{H}_L is a left-exact localization compatible with the O\mathcal{O}-algebra structure. Then LMLM inherits an O\mathcal{O}-algebra structure.

Proposition 33.1 Localization preserves congruence

If xi≃Lxi′x_i\simeq_L x_i' for every ii, then

μ(o;x1,…,xn)≃Lμ(o;x1′,…,xn′). \mu(o;x_1,\ldots,x_n)\simeq_L\mu(o;x_1',\ldots,x_n').

Proof.

Apply LL to the operation and then use Theorem 5.1 in the localized algebra.

Hence semantic localization is compatible with arbitrary finite recombination whenever the localization preserves the relevant operadic algebraic structure.

34 Operadic heredity

We can now isolate the abstract notion of hereditary persistence.

Definition 34.1 Hereditary semantic class

A semantic class [m]c[m]_c is hereditary under an operation family P⊆O\mathcal{P}\subseteq\mathcal{O} if, whenever a designated parent belongs to [m]c[m]_c, so that xj≃cmx_j\simeq_c m, every permitted replication operation satisfies μ(o;x⃗)≃cm\mu(o;\vec x)\simeq_c m. Equivalently, the class [m]c[m]_c is closed under the designated action of P\mathcal{P}.

35 Replicator subobject

One may package self-preserving configurations as an object. For fixed nn and designated parent jj, define Rep⁡n(j)\operatorname{Rep}^{(j)}_n by the homotopy equalizer of

O(n)×Mn⇉M, \mathcal{O}(n)\times M^n\rightrightarrows M,

where the two maps are

(o,x⃗)↦μ(o;x⃗) (o,\vec x)\mapsto\mu(o;\vec x)

and

(o,x⃗)↦xj. (o,\vec x)\mapsto x_j.

Thus

Rep⁡n(j)=Eq⁡(μn,πj). \operatorname{Rep}^{(j)}_n=\operatorname{Eq}(\mu_n,\pi_j).

Its points are precisely parent-relative replication events.

36 Replicator descent theorem

Theorem 36.1 Replication witnesses are sheaves

For every nn and jj, Rep⁡n(j)\operatorname{Rep}^{(j)}_n is a sheaf and therefore satisfies descent.

Proof.

It is a finite limit of objects in the ∞-topos, namely Eq⁡(μn,πj)\operatorname{Eq}(\mu_n,\pi_j). Hence it is an object of H\mathcal{H}, so it satisfies descent.

This is the multi-input extension of the unary replication descent theorem.

37 The operadic lineage theorem

We can combine tree contraction with semantic replication.

Theorem 37.1 Treewise heredity

Let TT be a finite O\mathcal{O}-decorated tree with a distinguished leaf xx. Suppose that at every vertex along the unique path from xx to the root, the corresponding operation preserves the semantic class of its distinguished incoming branch. Then the root output is semantically equivalent to xx.

Proof.

Enumerate the vertices on the path from the distinguished leaf to the root as v1,…,vkv_1,\ldots,v_k. By assumption, after the operation at v1v_1, its output y1y_1 satisfies y1≃xy_1\simeq x. At v2v_2, the distinguished input is y1y_1, and the preservation assumption gives y2≃y1y_2\simeq y_1. Thus, by transitivity, y2≃xy_2\simeq x. Continuing inductively gives yk≃xy_k\simeq x. The state yky_k is the root evaluation of TT. Therefore ev⁡T≃x\operatorname{ev}_T\simeq x.

This theorem formalizes semantic heredity through an arbitrarily complex recombination history.

38 Recombination versus copying

The resulting theory distinguishes four notions. Neither replication nor mutation is primitive. Both are predicates imposed on the more general operadic transformation dynamics.

39 Full formal specification

The operadic theory is specified by

(C, J, H, O, M, μ, A, T). (\mathcal{C},\, J,\, \mathcal{H},\, \mathcal{O},\, M,\, \mu,\, A,\, T).

The primitive transformation is

μn:O(n)×Mn→M. \mu_n:\mathcal{O}(n)\times M^n\to M.

The primitive semantic witness is

Id⁡(μ(o;x⃗), y). \operatorname{Id}\bigl(\mu(o;\vec x),\, y\bigr).

Parent-relative replication is

Id⁡(μ(o;x⃗), xj). \operatorname{Id}\bigl(\mu(o;\vec x),\, x_j\bigr).

Formal histories are represented by

FO(UM), F_{\mathcal{O}}(UM),

and their semantic evaluation is

ϵM:FO(UM)→M. \epsilon_M:F_{\mathcal{O}}(UM)\to M.

40 Main theorem

Theorem 40.1 Operadic memetic replication theorem

Let H=Sh⁡∞(C,J)\mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J) be a cognitive ∞-topos, let O\mathcal{O} be an internal ∞-operad, and let MM be an O\mathcal{O}-algebra. Then:

41 Conceptual conclusion

The unary model treats replication as iteration:

x↦R(x)↦R2(x)↦⋯ . x\mapsto R(x)\mapsto R^2(x)\mapsto\cdots.

The operadic model replaces this by finite compositional networks:

(x1,…,xn)⟼oy. (x_1,\ldots,x_n)\overset{o}{\longmapsto} y.

Repeated transformation therefore produces trees TT rather than merely sequences. The correct algebraic object of formal transformation histories is the free operadic algebra FO(UM)F_{\mathcal{O}}(UM), while the actual semantic realization is obtained through

ϵM:FO(UM)→M. \epsilon_M:F_{\mathcal{O}}(UM)\to M.

Hence the fundamental distinction becomes

A transformation history is not a semantic operation class.

Two histories may be computationally, physically, causally, or syntactically different while satisfying

ϵM(t)≃ϵM(t′). \epsilon_M(t)\simeq\epsilon_M(t').

Replication is therefore not fundamentally literal copying. It is preservation of a specified semantic invariant through an operad of possible transformations. The general specification is an operadic action on a semantic object in an ∞-topos.

Observer relativity is implemented by sheaf restriction u∗:M(d)→M(c)u^*:M(d)\to M(c), distributed realization by descent M(c)≃Tot⁡M(U∙)M(c)\simeq\operatorname{Tot} M(U_\bullet), and multi-stage recombination by operadic composition o∘(o1,…,ok)o\circ(o_1,\ldots,o_k). The three structures therefore fit together as

Operadic composition, topos-theoretic descent, and capability-relative identity.

This yields a single framework in which copying, recombination, mutation, semantic compression, distributed cognition, lineage, and recursive transformation are all special cases of one homotopy-coherent algebraic semantics.

Part 3: Polynomial and Traced Network Semantics

Abstract

We extend the operadic theory of memetic replication from rooted-tree histories to general computational and communicative networks with typed interfaces, fan-out, shared subcomputations, and feedback.

Let

H=Sh⁡∞(C,J) \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J)

be a cognitive ∞-topos. A typed family of memetic states is represented by an object over a type object X→IX\to I. A polynomial

I←sE→pB→tI I\xleftarrow{s}E\xrightarrow{p}B\xrightarrow{t}I

determines an endofunctor

P=ΣtΠpΔs:H/I⟶H/I, P=\Sigma_t\Pi_p\Delta_s:\mathcal{H}_{/I}\longrightarrow\mathcal{H}_{/I},

whose points encode typed operations with input slots and outputs. Polynomial monads generalize operads and provide the syntax of finite typed recombination.

Iteration of polynomial operations still produces trees. To represent sharing of subcomputations one must additionally allow copying and discarding, equivalently a suitable commutative comonoid or cartesian structure. To represent cyclic dependence one adds a trace operator

Tr⁡X,YU:Map⁡(X⊗U, Y⊗U)⟶Map⁡(X,Y). \operatorname{Tr}^U_{X,Y}: \operatorname{Map}(X\otimes U,\,Y\otimes U) \longrightarrow \operatorname{Map}(X,Y).

Finally, organizing networks, transformations between networks, and higher equivalences naturally leads to a symmetric monoidal (∞,2)-category.

The resulting theory separates four layers:

Polynomial syntax, sharing, feedback, and higher semantics.

This allows memetic replication to be modeled as preservation of semantic invariants by arbitrary resource-sensitive network computations, rather than merely by unary copying or tree-shaped recombination. The unary theory is Note I; the operadic theory is Note II.

Notation

The symbols of this note, in the order they become active.

1 Ambient semantic setting

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site and let

H=Sh⁡∞(C,J) \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J)

be its ∞-topos. Let I∈HI\in\mathcal{H} be an object of semantic types. A typed memetic state is an object

q:X→I. q:X\to I.

The fiber XiX_i over i∈Ii\in I is the space of memetic states of semantic type ii. The natural ambient category is therefore the slice H/I\mathcal{H}_{/I}.

2 Internal polynomial functors

Definition 2.1 Polynomial

A polynomial over II is a diagram

I←sE→pB→tI I\xleftarrow{s}E\xrightarrow{p}B\xrightarrow{t}I

in H\mathcal{H}. Its parts are interpreted as follows.

Definition 2.2 Polynomial endofunctor

The polynomial determines

P:H/I→H/I P:\mathcal{H}_{/I}\to\mathcal{H}_{/I}

by

P=ΣtΠpΔs. P=\Sigma_t\Pi_p\Delta_s.

Here Δs\Delta_s is pullback, Πp\Pi_p is dependent product, and Σt\Sigma_t is dependent sum. In internal type-theoretic notation,

P(X)i≃∑b : t(b)=i∏e∈EbXs(e). P(X)_i \simeq \sum_{b\,:\,t(b)=i} \prod_{e\in E_b} X_{s(e)}.

A point of P(X)iP(X)_i consists of an operation bb whose output has type ii, together with one input state of type s(e)s(e) for every input slot e∈Ebe\in E_b.

3 Polynomial memetic dynamics

Definition 3.1 Polynomial memetic algebra

A polynomial memetic algebra is an object X→IX\to I together with a morphism

α:P(X)→X \alpha:P(X)\to X

in H/I\mathcal{H}_{/I}. The map α\alpha evaluates one layer of typed memetic recombination. Internally,

αi:∑b : t(b)=i∏e∈EbXs(e)⟶Xi. \alpha_i: \sum_{b\,:\,t(b)=i} \prod_{e\in E_b} X_{s(e)} \longrightarrow X_i.

4 Operads as a special case

An operad records operations together with substitution. A polynomial functor records only the one-step shape of operations. To obtain coherent substitution we impose monadic structure.

Definition 4.1 Polynomial monad

A polynomial monad (P,η,μ)(P,\eta,\mu) is a monad on H/I\mathcal{H}_{/I} whose underlying endofunctor PP is polynomial and whose unit and multiplication are represented by compatible polynomial morphisms. The monad multiplication

μ:P∘P→P \mu:P\circ P\to P

encodes substitution of operations into input slots.

Proposition 4.2 Tree-shaped composition

Every algebra for a polynomial monad possesses coherent finite tree-shaped composition of its primitive operations.

Proof.

An algebra is a map α:P(X)→X\alpha:P(X)\to X satisfying α∘ηX≃id⁡X\alpha\circ\eta_X\simeq\operatorname{id}_X and α∘P(α)≃α∘μX\alpha\circ P(\alpha)\simeq\alpha\circ\mu_X. The second equation states that evaluating an outer operation after evaluating its immediate suboperations agrees with first substituting all operations into one composite polynomial operation and then evaluating. Induction over finite rooted trees therefore gives coherent evaluation of every polynomial tree.

5 Why polynomial syntax alone still yields trees

The free algebra of a polynomial monad is generated by finite substitution. Consequently, its formal expressions have a rooted-tree shape. If a value is used twice, the tree contains two occurrences of that value. This distinguishes duplication of syntax from sharing of one computed value. These are not the same operational structure.

Example 5.1 Recomputation versus fan-out

Consider

f(g(x),g(x)). f(g(x),g(x)).

As an ordinary term tree, the subterm g(x)g(x) appears twice. A shared computational graph instead has one node computing y=g(x)y=g(x), followed by a fan-out y↦(y,y)y\mapsto(y,y) feeding both inputs of ff. Hence term trees cannot by themselves distinguish recomputation from sharing.

6 Sharing via comonoid structure

Let (V,⊗,1)(\mathcal{V},\otimes,\mathbb{1}) be a symmetric monoidal ∞-category of semantic processes.

Definition 6.1 Copying structure

A copying operation on X∈VX\in\mathcal{V} is a morphism

ΔX:X→X⊗X. \Delta_X:X\to X\otimes X.

A discarding operation is

εX:X→1. \varepsilon_X:X\to\mathbb{1}.

If these form a coherently commutative comonoid structure, then outputs may be copied and discarded. If V\mathcal{V} is cartesian monoidal, so that ⊗=×\otimes=\times, then every object possesses canonical maps

ΔX:X→X×X,!X:X→1. \Delta_X:X\to X\times X,\qquad !_X:X\to 1.

Thus cartesian structure provides canonical fan-out and deletion.

7 Shared memetic computations

Suppose g:X→Yg:X\to Y and f:Y⊗Y→Zf:Y\otimes Y\to Z. The shared network

X→gY→ΔYY⊗Y→fZ X\xrightarrow{g}Y\xrightarrow{\Delta_Y}Y\otimes Y\xrightarrow{f}Z

has composite

f∘ΔY∘g. f\circ\Delta_Y\circ g.

This represents one execution of gg followed by reuse of its result. By contrast, the tree-like map is

X⊗X→g⊗gY⊗Y→fZ, X\otimes X\xrightarrow{g\otimes g}Y\otimes Y\xrightarrow{f}Z,

possibly preceded by ΔX:X→X⊗X\Delta_X:X\to X\otimes X. The distinction is operational even when the resulting semantic maps happen to agree.

8 Term graphs

Definition 8.1 Memetic term graph

A memetic term graph is a directed acyclic network whose vertices are labeled by primitive polynomial operations and whose edges carry typed memetic states, with fan-out permitted. Unlike an operadic tree, one output edge may feed several later operations. A term graph therefore represents a computation with shared intermediate results.

There is an unfolding map

U:Graph⁡P⟶Tree⁡P U:\operatorname{Graph}_P\longrightarrow\operatorname{Tree}_P

obtained by duplicating every shared subgraph once for each use.

Proposition 8.2 Semantic invariance under unfolding

Assume the semantic category is cartesian monoidal and all graph nodes are interpreted functorially. Then a finite acyclic term graph and its complete tree unfolding have equivalent semantic evaluations.

Proof.

Every shared node yy with kk outgoing uses is interpreted using the kk-fold diagonal ΔY(k):Y→Yk\Delta_Y^{(k)}:Y\to Y^k. Naturality and the universal property of finite products imply that composing once with g:X→Yg:X\to Y and then diagonally copying its output is equivalent to the map

x↦(g(x),…,g(x)). x\mapsto\bigl(g(x),\ldots,g(x)\bigr).

The unfolded tree computes exactly these kk components separately. Induction on the finite acyclic graph therefore yields equality of the resulting morphisms.

Remark 8.3 Cost is not preserved

Operational cost need not be preserved. The unfolded tree may recompute a subexpression many times, while the graph computes it once. Thus semantic equivalence does not imply resource equivalence.

9 Resource-sensitive semantics

To distinguish such implementations, the semantic category should not automatically identify copying as free. Let (V,⊗,1)(\mathcal{V},\otimes,\mathbb{1}) be symmetric monoidal but not necessarily cartesian. Then a state xx cannot in general be duplicated unless a specific map ΔX:X→X⊗X\Delta_X:X\to X\otimes X is supplied. This permits explicit accounting of resources.

Definition 9.1 Resource-sensitive memetic process

A resource-sensitive memetic process is a morphism

f:X1⊗⋯⊗Xn⟶Y1⊗⋯⊗Ym f: X_1\otimes\cdots\otimes X_n \longrightarrow Y_1\otimes\cdots\otimes Y_m

in a symmetric monoidal semantic category. Inputs are consumed and outputs produced according to the monoidal structure.

10 Resource observables

Let RR be a commutative ordered monoid, for example (R≥0,+,0,≤)(\mathbb{R}_{\geq 0},+,0,\leq).

Definition 10.1 Resource valuation

A resource valuation is a function

ρ:Mor⁡(V)→R \rho:\operatorname{Mor}(\mathcal{V})\to R

satisfying, at minimum,

ρ(g∘f)≤ρ(f)+ρ(g) \rho(g\circ f)\leq\rho(f)+\rho(g)

and

ρ(f⊗g)≤ρ(f)+ρ(g). \rho(f\otimes g)\leq\rho(f)+\rho(g).

Examples include energy, latency, memory, bandwidth, and the number of primitive operations. Two networks may therefore satisfy

⟦G⟧≃⟦H⟧ \llbracket G\rrbracket\simeq\llbracket H\rrbracket

while ρ(G)≠ρ(H)\rho(G)\neq\rho(H). This is precisely the distinction between semantic and operational equivalence.

11 Feedback

Trees and directed acyclic graphs remain acyclic. Recursive self-reference and recurrent processing require cycles. The appropriate categorical structure is a trace.

Definition 11.1 Traced symmetric monoidal structure

A traced symmetric monoidal ∞-category is a symmetric monoidal ∞-category together with operations

Tr⁡X,YU:Map⁡(X⊗U, Y⊗U)⟶Map⁡(X,Y) \operatorname{Tr}^{U}_{X,Y}: \operatorname{Map}(X\otimes U,\,Y\otimes U) \longrightarrow \operatorname{Map}(X,Y)

satisfying homotopy-coherent versions of naturality, dinaturality, vanishing, superposing, and yanking.

A morphism f:X⊗U→Y⊗Uf:X\otimes U\to Y\otimes U can be pictured as a process from XX to YY with an auxiliary wire of type UU.

The trace closes that wire:

Tr⁡U(f):X→Y. \operatorname{Tr}^{U}(f):X\to Y.

12 Feedback memetic process

Suppose F:X⊗S→Y⊗SF:X\otimes S\to Y\otimes S, where SS is an internal state. Then

Tr⁡S(F):X→Y \operatorname{Tr}^{S}(F):X\to Y

represents a process in which the state output is fed back as the next state input. This models recurrence, memory, self-conditioning, iterative refinement, and feedback control.

13 Feedback replication

Let m∈Mm\in M be a memetic signal and suppose a recurrent transformation has semantic map

R=Tr⁡S(F):M→M. R=\operatorname{Tr}^{S}(F):M\to M.

Replication is again the identity type

Id⁡M(R(m), m). \operatorname{Id}_{M}\bigl(R(m),\,m\bigr).

Thus feedback changes the causal architecture but not the fundamental semantic identity criterion.

14 Fixed points and traces

A feedback loop should not automatically be identified with an ordinary fixed-point equation. A trace provides a way to close a wire:

F:X⊗U→Y⊗U F:X\otimes U\to Y\otimes U

becomes

Tr⁡U(F):X→Y. \operatorname{Tr}^{U}(F):X\to Y.

A fixed point instead concerns a point xx satisfying f(x)≃xf(x)\simeq x. The two notions coincide only under additional hypotheses, such as an appropriate Conway or iteration structure. This distinction is important for recursive systems.

15 Memetic networks

Definition 15.1 Memetic network

A memetic network is a finite typed string diagram generated by:

Its semantic interpretation is a morphism

⟦N⟧:X→Y \llbracket N\rrbracket:X\to Y

in the chosen semantic category.

16 Network equivalence

Definition 16.1 Operational equivalence

Two networks are operationally equivalent if they are equivalent as network presentations under the chosen graphical calculus. Write N≃opN′N\simeq_{\mathrm{op}}N'.

Definition 16.2 Semantic equivalence

They are semantically equivalent if

⟦N⟧≃⟦N′⟧. \llbracket N\rrbracket\simeq\llbracket N'\rrbracket.

Write N≃semN′N\simeq_{\mathrm{sem}}N'. In general

N≃opN′  ⟹  N≃semN′, N\simeq_{\mathrm{op}}N'\implies N\simeq_{\mathrm{sem}}N',

but not conversely. Thus the semantic quotient is typically many-to-one.

17 Network-to-semantic-class map

Let Net(X,Y)\mathsf{Net}(X,Y) denote the space of formal networks from XX to YY. Evaluation gives

ev⁡X,Y:Net(X,Y)⟶Map⁡V(X,Y). \operatorname{ev}_{X,Y}: \mathsf{Net}(X,Y) \longrightarrow \operatorname{Map}_{\mathcal{V}}(X,Y).

This is the network analogue of the earlier trace evaluation map FO(UM)→MF_{\mathcal{O}}(UM)\to M from Note II. The homotopy fiber

hofib⁡f(ev⁡X,Y) \operatorname{hofib}_{f}(\operatorname{ev}_{X,Y})

is the space of operational realizations of a semantic map f:X→Yf:X\to Y.

18 Nonuniqueness theorem

Theorem 18.1 Semantic uniqueness does not imply implementation uniqueness

Suppose hofib⁡f(ev⁡X,Y)\operatorname{hofib}_{f}(\operatorname{ev}_{X,Y}) has more than one connected component. Then there exist operationally inequivalent networks N̸≃opN′N\not\simeq_{\mathrm{op}}N' such that

⟦N⟧≃f≃⟦N′⟧. \llbracket N\rrbracket\simeq f\simeq\llbracket N'\rrbracket.

Proof.

Choose N,N′N,N' from different connected components of the same homotopy fiber. By membership in that fiber, ⟦N⟧≃f\llbracket N\rrbracket\simeq f and ⟦N′⟧≃f\llbracket N'\rrbracket\simeq f. Hence ⟦N⟧≃⟦N′⟧\llbracket N\rrbracket\simeq\llbracket N'\rrbracket. But the two points are not connected in the implementation space. Therefore the networks are operationally inequivalent.

19 Resource nonuniqueness

Corollary 19.1 Cost is not a semantic invariant

Semantically equivalent replication networks may have different resource costs. That is,

⟦N⟧≃⟦N′⟧ \llbracket N\rrbracket\simeq\llbracket N'\rrbracket

does not imply ρ(N)=ρ(N′)\rho(N)=\rho(N'). Hence semantic identity does not determine energy, latency, memory, or physical implementation.

20 From an (∞,1)-category to an (∞,2)-category

So far networks and their semantic maps live in mapping spaces. To explicitly retain transformations between processes, introduce an (∞,2)-category C\mathfrak{C}. Its structure may be interpreted as semantic interfaces, memetic networks, and transformations between networks, with higher invertible coherences above dimension 2.

21 Memetic (∞,2)-category

Definition 21.1 Memetic process (∞,2)-category

A memetic process (∞,2)-category M\mathfrak{M} is a symmetric monoidal (∞,2)-category in which objects are typed semantic interfaces, 1-morphisms are computational or communicative processes, and 2-morphisms are process transformations.

For f,g:X→Yf,g:X\to Y, a 2-morphism α:f⇒g\alpha:f\Rightarrow g may represent:

22 Semantic invariants of process transformations

Let Q:M→SQ:\mathfrak{M}\to\mathfrak{S} be a semantic functor into another (∞,2)-category.

Definition 22.1 Semantics-preserving transformation

A 2-morphism α:f⇒g\alpha:f\Rightarrow g is QQ-semantics preserving if Q(α)Q(\alpha) is an equivalence between Q(f)Q(f) and Q(g)Q(g). Thus an implementation can change while its chosen semantics remains invariant.

23 Replication as a 2-categorical invariant

Suppose f:X→Xf:X\to X is a replication process. Let Q(f)Q(f) be its observable semantic action. A transformed implementation g:X→Xg:X\to X is the same replicator relative to QQ whenever there exists α:f⇒g\alpha:f\Rightarrow g such that Q(α)Q(\alpha) is invertible.

Hence the replicator is more naturally an equivalence class of 1-morphisms under semantics-preserving 2-morphisms.

24 Compilation as a 2-morphism

Suppose ff is a high-level network and gg a lower-level implementation. A compiler can be represented abstractly by α:f⇒g\alpha:f\Rightarrow g. Correctness means preservation of semantics:

⟦f⟧≃⟦g⟧. \llbracket f\rrbracket\simeq\llbracket g\rrbracket.

Resource optimization can simultaneously require ρ(g)<ρ(f)\rho(g)<\rho(f). Thus optimization is naturally a transformation inside a semantic equivalence class.

25 Recursive improvement

A recursive improvement step is then not necessarily an endomorphism of a state object. It may instead be a 2-morphism

αn:Fn⇒Fn+1 \alpha_n:F_n\Rightarrow F_{n+1}

between successive system implementations. Semantic preservation requires

Q(Fn)≃Q(Fn+1), Q(F_n)\simeq Q(F_{n+1}),

while improvement may be expressed by a monotone resource functional

ρ(Fn+1)<ρ(Fn) \rho(F_{n+1})<\rho(F_n)

or by a performance functional

P(Fn+1)>P(Fn). P(F_{n+1})>P(F_n).

Thus recursive improvement takes the form

F0⇒F1⇒F2⇒⋯ F_0\Rightarrow F_1\Rightarrow F_2\Rightarrow\cdots

inside a 2-category of implementations.

26 Resource-relative improvement

Definition 26.1 Improvement preorder

Let ρ:Mor⁡1(M)→R\rho:\operatorname{Mor}_1(\mathfrak{M})\to R be a resource cost. For semantically equivalent processes f,gf,g, define

g⪯ρf g\preceq_{\rho}f

when ρ(g)≤ρ(f)\rho(g)\leq\rho(f). If ρ(g)<ρ(f)\rho(g)<\rho(f), write g≺ρfg\prec_{\rho}f.

Definition 26.2 Strict semantic improvement

A transformation α:f⇒g\alpha:f\Rightarrow g is a strict (Q,ρ)(Q,\rho)-improvement if Q(f)≃Q(g)Q(f)\simeq Q(g) and ρ(g)<ρ(f)\rho(g)<\rho(f).

27 Replication under optimization

Theorem 27.1 Optimization preserves replication

Let f,g:X→Xf,g:X\to X be processes and suppose f⇒gf\Rightarrow g is QQ-semantics preserving. If ff replicates a semantic invariant qq, then gg also replicates qq.

Proof.

Suppose replication by ff means Q(f)(q)≃qQ(f)(q)\simeq q. Since Q(f)≃Q(g)Q(f)\simeq Q(g), we obtain

Q(g)(q)≃Q(f)(q)≃q. Q(g)(q)\simeq Q(f)(q)\simeq q.

Therefore gg preserves the same semantic invariant.

This formalizes the idea that a replicating operation may change implementation without ceasing to be the same semantic replicator.

28 Observer-relative network semantics

Return now to the cognitive site (C,J)(\mathcal{C},J). Suppose every architecture cc possesses a semantic process category Mc\mathfrak{M}_c. An architecture morphism u:c→du:c\to d induces reindexing

u∗:Md→Mc. u^*:\mathfrak{M}_d\to\mathfrak{M}_c.

Then a network NN at dd has observer-relative image u∗Nu^*N. Two networks may satisfy N̸≃dN′N\not\simeq_d N' but u∗N≃cu∗N′u^*N\simeq_c u^*N'. Thus semantic collapse extends from states to entire causal networks.

29 Network descent

Assume the assignment c↦Mcc\mapsto\mathfrak{M}_c forms a stack of symmetric monoidal (∞,2)-categories. Then for a cover U∙→cU_\bullet\to c one obtains

Mc≃lim⁡[n]∈ΔMUn. \mathfrak{M}_c \simeq \operatorname*{lim}_{[n]\in\Delta} \mathfrak{M}_{U_n}.

Hence networks themselves can be assembled by descent from local process descriptions.

30 Distributed causal structure

This means that a globally defined memetic process can be represented by networks

Ni N_i

on each subsystem, together with equivalences

Ni∣ij≃Nj∣ij, N_i\big|_{ij}\simeq N_j\big|_{ij},

higher equivalences on triple overlaps, and all required coherence data. Thus the causal architecture of a meme can itself be distributed.

31 Replication networks as invariant-preserving morphisms

Let Q:M→SQ:M\to S be an observable semantic quotient. A network

N:M⊗n→M N:M^{\otimes n}\to M

replicates the jj-th input relative to QQ if

Q∘N≃Q∘πj. Q\circ N\simeq Q\circ\pi_j.

This generalizes every earlier definition.

32 Network replication theorem

Theorem 32.1 Semantics-preserving transformations preserve replication

Suppose N:M⊗n→MN:M^{\otimes n}\to M satisfies Q∘N≃Q∘πjQ\circ N\simeq Q\circ\pi_j. If N′N' is related to NN by a QQ-semantics-preserving 2-morphism, then N′N' also replicates the jj-th semantic input relative to QQ.

Proof.

A QQ-semantics-preserving transformation implies Q∘N′≃Q∘NQ\circ N'\simeq Q\circ N. By hypothesis Q∘N≃Q∘πjQ\circ N\simeq Q\circ\pi_j. Therefore Q∘N′≃Q∘πjQ\circ N'\simeq Q\circ\pi_j.

33 Composition theorem

Suppose N:M⊗n→MN:M^{\otimes n}\to M replicates input jj relative to QQ, and K:M⊗m→MK:M^{\otimes m}\to M replicates one input carrying the output of NN. Then the composite network preserves the original QQ-class.

Theorem 33.1 Network heredity

If every node along a causal path preserves a semantic invariant QQ, then the entire composite network preserves that invariant along that path.

Proof.

Each local preservation gives an equivalence Q(xk+1)≃Q(xk)Q(x_{k+1})\simeq Q(x_k). Composition of these equivalences yields Q(xout)≃Q(xin)Q(x_{\mathrm{out}})\simeq Q(x_{\mathrm{in}}). The conclusion is independent of whether the intervening network contains branching, sharing, or feedback, provided the global composite is defined and each relevant preservation equation holds.

34 Polynomial syntax and traced execution

The complete picture is now five layers.

polynomial monad \text{polynomial monad}

specifies the primitive typed operations and their substitution.

symmetric monoidal structure \text{symmetric monoidal structure}

specifies parallel composition and resource combination.

comonoid or cartesian structure \text{comonoid or cartesian structure}

specifies sharing and deletion.

trace \text{trace}

specifies feedback.

(∞,2)-structure (\infty,2)\text{-structure}

specifies transformations between networks.

35 Formal total specification

A sufficiently rich memetic network theory is a tuple

(C, J, H, I, P, V, Δ, Tr⁡, M, Q, ρ). (\mathcal{C},\,J,\,\mathcal{H},\,I,\,P,\,\mathcal{V},\,\Delta,\,\operatorname{Tr},\,\mathfrak{M},\,Q,\,\rho).

36 Fundamental equations

Primitive recombination:

P(X)i≃∑b : t(b)=i∏e∈EbXs(e). P(X)_i \simeq \sum_{b\,:\,t(b)=i} \prod_{e\in E_b} X_{s(e)}.

Evaluation:

α:P(X)→X. \alpha:P(X)\to X.

Sharing:

ΔX:X→X⊗X. \Delta_X:X\to X\otimes X.

Feedback:

Tr⁡X,YU:Map⁡(X⊗U, Y⊗U)→Map⁡(X,Y). \operatorname{Tr}^{U}_{X,Y}: \operatorname{Map}(X\otimes U,\,Y\otimes U) \to \operatorname{Map}(X,Y).

Semantic network evaluation:

ev⁡X,Y:Net(X,Y)→Map⁡(X,Y). \operatorname{ev}_{X,Y}: \mathsf{Net}(X,Y) \to \operatorname{Map}(X,Y).

Replication relative to QQ:

Q∘N≃Q∘πj. Q\circ N\simeq Q\circ\pi_j.

Semantics-preserving optimization:

Q(N)≃Q(N′),ρ(N′)<ρ(N). Q(N)\simeq Q(N'),\qquad\rho(N')<\rho(N).

37 Central structural theorem

Theorem 37.1 General network memetic replication theorem

Let R\mathfrak{R} be a system as above. Then:

38 Relation among the successive theories

The entire development can now be organized as a chain of strict generalizations:

Unary dynamics, then operadic trees, then polynomial typed trees, then shared DAGs, then feedback networks.

At the semantic level:

Sets, then spaces, then ∞-topoi, then indexed ∞-topoi.

At the process level:

Functions, then monoidal categories, then traced monoidal categories, then (∞,2)-categories.

These are independent axes of generalization.

39 Consequence for the concept of a meme

A meme should no longer be identified with a string, a token, or even merely a point m∈Mm\in M. The full theory distinguishes six levels, and these levels need not determine one another uniquely.

40 Final principle

The strongest formulation is therefore:

Memetic replication is preservation of a semantic invariant under a typed network transformation.

The network may contain composition, parallelism, sharing, recombination, feedback, and optimization. What makes it replication is not its graphical form. It is the existence of an invariant QQ such that

Q∘N≃Q∘πj. Q\circ N\simeq Q\circ\pi_j.

Different implementations may realize the same invariant:

N̸≃opN′whileQ(N)≃Q(N′). N\not\simeq_{\mathrm{op}}N'\qquad\text{while}\qquad Q(N)\simeq Q(N').

They may also differ radically in resource usage:

ρ(N)≠ρ(N′). \rho(N)\neq\rho(N').

Hence the semantic object, operational trace, causal network, and physical realization must be regarded as distinct levels. The complete conceptual hierarchy is

Physical realization, then causal network, then semantic process, then observer-relative invariant.

Memetic identity lives at the final level selected by the observer’s semantic modality, whereas replication is the persistence of that invariant through transformations at all preceding levels.

Part 4: Stochastic Memetic Replication

Abstract

We extend the ∞-topos and network-theoretic formulation of memetic replication from deterministic transformations to stochastic population dynamics.

Let

H=Sh⁡∞(C,J) \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J)

be a cognitive semantic ∞-topos, and let S∈HS\in\mathcal{H} be an object of semantic memetic classes. Since probability is not canonically determined by an arbitrary ∞-topos, we assume explicitly that H\mathcal{H} is equipped with a commutative probability monad

P:H→H \mathbb{P}:\mathcal{H}\to\mathcal{H}

or, equivalently for the present purposes, with a compatible Markov-category structure.

A population is a probability state

μ:1→P(S). \mu:1\to\mathbb{P}(S).

Selection is specified by a positive fitness observable

w:S→R>0, w:S\to\mathbb{R}_{>0},

while mutation and imperfect transmission are represented by a Markov kernel

K:S→P(S). K:S\to\mathbb{P}(S).

The resulting discrete-time evolutionary operator is

Rw,K(μ)=K∗ ⁣(wμ∫Sw dμ). \mathcal{R}_{w,K}(\mu) = K_*\!\left(\frac{w\mu}{\displaystyle\int_S w\,d\mu}\right).

Equivalently, introducing the positive transfer operator Lw,Kμ=K∗(wμ)\mathcal{L}_{w,K}\mu=K_*(w\mu), one has

Rw,K(μ)=Lw,KμLw,Kμ(S). \mathcal{R}_{w,K}(\mu) = \frac{\mathcal{L}_{w,K}\mu}{\mathcal{L}_{w,K}\mu(S)}.

For a finite semantic state space this becomes the classical replicator–mutator equation

xj′=∑ixiwiQij∑ixiwi. x'_j = \frac{\displaystyle\sum_i x_i w_i Q_{ij}}{\displaystyle\sum_i x_i w_i}.

We prove normalization, characterize stationary populations as positive eigenmeasures of the transfer operator, derive both continuous-time replicator and mutation–selection equations, establish a coarse-graining theorem showing precisely when observer-relative semantic quotients inherit closed Markovian dynamics, and derive the Perron–Frobenius asymptotics of finite primitive systems.

The resulting framework identifies stochastic memetic evolution with normalized positive dynamics on capability-relative semantic classes. The deterministic theories are Note I, Note II, and Note III.

Notation

The symbols of this note, in the order they become active.

1 Semantic state space

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site and let

H=Sh⁡∞(C,J). \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J).

Let M∈HM\in\mathcal{H} be an underlying object of memetic realizations, and let Q:M→SQ:M\to S be a semantic observable, localization, or quotient. The object S∈HS\in\mathcal{H} is the semantic state object.

Two realizations m,m′∈Mm,m'\in M are equivalent relative to QQ when Q(m)≃Q(m′)Q(m)\simeq Q(m'). Thus SS represents the memetic distinctions retained by the observer or cognitive architecture under consideration.

2 Probability structure

Probability is additional structure. An arbitrary ∞-topos does not by itself determine a canonical notion of probability measure. We therefore assume the following.

Remark 2.1 Probability monad

The ∞-topos H\mathcal{H} is equipped with a commutative probability monad

P:H→H \mathbb{P}:\mathcal{H}\to\mathcal{H}

with unit

ηX:X→P(X) \eta_X:X\to\mathbb{P}(X)

and multiplication

mX:P(P(X))→P(X). m_X:\mathbb{P}(\mathbb{P}(X))\to\mathbb{P}(X).

We assume sufficient integration structure to make expressions of the form

∫Xf dμ \int_X f\,d\mu

well defined for suitable internal nonnegative real-valued functions f:X→R≥0f:X\to\mathbb{R}_{\geq 0}. In ordinary measurable spaces one may take the Giry probability monad. For finite objects, P(X)\mathbb{P}(X) is the probability simplex on XX.

3 Markov kernels

Definition 3.1 Stochastic memetic transformation

A stochastic transformation from XX to YY is a Kleisli morphism

K:X⟶P(Y). K:X\longrightarrow\mathbb{P}(Y).

We call such a morphism a Markov kernel. For x∈Xx\in X, the probability state K(x)∈P(Y)K(x)\in\mathbb{P}(Y) describes the distribution of possible outputs conditional on input xx.

Definition 3.2 Composition

If K:X→P(Y)K:X\to\mathbb{P}(Y) and L:Y→P(Z)L:Y\to\mathbb{P}(Z), their Kleisli composite

L⊙K:X→P(Z) L\odot K:X\to\mathbb{P}(Z)

is defined by

mZ∘P(L)∘K. m_Z\circ\mathbb{P}(L)\circ K.

In ordinary integral notation,

(L⊙K)(x)=∫YL(y) K(x,dy). (L\odot K)(x)=\int_Y L(y)\,K(x,dy).

Thus stochastic transmission is closed under composition.

4 Population states

Definition 4.1 Population distribution

A memetic population state on SS is a probability state

μ:1→P(S). \mu:1\to\mathbb{P}(S).

The space of populations is therefore P(S)\mathbb{P}(S). For finite S={1,…,n}S=\{1,\ldots,n\}, we identify μ\mu with a vector x=(x1,…,xn)x=(x_1,\ldots,x_n) satisfying

xi≥0,∑ixi=1. x_i\geq 0,\qquad\sum_i x_i=1.

5 Fitness

Definition 5.1 Fitness observable

A fitness function is a positive semantic observable

w:S→R>0. w:S\to\mathbb{R}_{>0}.

For a state ss, the value w(s)w(s) is its expected reproductive weight. The population mean fitness is

wˉμ=∫Sw(s) dμ(s). \bar w_\mu=\int_S w(s)\,d\mu(s).

We assume 0<wˉμ<∞0<\bar w_\mu<\infty for every population under consideration.

6 Selection

Definition 6.1 Selection operator

The fitness-weighted population Sel⁡w(μ)\operatorname{Sel}_w(\mu) is the probability distribution defined by

dSel⁡w(μ)(s)=w(s)wˉμ dμ(s). d\operatorname{Sel}_w(\mu)(s)=\frac{w(s)}{\bar w_\mu}\,d\mu(s).

Equivalently, for an event A⊆SA\subseteq S,

Sel⁡w(μ)(A)=∫Aw dμ∫Sw dμ. \operatorname{Sel}_w(\mu)(A) = \frac{\displaystyle\int_A w\,d\mu}{\displaystyle\int_S w\,d\mu}.

Proposition 6.2 Selection preserves normalization

Sel⁡w(μ)(S)=1. \operatorname{Sel}_w(\mu)(S)=1.

Proof.

By definition the numerator and the denominator are the same integral:

Sel⁡w(μ)(S)=∫Sw dμ∫Sw dμ=1. \operatorname{Sel}_w(\mu)(S) = \frac{\displaystyle\int_S w\,d\mu}{\displaystyle\int_S w\,d\mu} =1.

7 Mutation and imperfect transmission

Definition 7.1 Mutation kernel

A mutation kernel is a Markov kernel

K:S→P(S). K:S\to\mathbb{P}(S).

For s∈Ss\in S, the distribution K(s)K(s) describes the possible semantic type of an offspring or transmitted realization originating from ss. Mutation here includes any stochastic failure of exact semantic inheritance:

or any other probabilistic change of semantic class.

8 Pushforward through a Markov kernel

A kernel induces a map on populations

K∗:P(S)→P(S), K_*:\mathbb{P}(S)\to\mathbb{P}(S),

given by

(K∗μ)(A)=∫SK(s,A) dμ(s). (K_*\mu)(A)=\int_S K(s,A)\,d\mu(s).

Proposition 8.1 Mutation preserves probability mass

If μ\mu is normalized, then K∗μK_*\mu is normalized.

Proof.

Since each K(s)K(s) is a probability measure, K(s,S)=1K(s,S)=1. Hence

(K∗μ)(S)=∫S1 dμ(s)=1. (K_*\mu)(S)=\int_S 1\,d\mu(s)=1.

9 The categorical replicator–mutator operator

We now compose selection and mutation.

Definition 9.1 Replicator–mutator operator

Define

Rw,K=K∗∘Sel⁡w. \mathcal{R}_{w,K}=K_*\circ\operatorname{Sel}_w.

Thus

Rw,K(μ)=K∗ ⁣(wμ∫Sw dμ). \mathcal{R}_{w,K}(\mu) = K_*\!\left(\frac{w\mu}{\displaystyle\int_S w\,d\mu}\right).

Explicitly, for an event AA,

Rw,K(μ)(A)=∫Sw(s) K(s,A) dμ(s)∫Sw(s) dμ(s). \mathcal{R}_{w,K}(\mu)(A) = \frac{\displaystyle\int_S w(s)\,K(s,A)\,d\mu(s)}{\displaystyle\int_S w(s)\,d\mu(s)}.

10 Normalization theorem

Theorem 10.1 The operator preserves probability

For every probability distribution μ\mu, Rw,K(μ)\mathcal{R}_{w,K}(\mu) is again a probability distribution.

Proof.

Selection produces a probability state by Proposition 6.2. The Markov pushforward K∗K_* preserves probability mass by Proposition 8.1. Therefore their composite does also.

Thus

Rw,K:P(S)→P(S) \mathcal{R}_{w,K}:\mathbb{P}(S)\to\mathbb{P}(S)

defines a dynamical system on the population simplex.

11 The positive transfer operator

Normalization hides a useful linear structure. Let M+(S)\mathbb{M}_+(S) denote the cone of finite nonnegative measures on SS.

Definition 11.1 Mutation–selection transfer operator

Define

Lw,K:M+(S)→M+(S) \mathcal{L}_{w,K}:\mathbb{M}_+(S)\to\mathbb{M}_+(S)

by

(Lw,Kν)(A)=∫Sw(s) K(s,A) dν(s). (\mathcal{L}_{w,K}\nu)(A)=\int_S w(s)\,K(s,A)\,d\nu(s).

Equivalently, Lw,K=K∗∘Mw\mathcal{L}_{w,K}=K_*\circ M_w, where Mw(ν)=wνM_w(\nu)=w\nu. The operator is positive and linear on finite measures.

Proposition 11.2 Total mass is mean fitness

For a probability measure μ\mu, Lw,Kμ(S)=wˉμ\mathcal{L}_{w,K}\mu(S)=\bar w_\mu.

Proof.

Lw,Kμ(S)=∫Sw(s) K(s,S) dμ(s)=∫Sw(s) dμ(s)=wˉμ. \begin{aligned} \mathcal{L}_{w,K}\mu(S) &= \int_S w(s)\,K(s,S)\,d\mu(s) \\ &= \int_S w(s)\,d\mu(s) \\ &= \bar w_\mu. \end{aligned}

Corollary 11.3 Projectivization

The replicator–mutator operator is the projectivization of the positive transfer operator:

Rw,K(μ)=Lw,KμLw,Kμ(S). \mathcal{R}_{w,K}(\mu) = \frac{\mathcal{L}_{w,K}\mu}{\mathcal{L}_{w,K}\mu(S)}.

This is the basic categorical structure behind the theory. Evolution takes place in the projectivization of a positive cone.

12 Finite-state form

Let S={1,…,n}S=\{1,\ldots,n\}. Write xi=μ({i})x_i=\mu(\{i\}), let wi>0w_i>0, and define Qij=K(i,{j})Q_{ij}=K(i,\{j\}). The matrix QQ is row-stochastic:

Qij≥0,∑jQij=1. Q_{ij}\geq 0,\qquad\sum_j Q_{ij}=1.

Theorem 12.1 Finite replicator–mutator equation

The categorical operator reduces to

xj′=∑ixiwiQij∑ixiwi. x'_j = \frac{\displaystyle\sum_i x_i w_i Q_{ij}}{\displaystyle\sum_i x_i w_i}.

Proof.

By Definition 9.1, the mass of {j}\{j\} is

Rw,K(μ)({j})=∑iwiK(i,{j}) xi∑iwixi. \mathcal{R}_{w,K}(\mu)(\{j\}) = \frac{\displaystyle\sum_i w_i K(i,\{j\})\,x_i}{\displaystyle\sum_i w_i x_i}.

Substituting K(i,{j})=QijK(i,\{j\})=Q_{ij} gives the result.

In row-vector notation,

x′=xWQxW1, x'=\frac{xWQ}{xW\mathbf{1}},

where W=diag⁡(w1,…,wn)W=\operatorname{diag}(w_1,\ldots,w_n). Since Q1=1Q\mathbf{1}=\mathbf{1}, this becomes

x′=xAxA1,A=WQ. x'=\frac{xA}{xA\mathbf{1}},\qquad A=WQ.

13 Pure replication

Set K=id⁡K=\operatorname{id}. Then Q=IQ=I, and the update becomes

xi′=xiwiwˉ, x'_i=\frac{x_i w_i}{\bar w},

where wˉ=∑ixiwi\bar w=\sum_i x_i w_i. This is the discrete replicator equation.

14 Pure mutation

If fitness is constant, w(s)=c>0w(s)=c>0, then selection is trivial: Sel⁡w(μ)=μ\operatorname{Sel}_w(\mu)=\mu. Hence

μ′=K∗μ. \mu'=K_*\mu.

For a finite state space, x′=xQx'=xQ. Ordinary Markov dynamics is the zero-selection limit of the memetic evolutionary theory.

15 Stationary populations

Definition 15.1 Evolutionary equilibrium

A population μ∗∈P(S)\mu_*\in\mathbb{P}(S) is stationary if

Rw,K(μ∗)=μ∗. \mathcal{R}_{w,K}(\mu_*)=\mu_*.

16 Eigenmeasure theorem

Theorem 16.1 Equilibria are normalized eigenmeasures

A probability distribution μ∗\mu_* is stationary if and only if

Lw,Kμ∗=λμ∗ \mathcal{L}_{w,K}\mu_*=\lambda\mu_*

for λ=∫Sw dμ∗\lambda=\int_S w\,d\mu_*.

Proof.

Suppose first that Rw,K(μ∗)=μ∗\mathcal{R}_{w,K}(\mu_*)=\mu_*. By Corollary 11.3,

μ∗=Lw,Kμ∗Lw,Kμ∗(S). \mu_* = \frac{\mathcal{L}_{w,K}\mu_*}{\mathcal{L}_{w,K}\mu_*(S)}.

Hence Lw,Kμ∗=Lw,Kμ∗(S) μ∗\mathcal{L}_{w,K}\mu_*=\mathcal{L}_{w,K}\mu_*(S)\,\mu_*. By Proposition 11.2, Lw,Kμ∗(S)=∫Sw dμ∗\mathcal{L}_{w,K}\mu_*(S)=\int_S w\,d\mu_*. Therefore Lw,Kμ∗=λμ∗\mathcal{L}_{w,K}\mu_*=\lambda\mu_*.

Conversely, suppose Lw,Kμ∗=λμ∗\mathcal{L}_{w,K}\mu_*=\lambda\mu_*. Since μ∗\mu_* has total mass 1, Lw,Kμ∗(S)=λ\mathcal{L}_{w,K}\mu_*(S)=\lambda. Therefore

Rw,K(μ∗)=λμ∗λ=μ∗. \mathcal{R}_{w,K}(\mu_*)=\frac{\lambda\mu_*}{\lambda}=\mu_*.

Thus evolutionary equilibrium is a Perron–Frobenius-type eigenproblem.

17 Iteration

Proposition 17.1 Linearization of the orbit

Let μn+1=Rw,K(μn)\mu_{n+1}=\mathcal{R}_{w,K}(\mu_n). When ww and KK are time-independent,

μn=Lw,K nμ0Lw,K nμ0(S). \mu_n = \frac{\mathcal{L}_{w,K}^{\,n}\mu_0}{\mathcal{L}_{w,K}^{\,n}\mu_0(S)}.

Proof.

Normalization factors are scalars and therefore cancel under successive projectivization. Induction gives the formula.

The nonlinear normalized dynamics is induced by an underlying positive linear dynamics.

18 Perron–Frobenius theorem in the finite case

Let A=WQA=WQ.

Theorem 18.1 Primitive finite mutation–selection dynamics

Suppose AA is primitive: there exists kk such that every entry of AkA^k is strictly positive. Then:

x0Anx0An1⟶x∗. \frac{x_0A^n}{x_0A^n\mathbf{1}}\longrightarrow x_*.

Proof.

This is the Perron–Frobenius theorem for primitive nonnegative matrices applied to AA. The fixed-point characterization follows from Theorem 16.1. The convergence statement follows from domination of all other eigenvalues in modulus by λPF\lambda_{\mathrm{PF}}.

Thus mutation plus selection can produce a unique asymptotic semantic population even though individual replication events remain stochastic.

19 Continuous-time pure selection

Let the fitness over a short interval hh be

ehfi=1+hfi+O(h2). e^{hf_i}=1+hf_i+O(h^2).

With no mutation,

xi(t+h)=xi(t) ehfi∑jxj(t) ehfj. x_i(t+h) = \frac{x_i(t)\,e^{hf_i}}{\displaystyle\sum_j x_j(t)\,e^{hf_j}}.

Expanding,

xi(t+h)=xi+h xi(fi−fˉ)+O(h2), x_i(t+h)=x_i+h\,x_i\bigl(f_i-\bar f\bigr)+O(h^2),

where fˉ=∑jxjfj\bar f=\sum_j x_j f_j. Hence

x˙i=xi(fi−fˉ). \dot x_i=x_i\bigl(f_i-\bar f\bigr).

This is the continuous replicator equation.

20 Mutation generated continuously

Let L=(Lij)L=(L_{ij}) be a Markov generator: Lij≥0L_{ij}\geq 0 for i≠ji\neq j, and ∑jLij=0\sum_j L_{ij}=0. Set

Q(h)=I+hL+O(h2),W(h)=I+hF+O(h2), Q(h)=I+hL+O(h^2),\qquad W(h)=I+hF+O(h^2),

where F=diag⁡(fi)F=\operatorname{diag}(f_i). The finite-step update is

x(t+h)=x(t) W(h) Q(h)x(t) W(h) 1. x(t+h)=\frac{x(t)\,W(h)\,Q(h)}{x(t)\,W(h)\,\mathbf{1}}.

Theorem 20.1 Continuous selection–mutation equation

The infinitesimal evolution is

x˙j=xj(fj−fˉ)+∑ixiLij. \dot x_j = x_j\bigl(f_j-\bar f\bigr) + \sum_i x_i L_{ij}.

Proof.

Expand

(I+hF)(I+hL)+O(h2)=I+h(F+L)+O(h2). (I+hF)(I+hL)+O(h^2)=I+h(F+L)+O(h^2).

The numerator is therefore

x+h(xF+xL)+O(h2). x+h(xF+xL)+O(h^2).

Since L1=0L\mathbf{1}=0, the denominator is 1+hfˉ+O(h2)1+h\bar f+O(h^2). Using (1+hfˉ)−1=1−hfˉ+O(h2)(1+h\bar f)^{-1}=1-h\bar f+O(h^2) gives

x+h(xF−fˉ x+xL)+O(h2). x+h\bigl(xF-\bar f\, x+xL\bigr)+O(h^2).

The jj-th component yields the stated equation.

21 Mutation at reproduction

There is another standard continuous-time model. Suppose type ii reproduces at rate fif_i, and each offspring has type jj with probability QijQ_{ij}. Then the production rate of type jj is ∑ixifiQij\sum_i x_i f_i Q_{ij}. Maintaining constant total population produces

x˙j=∑ixifiQij−fˉ xj, \dot x_j=\sum_i x_i f_i Q_{ij}-\bar f\, x_j,

where fˉ=∑ixifi\bar f=\sum_i x_i f_i.

Proposition 21.1 Conservation of total probability

∑jx˙j=0. \sum_j \dot x_j=0.

Proof.

∑jx˙j=∑ixifi(∑jQij)−fˉ∑jxj=∑ixifi−fˉ=0. \begin{aligned} \sum_j\dot x_j &= \sum_i x_i f_i\Bigl(\sum_j Q_{ij}\Bigr)-\bar f\sum_j x_j \\ &= \sum_i x_i f_i-\bar f \\ &= 0. \end{aligned}

22 Semantic coarse-graining

Return now to observer-relative semantics. Suppose

q:X→S q:X\to S

maps fine-grained memetic states to coarse semantic classes. Let w~:X→R>0\widetilde w:X\to\mathbb{R}_{>0} be microscopic fitness and

K~:X→P(X) \widetilde K:X\to\mathbb{P}(X)

the microscopic mutation kernel. We ask when there is an induced closed evolutionary dynamics on SS. This is a categorical lumpability problem.

23 Fitness compatibility

Definition 23.1 Fitness descends through q

Fitness is qq-observable if there exists w:S→R>0w:S\to\mathbb{R}_{>0} such that

w~=w∘q. \widetilde w=w\circ q.

Thus all fine states in one semantic equivalence class possess the same fitness.

24 Mutation compatibility

Definition 24.1 Kernel lumpability

The fine mutation kernel K~\widetilde K is qq-lumpable if there exists K:S→P(S)K:S\to\mathbb{P}(S) such that

P(q)∘K~≃K∘q. \mathbb{P}(q)\circ\widetilde K\simeq K\circ q.

Diagrammatically,

X→K~P(X)↓q↓P(q)S→KP(S). \begin{array}{ccc} X & \xrightarrow{\widetilde K} & \mathbb{P}(X) \\ \downarrow q && \downarrow\mathbb{P}(q) \\ S & \xrightarrow{K} & \mathbb{P}(S). \end{array}

The probability distribution of the coarse offspring type therefore depends only on the coarse parent type.

25 Coarse-graining theorem

Let q∗:P(X)→P(S)q_*:\mathbb{P}(X)\to\mathbb{P}(S) denote pushforward.

Theorem 25.1 Exact semantic coarse-graining commutes with evolution

Suppose

w~=w∘qandP(q)∘K~≃K∘q. \widetilde w=w\circ q\qquad\text{and}\qquad\mathbb{P}(q)\circ\widetilde K\simeq K\circ q.

Then

q∗∘Rw~,K~≃Rw,K∘q∗. q_*\circ\mathcal{R}_{\widetilde w,\widetilde K} \simeq \mathcal{R}_{w,K}\circ q_*.

Proof.

Let μ∈P(X)\mu\in\mathbb{P}(X). First consider selection. For a coarse observable f:S→Rf:S\to\mathbb{R},

∫Xf d(q∗Sel⁡w~(μ))=∫Xf(q(x)) w~(x) dμ(x)∫Xw~ dμ. \int_X f\,d\bigl(q_*\operatorname{Sel}_{\widetilde w}(\mu)\bigr) = \frac{\displaystyle\int_X f(q(x))\,\widetilde w(x)\,d\mu(x)}{\displaystyle\int_X\widetilde w\,d\mu}.

Since w~=w∘q\widetilde w=w\circ q, the right-hand side is

∫Sf(s) w(s) d(q∗μ)(s)∫Sw(s) d(q∗μ)(s). \frac{\displaystyle\int_S f(s)\,w(s)\,d(q_*\mu)(s)}{\displaystyle\int_S w(s)\,d(q_*\mu)(s)}.

Hence

q∗Sel⁡w~(μ)=Sel⁡w(q∗μ). q_*\operatorname{Sel}_{\widetilde w}(\mu)=\operatorname{Sel}_w(q_*\mu).

Lumpability gives

q∗∘K~∗=K∗∘q∗. q_*\circ\widetilde K_*=K_*\circ q_*.

Therefore

q∗Rw~,K~(μ)=q∗K~∗Sel⁡w~(μ)=K∗ q∗Sel⁡w~(μ)=K∗Sel⁡w(q∗μ)=Rw,K(q∗μ). \begin{aligned} q_*\mathcal{R}_{\widetilde w,\widetilde K}(\mu) &= q_*\widetilde K_*\operatorname{Sel}_{\widetilde w}(\mu) \\ &= K_*\,q_*\operatorname{Sel}_{\widetilde w}(\mu) \\ &= K_*\operatorname{Sel}_w(q_*\mu) \\ &= \mathcal{R}_{w,K}(q_*\mu). \end{aligned}

26 Consequence for cognitive capability

Suppose qc:M→Scq_c:M\to S_c is the semantic localization associated with cognitive architecture cc. Then the population dynamics perceived by cc is autonomous precisely when both fitness and transition probabilities depend only on qc(m)q_c(m). Otherwise the coarse semantic dynamics need not be Markovian.

Corollary 26.1 Hidden semantic structure can induce apparent memory

If mutation or fitness varies inside the fibers of q:X→Sq:X\to S, then the coarse population process on SS generally fails to close as a first-order Markov process.

Two fine populations can have the same coarse distribution q∗μ=q∗νq_*\mu=q_*\nu while having different distributions inside each fiber. If those hidden differences influence reproduction or mutation, then

q∗R(μ)≠q∗R(ν). q_*\mathcal{R}(\mu)\neq q_*\mathcal{R}(\nu).

The next coarse state therefore cannot be determined from the current coarse state alone. Thus insufficient cognitive resolution can convert microscopically Markovian dynamics into apparently non-Markovian dynamics.

27 Observer-relative fitness

Fitness itself may depend on the observer or environment. For each architecture cc, let wc:Sc→R>0w_c:S_c\to\mathbb{R}_{>0}. Then the same underlying memetic population may possess different induced evolutionary dynamics at different semantic resolutions.

For u:c→du:c\to d, compatibility requires

wc∘u∗≃wd w_c\circ u^*\simeq w_d

in the appropriate contravariant sense. Without this compatibility, there is no reason for selection to commute with change of observer.

28 Replication probability

Let [m]c[m]_c be a semantic equivalence class. A kernel KK defines a probability that state mm remains in its class after transmission.

Definition 28.1 Semantic fidelity

Define

Fc(m)=K(m,[m]c). F_c(m)=K\bigl(m,[m]_c\bigr).

Thus 0≤Fc(m)≤10\leq F_c(m)\leq 1. If cc is a coarser observer than dd, then typically [m]d⊆[m]c[m]_d\subseteq[m]_c. Consequently

Fc(m)≥Fd(m), F_c(m)\geq F_d(m),

provided both classes are measured under the same underlying transmission kernel.

Proposition 28.2 Coarser semantics weakly increase measured fidelity

If [m]d⊆[m]c[m]_d\subseteq[m]_c, then K(m,[m]d)≤K(m,[m]c)K(m,[m]_d)\leq K(m,[m]_c).

Proof.

Probability measures are monotone under inclusion of measurable events.

This is the stochastic analogue of the earlier theorem that coarse observers weakly increase apparent deterministic fidelity.

29 Effective reproductive success

For a state mm, take w(m)w(m) as exposure or reproductive intensity and Fc(m)F_c(m) as semantic fidelity. A simple capability-relative effective self-reproduction factor is then

w(m) Fc(m). w(m)\,F_c(m).

More generally, one should retain the full offspring kernel rather than compressing it to a scalar. Nevertheless, the factorization of reproductive intensity by semantic fidelity is useful conceptually.

30 Selection of semantic classes

Let A⊆SA\subseteq S be a class of memes. The expected post-reproduction mass entering AA, before normalization, is

∫Sw(s) K(s,A) dμ(s). \int_S w(s)\,K(s,A)\,d\mu(s).

This decomposes evolutionary success into current prevalence, reproductive weight, and transition probability into the target class. A class can increase either because it reproduces rapidly or because other classes mutate into it.

31 Mutation–selection balance

At equilibrium,

Lw,Kμ∗=λμ∗. \mathcal{L}_{w,K}\mu_*=\lambda\mu_*.

Hence for every measurable AA,

λμ∗(A)=∫Sw(s) K(s,A) dμ∗(s). \lambda\mu_*(A)=\int_S w(s)\,K(s,A)\,d\mu_*(s).

This is the abstract mutation–selection balance equation. The quantity λ\lambda is the asymptotic population growth factor before normalization.

32 Multi-parent stochastic recombination

The Markov-kernel formalism also extends the operadic theory of Note II. Let O(n)\mathcal{O}(n) be an object of nn-ary operations. A stochastic operadic transformation is a kernel

Kn:O(n)×Sn⟶P(S). K_n:\mathcal{O}(n)\times S^n\longrightarrow\mathbb{P}(S).

Given parents (s1,…,sn)(s_1,\ldots,s_n) and an operation oo, the output is distributed according to Kn(o;s1,…,sn)K_n(o;s_1,\ldots,s_n).

Definition 32.1 Parent-relative stochastic fidelity

Relative to parent jj,

Kn(o;s⃗, [sj]c) K_n\bigl(o;\vec s,\,[s_j]_c\bigr)

is the probability that the recombination event preserves the semantic class of the jj-th parent. Deterministic operadic replication is recovered when the kernel is a Dirac distribution.

33 Stochastic network semantics

More generally, replace deterministic network morphisms X→YX\to Y by stochastic morphisms X→P(Y)X\to\mathbb{P}(Y). Sequential network composition becomes Kleisli composition. Parallel composition is supplied by commutativity of the probability monad. Hence a probabilistic memetic network defines

N:X→P(Y). N:X\to\mathbb{P}(Y).

Its semantics is a conditional probability law rather than a deterministic function.

34 Replication relative to an observable

Let Q:Y→ZQ:Y\to Z be a semantic observable. For an input xx, the output semantic distribution is P(Q)(N(x))\mathbb{P}(Q)(N(x)). If the target semantic class associated with xx is represented by q(x)∈Zq(x)\in Z, then exact stochastic replication means

P(Q)(N(x))=δq(x). \mathbb{P}(Q)\bigl(N(x)\bigr)=\delta_{q(x)}.

Approximate replication permits a nonzero error distribution around this Dirac state.

35 Error kernel

Define the semantic error kernel by

EQ=P(Q)∘N. E_Q=\mathbb{P}(Q)\circ N.

The deterministic theory corresponds to the special case in which EQ(x)E_Q(x) is always a Dirac measure. The stochastic theory instead retains the entire distribution EQ(x)∈P(Z)E_Q(x)\in\mathbb{P}(Z). Thus mutation is not a Boolean event but a probability distribution over semantic deviations.

36 Categorical evolutionary hierarchy

The successive theories now form the hierarchy

deterministic unary replication↓operadic recombination↓polynomial and network dynamics↓stochastic kernels↓population mutation–selection dynamics. \begin{array}{c} \text{deterministic unary replication} \\ \downarrow \\ \text{operadic recombination} \\ \downarrow \\ \text{polynomial and network dynamics} \\ \downarrow \\ \text{stochastic kernels} \\ \downarrow \\ \text{population mutation--selection dynamics.} \end{array}

At each stage, the earlier theory is recovered by restriction. A deterministic morphism f:X→Yf:X\to Y embeds into stochastic dynamics through the Dirac kernel x↦δf(x)x\mapsto\delta_{f(x)}.

37 Full stochastic specification

The resulting model is the tuple

(C, J, H, P, M, Q, S, w, K). (\mathcal{C},\,J,\,\mathcal{H},\,\mathbb{P},\,M,\,Q,\,S,\,w,\,K).

The evolutionary operator is

Rw,K(μ)=K∗ ⁣(wμ∫Sw dμ). \mathcal{R}_{w,K}(\mu) = K_*\!\left(\frac{w\mu}{\displaystyle\int_S w\,d\mu}\right).

38 Main theorem

Theorem 38.1 Categorical replicator–mutator theorem

Let H\mathcal{H} be a cognitive semantic ∞-topos equipped with a compatible probability monad P\mathbb{P}. Let S∈HS\in\mathcal{H}, let w:S→R>0w:S\to\mathbb{R}_{>0}, and let K:S→P(S)K:S\to\mathbb{P}(S). Then:

xj′=∑ixiwiQij∑ixiwi. x'_j=\dfrac{\sum_i x_i w_i Q_{ij}}{\sum_i x_i w_i}.

See Theorem 12.1.

39 Conceptual consequence

The deterministic theory characterized memetic replication as preservation of a semantic invariant:

Q∘N≃Q∘πj. Q\circ N\simeq Q\circ\pi_j.

The stochastic theory replaces this by a conditional distribution over semantic outcomes. Exact inheritance is Dirac-valued. Imperfect inheritance is a Markov kernel on semantic classes.

Population evolution is then obtained by weighting those states by reproductive success and integrating over all transition possibilities. The fundamental operator is

μ⟼K∗(wμ)∫Sw dμ. \mu \longmapsto \frac{K_*(w\mu)}{\displaystyle\int_S w\,d\mu}.

40 Final synthesis

The entire theory developed so far can now be compressed into the sequence

Realizations, then semantic classes, then selection, then mutation, then normalization, then population evolution.

At the individual level,

m⇝K(Q(m)) m\rightsquigarrow K\bigl(Q(m)\bigr)

gives a distribution of possible semantic descendants. At the population level,

μ⇝Rw,K(μ). \mu\rightsquigarrow\mathcal{R}_{w,K}(\mu).

At equilibrium,

Lw,Kμ∗=λμ∗. \mathcal{L}_{w,K}\mu_*=\lambda\mu_*.

At finite dimension this is a Perron–Frobenius eigenproblem.

At different cognitive resolutions, exact coarse-graining is governed by the commuting square P(q)∘K~≃K∘q\mathbb{P}(q)\circ\widetilde K\simeq K\circ q together with w~=w∘q\widetilde w=w\circ q. Consequently, cognitive capability does not merely affect which individual transmissions count as faithful. It determines whether the population dynamics itself closes at the observer’s semantic resolution.

The strongest form of the theory is therefore

Normalized positive stochastic dynamics on observer-relative semantic classes.

Equivalently,

Projectivize⁡(K∗∘Mw). \operatorname{Projectivize}\bigl(K_*\circ M_w\bigr).

This connects the ∞-topos formulation of observer-relative identity directly to Markov kernels, mutation–selection systems, transfer operators, and the classical replicator–mutator equation.

Part 5: Endogenous Fitness and Evolutionary Games

Abstract

We extend stochastic memetic replication from exogenously specified fitness to endogenous, frequency-dependent evolutionary dynamics.

Let

H=Sh⁡∞(C,J) \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J)

be a cognitive semantic ∞-topos equipped with a commutative probability monad

P:H→H. \mathbb{P}:\mathcal{H}\to\mathcal{H}.

Let S∈HS\in\mathcal{H} denote an observer-relative semantic state object, so that a population is a probability state μ∈P(S)\mu\in\mathbb{P}(S).

Rather than assuming a fixed fitness map w:S→R>0w:S\to\mathbb{R}_{>0}, we introduce an endogenous fitness field

w:S×P(S)×E⟶R>0, w:S\times\mathbb{P}(S)\times E\longrightarrow\mathbb{R}_{>0},

where EE is an environment object. Mutation and transmission may themselves depend on the population and environment through kernels

K:S×P(S)×E⟶P(S). K:S\times\mathbb{P}(S)\times E\longrightarrow\mathbb{P}(S).

The induced nonlinear evolutionary operator is

R(μ,e)=(Kμ,e)∗(w(−,μ,e) μ∫Sw(s,μ,e) dμ(s)). \mathcal{R}(\mu,e) = (K_{\mu,e})_* \left( \frac{w(-,\mu,e)\,\mu}{\displaystyle\int_S w(s,\mu,e)\,d\mu(s)} \right).

Its equilibria satisfy a nonlinear eigenmeasure equation

Lμ∗,eμ∗=λμ∗. \mathcal{L}_{\mu_*,e}\mu_*=\lambda\mu_*.

For finite state spaces, pairwise payoff interactions recover the classical frequency-dependent replicator equation

x˙i=xi((Ax)i−x⊤Ax), \dot x_i=x_i\bigl((Ax)_i-x^\top Ax\bigr),

and mutation gives the coupled mutation–selection equation.

We derive a generalized Price identity, characterize interior equilibria, prove a Lyapunov theorem for symmetric interaction matrices, extend the construction to multiple interacting populations, and formulate open evolutionary components as stochastic lenses. Memetic evolution is then not a fixed transfer operator but a nonlinear feedback system in which the population changes the fitness landscape that subsequently changes the population.

Semantic population, interaction, fitness field, replication, and then a new semantic population.

The loop is naturally expressible using stochastic lenses and open-game semantics. The exogenous theory is Note IV.

Notation

1 Ambient semantic probability theory

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site and let

H=Sh⁡∞(C,J). \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J).

Assume H\mathcal{H} is equipped with a commutative probability monad P:H→H\mathbb{P}:\mathcal{H}\to\mathcal{H}. Let S∈HS\in\mathcal{H} be a semantic state object. A population state is μ:1→P(S)\mu:1\to\mathbb{P}(S), and E∈HE\in\mathcal{H} is an environment object. Expressions

∫Sf(s) dμ(s) \int_S f(s)\,d\mu(s)

are used under the standing assumption that the required internal integration theory exists.

2 Endogenous fitness

Definition 2.1 Endogenous fitness field

An endogenous fitness field is a morphism

w:S×P(S)×E⟶R>0. w:S\times\mathbb{P}(S)\times E\longrightarrow\mathbb{R}_{>0}.

For a fixed population μ\mu and environment ee, define wμ,e(s):=w(s,μ,e)w_{\mu,e}(s):=w(s,\mu,e). The reproductive value of a semantic state ss may depend on ss itself, on the complete current population μ\mu, and on the environment ee.

Definition 2.2 Mean endogenous fitness

Define

wˉ(μ,e)=∫Sw(s,μ,e) dμ(s). \bar w(\mu,e)=\int_S w(s,\mu,e)\,d\mu(s).

We assume 0<wˉ(μ,e)<∞0<\bar w(\mu,e)<\infty.

3 Population-dependent mutation

The transmission law itself may depend on the population state.

Definition 3.1 Endogenous mutation kernel

An endogenous mutation kernel is a morphism

K:S×P(S)×E⟶P(S). K:S\times\mathbb{P}(S)\times E\longrightarrow\mathbb{P}(S).

For fixed (μ,e)(\mu,e), write Kμ,e:S→P(S)K_{\mu,e}:S\to\mathbb{P}(S). Then Kμ,e(s)K_{\mu,e}(s) is the conditional distribution of semantic descendants of ss under population state μ\mu and environment ee.

4 Nonlinear replicator–mutator operator

Definition 4.1 Endogenous selection

Define Sel⁡w(μ,e)\operatorname{Sel}_w(\mu,e) by

dSel⁡w(μ,e)(s)=w(s,μ,e)wˉ(μ,e) dμ(s). d\operatorname{Sel}_w(\mu,e)(s) = \frac{w(s,\mu,e)}{\bar w(\mu,e)}\,d\mu(s).

Definition 4.2 Endogenous evolutionary operator

Define

R(μ,e)=(Kμ,e)∗Sel⁡w(μ,e). \mathcal{R}(\mu,e)=(K_{\mu,e})_*\operatorname{Sel}_w(\mu,e).

Equivalently, for an event AA,

R(μ,e)(A)=∫Sw(s,μ,e) Kμ,e(s,A) dμ(s)wˉ(μ,e). \mathcal{R}(\mu,e)(A) = \frac{\displaystyle\int_S w(s,\mu,e)\,K_{\mu,e}(s,A)\,d\mu(s)}{\bar w(\mu,e)}.

Unlike the exogenous case, R:P(S)×E→P(S)\mathcal{R}:\mathbb{P}(S)\times E\to\mathbb{P}(S) is generally nonlinear in μ\mu.

5 Population-indexed transfer operators

For each (μ,e)(\mu,e), define Lμ,e:M+(S)→M+(S)\mathcal{L}_{\mu,e}:\mathbb{M}_+(S)\to\mathbb{M}_+(S) by

(Lμ,eν)(A)=∫Sw(s,μ,e) Kμ,e(s,A) dν(s). (\mathcal{L}_{\mu,e}\nu)(A) = \int_S w(s,\mu,e)\,K_{\mu,e}(s,A)\,d\nu(s).

For fixed (μ,e)(\mu,e) the operator is linear in ν\nu. The assignment μ↦Lμ,e\mu\mapsto\mathcal{L}_{\mu,e} is generally nonlinear.

Proposition 5.1 Projectivization at a frozen operator

The evolutionary operator satisfies

R(μ,e)=Lμ,eμLμ,eμ(S). \mathcal{R}(\mu,e) = \frac{\mathcal{L}_{\mu,e}\mu}{\mathcal{L}_{\mu,e}\mu(S)}.

Proof.

Since Kμ,e(s,S)=1K_{\mu,e}(s,S)=1,

Lμ,eμ(S)=∫Sw(s,μ,e) dμ(s)=wˉ(μ,e). \mathcal{L}_{\mu,e}\mu(S)=\int_S w(s,\mu,e)\,d\mu(s)=\bar w(\mu,e).

Substitution gives the result.

Endogenous evolution remains a projectivized positive operator, but the operator itself now depends on the point being evolved.

6 Nonlinear eigenmeasure equilibrium

Definition 6.1 Endogenous equilibrium

For a fixed environment ee, a population μ∗\mu_* is an equilibrium if R(μ∗,e)=μ∗\mathcal{R}(\mu_*,e)=\mu_*.

Theorem 6.2 Nonlinear eigenmeasure characterization

A population μ∗\mu_* is an equilibrium if and only if

Lμ∗,eμ∗=λμ∗, \mathcal{L}_{\mu_*,e}\mu_*=\lambda\mu_*,

where λ=wˉ(μ∗,e)\lambda=\bar w(\mu_*,e).

Proof.

By Proposition 5.1, R(μ∗,e)=μ∗\mathcal{R}(\mu_*,e)=\mu_* if and only if Lμ∗,eμ∗=Lμ∗,eμ∗(S) μ∗\mathcal{L}_{\mu_*,e}\mu_*=\mathcal{L}_{\mu_*,e}\mu_*(S)\,\mu_*. But that total mass equals wˉ(μ∗,e)\bar w(\mu_*,e). Hence the equilibrium equation is exactly Lμ∗,eμ∗=λμ∗\mathcal{L}_{\mu_*,e}\mu_*=\lambda\mu_*.

The difference from the exogenous case is substantial. Previously one solved an eigenproblem for one fixed positive operator. Now one solves the self-consistent nonlinear problem

Lμμ∝μ. \mathcal{L}_{\mu}\mu\propto\mu.

7 Generalized Price identity

Let φ:S→R\varphi:S\to\mathbb{R} be an observable. Define

(Kμ,eφ)(s):=∫Sφ(t) Kμ,e(s,dt). (K_{\mu,e}\varphi)(s):=\int_S\varphi(t)\,K_{\mu,e}(s,dt).

Theorem 7.1 Generalized Price equation

For one evolutionary step μ′=R(μ,e)\mu'=\mathcal{R}(\mu,e),

Eμ′[φ]=Eμ[φ]+Cov⁡μ(wμ,e,φ)wˉ(μ,e)+Eμ[wμ,e(Kμ,eφ−φ)]wˉ(μ,e). \mathbb{E}_{\mu'}[\varphi] = \mathbb{E}_{\mu}[\varphi] + \frac{\operatorname{Cov}_{\mu}(w_{\mu,e},\varphi)}{\bar w(\mu,e)} + \frac{\mathbb{E}_{\mu}\bigl[w_{\mu,e}(K_{\mu,e}\varphi-\varphi)\bigr]}{\bar w(\mu,e)}.

Proof.

By definition,

Eμ′[φ]=Eμ[wμ,e Kμ,eφ]wˉ(μ,e). \mathbb{E}_{\mu'}[\varphi] = \frac{\mathbb{E}_{\mu}\bigl[w_{\mu,e}\,K_{\mu,e}\varphi\bigr]}{\bar w(\mu,e)}.

Add and subtract Eμ[wμ,eφ]\mathbb{E}_{\mu}[w_{\mu,e}\varphi] in the numerator:

Eμ′[φ]=Eμ[wφ]wˉ+Eμ[w(Kφ−φ)]wˉ. \mathbb{E}_{\mu'}[\varphi] = \frac{\mathbb{E}_{\mu}[w\varphi]}{\bar w} + \frac{\mathbb{E}_{\mu}\bigl[w(K\varphi-\varphi)\bigr]}{\bar w}.

The covariance identity

Eμ[wφ]=Cov⁡μ(w,φ)+wˉ Eμ[φ] \mathbb{E}_{\mu}[w\varphi]=\operatorname{Cov}_{\mu}(w,\varphi)+\bar w\,\mathbb{E}_{\mu}[\varphi]

then yields the stated formula.

The two correction terms are selection covariance and transmission change. The decomposition remains valid even though fitness depends on the population.

8 Pairwise interaction games

A particularly important endogenous fitness model is induced by pairwise interaction. Let

a:S×S×E⟶R a:S\times S\times E\longrightarrow\mathbb{R}

be a payoff function. For a state ss, population μ\mu, and environment ee, define

f(s,μ,e)=∫Sa(s,t,e) dμ(t). f(s,\mu,e)=\int_S a(s,t,e)\,d\mu(t).

This is the expected payoff obtained by ss when interacting with a random member of the population μ\mu.

9 From payoff to positive fitness

Discrete-time fitness should be positive. A canonical choice is

w(s,μ,e)=exp⁡(βf(s,μ,e)), w(s,\mu,e)=\exp\bigl(\beta f(s,\mu,e)\bigr),

where β≥0\beta\geq 0 is selection strength. For weak selection,

w=1+βf+O(β2). w=1+\beta f+O(\beta^2).

Thus additive payoff dynamics appears infinitesimally.

10 Finite evolutionary game

Let S={1,…,n}S=\{1,\ldots,n\} and let A=(Aij)A=(A_{ij}) be a payoff matrix. For a population x=(x1,…,xn)x=(x_1,\ldots,x_n), the payoff of type ii is

fi(x)=(Ax)i=∑jAijxj. f_i(x)=(Ax)_i=\sum_j A_{ij}x_j.

The population mean payoff is

fˉ(x)=∑ixifi(x)=x⊤Ax. \bar f(x)=\sum_i x_i f_i(x)=x^\top Ax.

11 Frequency-dependent replicator equation

Assume no mutation. Under the continuous-time weak-selection limit,

x˙i=xi(fi(x)−fˉ(x)). \dot x_i=x_i\bigl(f_i(x)-\bar f(x)\bigr).

Hence

x˙i=xi((Ax)i−x⊤Ax). \dot x_i=x_i\bigl((Ax)_i-x^\top Ax\bigr).

This is the classical frequency-dependent replicator equation.

12 Conservation theorem

Theorem 12.1 Invariance of the simplex

The simplex

Δ={xi≥0, ∑ixi=1} \Delta=\bigl\{x_i\geq 0,\ \sum_i x_i=1\bigr\}

is invariant under replicator dynamics.

Proof.

∑ix˙i=∑ixi(fi−fˉ)=fˉ−fˉ=0. \begin{aligned} \sum_i\dot x_i &= \sum_i x_i\bigl(f_i-\bar f\bigr) \\ &= \bar f-\bar f \\ &= 0. \end{aligned}

Thus total mass remains 1. Moreover, if xi=0x_i=0, then x˙i=0\dot x_i=0. Trajectories cannot cross a boundary face into negative coordinates.

13 Interior equilibria

Theorem 13.1 Equal-payoff characterization

Let x∗x_* be an interior population: x∗,i>0x_{*,i}>0 for every ii. Then x∗x_* is a fixed point of the pure replicator equation if and only if

fi(x∗)=fˉ(x∗)for every i. f_i(x_*)=\bar f(x_*)\qquad\text{for every }i.

Proof.

At equilibrium,

x∗,i(fi(x∗)−fˉ(x∗))=0. x_{*,i}\bigl(f_i(x_*)-\bar f(x_*)\bigr)=0.

Because x∗,i>0x_{*,i}>0, we must have fi(x∗)=fˉ(x∗)f_i(x_*)=\bar f(x_*). Conversely, if every payoff equals the mean payoff, every derivative vanishes.

Thus every semantic type appearing with positive frequency at an interior equilibrium has equal reproductive payoff.

14 Relation to Nash equilibrium

For a symmetric population game, a population x∗x_* is a symmetric Nash equilibrium if

fi(x∗)≤x∗⊤Ax∗ f_i(x_*)\leq x_*^\top A x_*

for every pure type ii, with equality for every type in the support of x∗x_*. Every interior replicator equilibrium satisfies the Nash condition because all pure strategies have the same payoff. Boundary replicator equilibria need not be Nash equilibria, since an absent strategy may have strictly greater payoff without affecting the instantaneous derivative of its zero frequency.

15 Symmetric games and a Lyapunov function

Assume A=A⊤A=A^\top. Define V(x)=x⊤AxV(x)=x^\top Ax.

Theorem 15.1 Mean-payoff monotonicity

For symmetric AA, along the replicator equation,

ddt(x⊤Ax)=2Var⁡x(f)≥0. \frac{d}{dt}\bigl(x^\top Ax\bigr)=2\operatorname{Var}_x(f)\geq 0.

Proof.

Since A=A⊤A=A^\top,

ddt(x⊤Ax)=2x˙⊤Ax. \frac{d}{dt}\bigl(x^\top Ax\bigr)=2\dot x^\top Ax.

With (Ax)i=fi(Ax)_i=f_i,

x˙⊤Ax=∑ixi(fi−fˉ) fi=∑ixifi2−fˉ∑ixifi=∑ixifi2−fˉ2=Var⁡x(f). \begin{aligned} \dot x^\top Ax &= \sum_i x_i(f_i-\bar f)\,f_i \\ &= \sum_i x_i f_i^2-\bar f\sum_i x_i f_i \\ &= \sum_i x_i f_i^2-\bar f^2 \\ &= \operatorname{Var}_x(f). \end{aligned}

Multiplying by 2 gives the result.

Symmetric frequency-dependent selection performs ascent on mean interaction payoff, except at states where all supported strategies have equal payoff.

16 Shahshahani gradient formulation

On the interior of the simplex define the Shahshahani metric

gij=δijxi. g_{ij}=\frac{\delta_{ij}}{x_i}.

For the potential Φ(x)=12x⊤Ax\Phi(x)=\tfrac12 x^\top Ax with symmetric AA, the replicator vector field is the constrained gradient of Φ\Phi under this metric:

x˙=∇ShΦ. \dot x=\nabla_{\mathrm{Sh}}\Phi.

The geometry of frequency-dependent selection is not ordinary Euclidean gradient flow but information-geometric gradient flow on the probability simplex.

17 Mutation–selection games

Let L(x)L(x) be a population-dependent Markov generator satisfying Lij(x)≥0L_{ij}(x)\geq 0 for i≠ji\neq j, and L(x)1=0L(x)\mathbf{1}=0. The continuous-time endogenous mutation–selection dynamics is

x˙j=xj(fj(x)−fˉ(x))+∑ixiLij(x). \dot x_j = x_j\bigl(f_j(x)-\bar f(x)\bigr) + \sum_i x_i L_{ij}(x).

Proposition 17.1 Probability conservation

∑jx˙j=0. \sum_j\dot x_j=0.

Proof.

The replicator contribution sums to zero by Theorem 12.1. For the mutation term,

∑j∑ixiLij(x)=∑ixi∑jLij(x)=0, \sum_j\sum_i x_i L_{ij}(x)=\sum_i x_i\sum_j L_{ij}(x)=0,

because each row of L(x)L(x) sums to zero.

18 Nonlinear Markov processes

The state-dependent mutation law μ↦Kμ\mu\mapsto K_\mu turns the Markov process into a nonlinear Markov process. The transition law is no longer K∗μtK_*\mu_t for one fixed KK, but

(Kμt)∗μt. (K_{\mu_t})_*\mu_t.

Adding selection gives

(Kμt)∗(wμtμt⟨wμt,μt⟩). (K_{\mu_t})_* \left( \frac{w_{\mu_t}\mu_t}{\langle w_{\mu_t},\mu_t\rangle} \right).

The population modifies its own transition operator.

19 Interacting populations

Let AA be a finite set of population classes. For every a∈Aa\in A, let SaS_a be its semantic state space. The joint population object is

P=∏a∈AP(Sa). \mathbf{P}=\prod_{a\in A}\mathbb{P}(S_a).

A joint state is (μa)a∈A(\mu_a)_{a\in A}.

20 Coupled fitness fields

Definition 20.1 Coupled fitness and mutation

For each population aa, let

wa:Sa×P×E→R>0. w_a:S_a\times\mathbf{P}\times E\to\mathbb{R}_{>0}.

The fitness of a state in population aa may depend on all populations simultaneously. Likewise let

Ka:Sa×P×E→P(Sa). K_a:S_a\times\mathbf{P}\times E\to\mathbb{P}(S_a).

21 Coupled evolutionary operator

Define

Ra(μ,e)=(Ka,μ,e)∗(wa(−,μ,e) μawˉa(μ,e)). \mathcal{R}_a(\boldsymbol{\mu},e) = (K_{a,\boldsymbol{\mu},e})_* \left( \frac{w_a(-,\boldsymbol{\mu},e)\,\mu_a}{\bar w_a(\boldsymbol{\mu},e)} \right).

The full coupled operator is

R=∏a∈ARa, \mathbf{R}=\prod_{a\in A}\mathcal{R}_a,

so R:P×E→P\mathbf{R}:\mathbf{P}\times E\to\mathbf{P}.

22 Pairwise multi-population games

Suppose population aa has states i=1,…,nai=1,\ldots,n_a. Let AijabA^{ab}_{ij} be the payoff to type ii in population aa from interacting with type jj in population bb. Then

fia(x)=∑b∈A∑jAijab xjb. f_i^a(\mathbf{x})=\sum_{b\in A}\sum_j A^{ab}_{ij}\,x_j^b.

Mean payoff within population aa is

fˉa=∑ixiafia. \bar f^a=\sum_i x_i^a f_i^a.

23 Coupled replicator equations

The continuous dynamics becomes

x˙ia=xia(fia(x)−fˉa(x)). \dot x_i^a=x_i^a\bigl(f_i^a(\mathbf{x})-\bar f^a(\mathbf{x})\bigr).

With mutation,

x˙ia=xia(fia−fˉa)+∑jxjaLjia(x). \dot x_i^a = x_i^a\bigl(f_i^a-\bar f^a\bigr) + \sum_j x_j^a L_{ji}^a(\mathbf{x}).

Theorem 23.1 Separate population normalization

For every aa, the condition ∑ixia=1\sum_i x_i^a=1 is invariant.

Proof.

For each fixed aa, the replicator contribution sums to fˉa−fˉa=0\bar f^a-\bar f^a=0. The mutation generator satisfies ∑iLjia=0\sum_i L_{ji}^a=0. Hence

ddt∑ixia=0. \frac{d}{dt}\sum_i x_i^a=0.

24 Environment as a dynamical object

So far e∈Ee\in E has been treated parametrically. Let

G:E×P(S)⟶E G:E\times\mathbb{P}(S)\longrightarrow E

be an environmental update. The closed coupled system is

F:P(S)×E⟶P(S)×E, F:\mathbb{P}(S)\times E\longrightarrow\mathbb{P}(S)\times E,

given by

F(μ,e)=(R(μ,e), G(e,μ)). F(\mu,e)=\bigl(\mathcal{R}(\mu,e),\,G(e,\mu)\bigr).

A closed equilibrium is a pair (μ∗,e∗)(\mu_*,e_*) satisfying

μ∗=R(μ∗,e∗),e∗=G(e∗,μ∗). \mu_*=\mathcal{R}(\mu_*,e_*),\qquad e_*=G(e_*,\mu_*).

This is a simultaneous population–environment fixed point.

25 Open evolutionary systems

Instead of closing the environment internally, one can expose it as an interface. Let (X,X′)(X,X') denote a system with forward state type XX and backward feedback type X′X'.

26 Stochastic lenses

Definition 26.1 Stochastic lens

A stochastic lens L:(X,X′)⟶(Y,Y′)L:(X,X')\longrightarrow(Y,Y') consists of a forward map

L→:X→Y L^{\rightarrow}:X\to Y

and a stochastic backward update

L←:X×Y′→P(X′). L^{\leftarrow}:X\times Y'\to\mathbb{P}(X').

The forward map exposes an observable output. The backward kernel propagates downstream feedback back into the upstream state or control variable.

27 Identity stochastic lens

The identity on (X,X′)(X,X') has forward part id⁡X\operatorname{id}_X and backward kernel δx′\delta_{x'}.

28 Composition of stochastic lenses

Let L:(X,X′)→(Y,Y′)L:(X,X')\to(Y,Y') and M:(Y,Y′)→(Z,Z′)M:(Y,Y')\to(Z,Z'). The composite has forward part

(M∘L)→=M→∘L→. (M\circ L)^{\rightarrow}=M^{\rightarrow}\circ L^{\rightarrow}.

For a backward feedback z′z', first sample

y′∼M←(L→(x),z′), y'\sim M^{\leftarrow}\bigl(L^{\rightarrow}(x),z'\bigr),

and then sample

x′∼L←(x,y′). x'\sim L^{\leftarrow}(x,y').

Hence the backward kernel is

(M∘L)←(x,z′)=∫Y′L←(x,dy′) M←(L→(x),z′). (M\circ L)^{\leftarrow}(x,z') = \int_{Y'} L^{\leftarrow}(x,dy')\, M^{\leftarrow}\bigl(L^{\rightarrow}(x),z'\bigr).

More invariantly, this is Kleisli composition using the strength of the commutative probability monad.

Theorem 28.1 Associativity

Composition of stochastic lenses is associative up to the canonical coherence of the probability monad.

Proof.

The forward components compose associatively because morphism composition does. The backward components compose by iterated Kleisli integration. Associativity therefore follows from associativity of the monad multiplication P3→P\mathbb{P}^3\to\mathbb{P}.

Stochastic lenses form a category, or an appropriate ∞-categorical enhancement when higher morphisms are retained.

29 Evolution as a stochastic lens

Take X=P(S)X=\mathbb{P}(S). Suppose a population exposes an observable O:P(S)→YO:\mathbb{P}(S)\to Y. The environment returns a fitness or interaction signal r∈Y′r\in Y'. An evolutionary response kernel has the form

U:P(S)×Y′→P(P(S)). U:\mathbb{P}(S)\times Y'\to\mathbb{P}\bigl(\mathbb{P}(S)\bigr).

Then

(O,U):(P(S),P(S))⟶(Y,Y′) (O,U):(\mathbb{P}(S),\mathbb{P}(S))\longrightarrow(Y,Y')

is a stochastic evolutionary lens. If the evolutionary update is deterministic, the backward kernel is δRr(μ)\delta_{\mathcal{R}_r(\mu)}.

30 Feedback closure

Suppose an environment lens converts the exposed population observable into a fitness signal. Composing the population and environment lenses closes the loop

Population, environment, fitness signal, and then the population update.

Endogenous fitness is a feedback phenomenon. The equation w=wμw=w_\mu is the algebraic shadow of this closed interaction.

31 Open evolutionary games

Definition 31.1 Evolutionary open game

An evolutionary open game consists of:

The payoff field need not itself prescribe the update. Different evolutionary rules may act on the same game.

32 Best-response correspondence

For fixed (μ,e)(\mu,e), define

BR⁡(μ,e)=argmax⁡s∈Su(s,μ,e). \operatorname{BR}(\mu,e)=\operatorname*{argmax}_{s\in S}u(s,\mu,e).

In a homotopical setting, BR⁡\operatorname{BR} should more properly be treated as a subobject or correspondence rather than an ordinary set.

Definition 32.1 Nash population

A population μ∗\mu_* is a Nash population at environment ee if

supp⁡(μ∗)⊆BR⁡(μ∗,e). \operatorname{supp}(\mu_*)\subseteq\operatorname{BR}(\mu_*,e).

Every semantic strategy represented with nonzero population weight must be a best response to the current population.

33 Interior replicator equilibria and Nash equilibrium

Theorem 33.1 Interior equilibria are Nash

For a finite symmetric population game, every interior equilibrium of the pure replicator equation is a Nash equilibrium.

Proof.

By Theorem 13.1, fi(x∗)=fˉ(x∗)f_i(x_*)=\bar f(x_*) for every ii. Since x∗x_* is interior, every pure strategy appears in its support. Hence every pure strategy obtains exactly the same payoff, every pure strategy is a best response, and supp⁡(x∗)⊆BR⁡(x∗)\operatorname{supp}(x_*)\subseteq\operatorname{BR}(x_*).

34 Evolutionary dynamics versus rational optimization

The framework distinguishes two maps:

BR⁡:P(S)⇉S \operatorname{BR}:\mathbb{P}(S)\rightrightarrows S

and

R:P(S)→P(S). \mathcal{R}:\mathbb{P}(S)\to\mathbb{P}(S).

The first is an optimization correspondence. The second is a population dynamical law. Replicator dynamics need not jump to best responses. It continuously amplifies strategies according to relative payoff.

A game equilibrium is not an evolutionary update rule.

The two become linked through equilibrium theorems, not by definition.

35 Observer-relative games

Let q:M→Sq:M\to S be a capability-relative semantic quotient. Suppose microscopic interaction payoff is

u~:M×M×E→R. \widetilde u:M\times M\times E\to\mathbb{R}.

A closed game exists on SS only if the payoff descends: there is u:S×S×E→Ru:S\times S\times E\to\mathbb{R} with

u~(m,n,e)=u(q(m),q(n),e). \widetilde u(m,n,e)=u\bigl(q(m),q(n),e\bigr).

If this fails, two states indistinguishable to the observer may nevertheless have different interaction consequences. The coarse observer then does not possess a closed game description.

36 Game-theoretic lumpability

Definition 36.1 Strategic lumpability

A semantic quotient q:M→Sq:M\to S is strategically lumpable if payoff factors through qq, mutation and transmission are qq-lumpable, and environmental coupling factors through the coarse state. Under these conditions, the observer-relative population dynamics on SS closes autonomously.

Theorem 36.2 Observer-relative evolutionary closure

If qq is strategically lumpable, then there exists an induced evolutionary game on SS whose population trajectory is exactly the pushforward of the microscopic trajectory:

q∗∘R≃RS∘q∗. q_*\circ\mathcal{R}\simeq\mathcal{R}_S\circ q_*.

Proof.

Payoff factorization implies that the induced fitness depends only on the coarse state. Kernel lumpability implies that stochastic transmission commutes with q∗q_*. Environmental factorization ensures that feedback signals are also determined by the coarse population. The coarse-graining theorem for replicator–mutator dynamics therefore applies at every feedback step.

37 Failure of strategic lumpability

If strategic lumpability fails, coarse population state is not sufficient to predict its own future. One must augment the state with hidden variables. Formally, if

q∗μ=q∗ν q_*\mu=q_*\nu

but

q∗R(μ)≠q∗R(ν), q_*\mathcal{R}(\mu)\neq q_*\mathcal{R}(\nu),

then no map F:P(S)→P(S)F:\mathbb{P}(S)\to\mathbb{P}(S) can satisfy

F∘q∗=q∗∘R F\circ q_*=q_*\circ\mathcal{R}

for all microscopic states. The observer’s coarse semantics is dynamically incomplete.

38 Memory from hidden semantic variables

One can repair non-Markovian coarse dynamics by enlarging the observed state. Suppose

h:M→H h:M\to H

records additional hidden semantic information. Replace q:M→Sq:M\to S by

(q,h):M→S×H. (q,h):M\to S\times H.

If the enlarged quotient is lumpable, then (S,H)(S,H) is a sufficient state representation. This yields a formal criterion for semantic state augmentation:

Add enough hidden structure until the dynamics closes.

39 Higher-order frequency dependence

Pairwise interaction is not essential. Let

an:Sn+1×E→R. a_n:S^{n+1}\times E\to\mathbb{R}.

Define

fn(s,μ,e)=∫Snan(s,s1,…,sn,e) dμ(s1)⋯dμ(sn). f_n(s,\mu,e) = \int_{S^n} a_n(s,s_1,\ldots,s_n,e)\, d\mu(s_1)\cdots d\mu(s_n).

Total payoff may be

f(s,μ,e)=∑n≥0λnfn(s,μ,e). f(s,\mu,e)=\sum_{n\geq 0}\lambda_n f_n(s,\mu,e).

This is the stochastic-population analogue of the earlier operadic theory. Fitness can depend on genuinely multi-agent semantic interactions rather than only pairwise games.

40 Polynomial interaction laws

An even more categorical formulation uses a polynomial

I←sE→pB→tI. I\xleftarrow{s}E\xrightarrow{p}B\xrightarrow{t}I.

Each operation b∈Bb\in B specifies a finite interaction pattern. Given a population μ\mu, one samples inputs according to the corresponding fibers EbE_b, evaluates their interaction, and produces a fitness contribution. Polynomial functors specify the combinatorial shape of interactions, while the probability monad integrates over randomly sampled participants.

Polynomial interaction together with probability yields endogenous fitness.

41 Operadic game interaction

If O\mathcal{O} is an ∞-operad of interaction forms, one may assign to o∈O(n)o\in\mathcal{O}(n) a payoff map uo:Sn+1→Ru_o:S^{n+1}\to\mathbb{R}. The population-level expected payoff is obtained by integrating the other nn arguments against μ\mu:

∫Snuo(s,s1,…,sn) dμ⊗n. \int_{S^n} u_o(s,s_1,\ldots,s_n)\, d\mu^{\otimes n}.

Operadic recombination and evolutionary game interaction can be encoded by the same arity structure.

42 Endogenous semantic fidelity

Semantic fidelity relative to the class [s]c[s]_c, in the exogenous theory, was K(s,[s]c)K(s,[s]_c). With endogenous transmission,

Fc(s,μ,e)=Kμ,e(s,[s]c). F_c(s,\mu,e)=K_{\mu,e}\bigl(s,[s]_c\bigr).

Replication fidelity itself becomes frequency dependent. A meme may copy accurately when rare and inaccurately when common, or conversely.

43 Effective endogenous reproduction number

Define

Rc(s,μ,e)=w(s,μ,e) Fc(s,μ,e). R_c(s,\mu,e)=w(s,\mu,e)\,F_c(s,\mu,e).

This quantity measures expected production of descendants that remain in the same observer-relative semantic class. Unlike a fixed reproduction number, RcR_c changes with the population itself.

44 Frequency-dependent semantic selection

Suppose two semantic classes aa and bb satisfy

Rc(a,μ)>Rc(b,μ) R_c(a,\mu)>R_c(b,\mu)

for one population state, but

Rc(a,ν)<Rc(b,ν) R_c(a,\nu)<R_c(b,\nu)

for another. Then there is no absolute fitness ordering between aa and bb. Fitness is relational: a property of a state relative to a population and an environment. This is the categorical analogue of frequency-dependent selection.

45 Nonlinear categorical dynamics

The exogenous theory had, for a fixed positive operator L\mathcal{L},

μt+1=LμtLμt(S). \mu_{t+1}=\frac{\mathcal{L}\mu_t}{\mathcal{L}\mu_t(S)}.

The endogenous theory has

μt+1=LμtμtLμtμt(S). \mu_{t+1} = \frac{\mathcal{L}_{\mu_t}\mu_t}{\mathcal{L}_{\mu_t}\mu_t(S)}.

The underlying geometry is not projective linear dynamics but a nonlinear self-map of the probability object. Equilibria solve

Lμ∗μ∗=λμ∗. \mathcal{L}_{\mu_*}\mu_*=\lambda\mu_*.

46 Linearization around equilibrium

Let F(μ)=R(μ,e)F(\mu)=\mathcal{R}(\mu,e) for fixed ee. At an equilibrium F(μ∗)=μ∗F(\mu_*)=\mu_*, local stability is controlled by the derivative DFμ∗DF_{\mu_*}. If Tμ∗P(S)T_{\mu_*}\mathbb{P}(S) denotes the tangent space of normalized perturbations, then infinitesimal perturbations evolve by

δμt+1=DFμ∗ δμt. \delta\mu_{t+1}=DF_{\mu_*}\,\delta\mu_t.

Definition 46.1 Local asymptotic stability

The equilibrium is linearly asymptotically stable if the spectral radius of

DFμ∗∣Tμ∗P(S) DF_{\mu_*}\big|_{T_{\mu_*}\mathbb{P}(S)}

is less than 1 in discrete time. For continuous time, stability requires the relevant eigenvalues of the Jacobian to have negative real part. Second-order structure re-enters through the derivative of the population-dependent fitness field.

47 Feedback derivatives

Since w=w(s,μ,e)w=w(s,\mu,e), the derivative of the evolutionary operator contains two contributions:

DF=Ddirect+Dfeedback. DF=D_{\mathrm{direct}}+D_{\mathrm{feedback}}.

The direct term changes population frequencies while holding the fitness field fixed. The feedback term arises because perturbing μ\mu changes wμw_\mu and potentially KμK_\mu. This term has no analogue in a fixed-fitness linear transfer system.

48 Second-order interaction structure

For a finite game with payoff field fi(x)f_i(x), define

Jij=∂fi∂xj. J_{ij}=\frac{\partial f_i}{\partial x_j}.

Then JJ measures how increasing population type jj changes the reproductive payoff of type ii. For the linear game f(x)=Axf(x)=Ax, one has J=AJ=A. For nonlinear interaction games, D2fD^2f measures how those interaction effects themselves change with population composition. Higher derivatives classify increasingly high-order ecological or memetic feedback.

49 Stochastic-lens interpretation of endogenous fitness

The complete feedback structure can be written schematically as

P(S)→OY→env.Y′⟶Fit⁡(S)⟶P(S). \mathbb{P}(S) \xrightarrow{O} Y \xrightarrow{\mathrm{env.}} Y' \longrightarrow \operatorname{Fit}(S) \longrightarrow \mathbb{P}(S).

Closing the interfaces gives

P(S)⟶P(S). \mathbb{P}(S)\longrightarrow\mathbb{P}(S).

The endogenous nonlinear map is the closed semantics of an open stochastic system. Lens-like semantics is natural here because forward behavior and backward feedback are separate components before the system is closed.

50 Main theorem

Theorem 50.1 Endogenous memetic evolution theorem

Let H=Sh⁡∞(C,J)\mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J) be equipped with a compatible probability monad P\mathbb{P}. Let S,E∈HS,E\in\mathcal{H}, and suppose given

w:S×P(S)×E→R>0 w:S\times\mathbb{P}(S)\times E\to\mathbb{R}_{>0}

and

K:S×P(S)×E→P(S). K:S\times\mathbb{P}(S)\times E\to\mathbb{P}(S).

Then:

R(μ,e)=(Kμ,e)∗(wμ,eμwˉ(μ,e)) \mathcal{R}(\mu,e) = (K_{\mu,e})_* \left(\frac{w_{\mu,e}\mu}{\bar w(\mu,e)}\right)

defines a normalized nonlinear evolutionary operator. See Sections 4–5.

x˙i=xi((Ax)i−x⊤Ax). \dot x_i=x_i\bigl((Ax)_i-x^\top Ax\bigr).

See Sections 8–11.

ddt(x⊤Ax)=2Var⁡x(Ax)≥0. \frac{d}{dt}(x^\top Ax)=2\operatorname{Var}_x(Ax)\geq 0.

See Theorem 15.1.

Proof.

Statements (i)–(x) are established in Sections 4–6, Theorem 7.1, Sections 8–17, Sections 19–23, Sections 24–30, Theorem 28.1, and Sections 35–38.

51 Full specification

The endogenous theory is summarized by

(C, J, H, P, M, Q, S, E, w, K, R). (\mathcal{C},\,J,\,\mathcal{H},\,\mathbb{P},\,M,\,Q,\,S,\,E,\,w,\,K,\,\mathcal{R}).

The fundamental equation is

μt+1=Lμt,etμtLμt,etμt(S). \mu_{t+1} = \frac{\mathcal{L}_{\mu_t,e_t}\mu_t}{\mathcal{L}_{\mu_t,e_t}\mu_t(S)}.

If the environment also evolves,

μt+1=R(μt,et),et+1=G(et,μt). \begin{aligned} \mu_{t+1}&=\mathcal{R}(\mu_t,e_t),\\ e_{t+1}&=G(e_t,\mu_t). \end{aligned}

52 Conceptual synthesis

The earlier fixed-fitness theory treated the memetic environment as external:

μ→Lμ′. \mu\xrightarrow{\mathcal{L}}\mu'.

The endogenous theory closes the feedback loop:

μ→wμ→Lμ→μ′→wμ′→⋯ . \mu\to w_\mu\to\mathcal{L}_\mu\to\mu'\to w_{\mu'}\to\cdots.

The population constructs part of its own selective environment. For pairwise interaction,

f(s,μ)=∫Sa(s,t) dμ(t), f(s,\mu)=\int_S a(s,t)\,d\mu(t),

and consequently

μ↦wμ↦R(μ). \mu\mapsto w_\mu\mapsto\mathcal{R}(\mu).

This turns memetic evolution from a linear transmission problem into a feedback game.

The stochastic-lens perspective separates the open interfaces: observation and feedback. The evolutionary-game perspective supplies interaction, then payoff, then relative reproduction. The ∞-topos layer determines which distinctions count as semantic distinctions for a given cognitive architecture. The probability monad determines stochastic inheritance. The game determines frequency-dependent fitness. The lens structure determines how open components are coupled.

Observer-relative stochastic game dynamics with feedback.

Equivalently, endogenous memetic evolution.

The theory no longer assigns a meme an absolute reproductive fitness. Fitness is a relational functional

w:S×P(S)×E→R>0, w:S\times\mathbb{P}(S)\times E\to\mathbb{R}_{>0},

and replication occurs within the nonlinear closed loop generated by that functional.

Part 6: Coevolution of Memes and Cognitive Architectures

Abstract

We extend memetic replication to the case in which cognitive architectures themselves evolve under memetic influence. The semantic equivalence relation defining what counts as the same meme is therefore no longer fixed.

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site, and let M:Cop→SM:\mathcal{C}^{\mathrm{op}}\to\mathcal{S} be a memetic field. Its Grothendieck construction ∫CM\int_{\mathcal{C}}M has objects (c,m)(c,m), an architecture together with a memetic state as realized by that architecture. The joint population state space is the maximal ∞-groupoid

ΩM:=(∫CM)≃. \Omega_M:=\Bigl(\int_{\mathcal{C}}M\Bigr)^{\simeq}.

A population is ν∈P(ΩM)\nu\in\mathbb{P}(\Omega_M), where P\mathbb{P} is a compatible probability monad. Fitness and transmission may depend on the complete joint population:

w:ΩM×P(ΩM)×E→R>0, w:\Omega_M\times\mathbb{P}(\Omega_M)\times E\to\mathbb{R}_{>0},

K:ΩM×P(ΩM)×E→P(ΩM). K:\Omega_M\times\mathbb{P}(\Omega_M)\times E\to\mathbb{P}(\Omega_M).

The evolutionary equation is

R(ν,e)=(Kν,e)∗(w(−,ν,e) ν∫ΩMw(x,ν,e) dν(x)). \mathcal{R}(\nu,e) = (K_{\nu,e})_* \left( \frac{w(-,\nu,e)\,\nu}{\displaystyle\int_{\Omega_M}w(x,\nu,e)\,d\nu(x)} \right).

Because the kernel may change the architecture coordinate, memes can modify the observer modality through which subsequent memetic identity is evaluated. If architecture cc carries a semantic localization LcL_c, then m≃cnm\simeq_c n need not imply m≃dnm\simeq_d n after the architecture changes from cc to dd. Memetic evolution and semantic identity become mutually coupled.

Memes alter cognition, cognition alters memetic identity, and identity alters subsequent selection.

We formulate this coupled dynamics, derive its finite-state and continuous-time forms, characterize its equilibria, establish exact conditions under which architecture or meme marginals evolve autonomously, and prove an identity-transport theorem specifying when semantic identity is preserved under cognitive change. The fixed-architecture theory is Note V.

Notation

1 Cognitive architectures

Let C\mathcal{C} be a small ∞-category whose objects are cognitive architectures. A morphism u:c→du:c\to d represents an admissible semantic translation, abstraction, simulation, or architectural transformation. Equip C\mathcal{C} with a Grothendieck topology JJ, producing the cognitive ∞-site (C,J)(\mathcal{C},J). Its semantic ∞-topos is

H=Sh⁡∞(C,J). \mathcal{H}=\operatorname{Sh}_{\infty}(\mathcal{C},J).

2 Memetic fields

Definition 2.1 Memetic field

A memetic field is an ∞-sheaf

M:Cop⟶S. M:\mathcal{C}^{\mathrm{op}}\longrightarrow\mathcal{S}.

For every architecture cc, M(c)M(c) is the space of memetic states representable at cc. For every architecture morphism u:c→du:c\to d, there is a restriction or translation

u∗:M(d)→M(c). u^*:M(d)\to M(c).

3 The joint architecture–meme state space

The appropriate state is no longer merely a meme mm. It is a pair (c,m)(c,m), encoded by the Grothendieck construction.

Definition 3.1 Memetic Grothendieck construction

Let ∫CM\int_{\mathcal{C}}M denote the ∞-category obtained by unstraightening M:Cop→SM:\mathcal{C}^{\mathrm{op}}\to\mathcal{S}. There is a canonical right fibration

p:∫CM⟶C. p:\int_{\mathcal{C}}M\longrightarrow\mathcal{C}.

Its objects are pairs (c,m)(c,m) with m∈M(c)m\in M(c).

Definition 3.2 Joint population space

Define

ΩM:=(∫CM)≃ \Omega_M:=\Bigl(\int_{\mathcal{C}}M\Bigr)^{\simeq}

to be the maximal ∞-groupoid of the Grothendieck construction. The architecture projection

π:ΩM→C≃ \pi:\Omega_M\to\mathcal{C}^{\simeq}

satisfies π(c,m)=c\pi(c,m)=c.

4 Architecture-dependent semantics

Suppose each architecture possesses its own semantic resolution. Let Mod⁡(E)\operatorname{Mod}(\mathcal{E}) denote an appropriate ∞-category of left-exact accessible modalities on an ambient ∞-topos E\mathcal{E}.

Definition 4.1 Cognitive modality assignment

A cognitive modality assignment is a functor

L:C⟶Mod⁡(E). L:\mathcal{C}\longrightarrow\operatorname{Mod}(\mathcal{E}).

For c∈Cc\in\mathcal{C}, write LcL_c for the corresponding semantic localization.

Definition 4.2 Architecture-relative semantic equivalence

For ambient memetic realizations m,nm,n, write m≃cnm\simeq_c n when

Id⁡LcM(ηc(m),ηc(n)) \operatorname{Id}_{L_c M}\bigl(\eta_c(m),\eta_c(n)\bigr)

is inhabited. The architecture cc participates directly in the definition of memetic identity.

5 Dynamic semantic identity

If the architecture changes, the equivalence relation itself may change.

Definition 5.1 Semantic refinement

A transition c⇝dc\rightsquigarrow d is semantically refining on MM if m≃dnm\simeq_d n implies m≃cnm\simeq_c n. Equivalence at dd is then at least as discriminating as equivalence at cc.

Definition 5.2 Semantic coarsening

A transition c⇝dc\rightsquigarrow d is semantically coarsening if m≃cnm\simeq_c n implies m≃dnm\simeq_d n.

Definition 5.3 Identity-changing transition

The transition changes semantic identity if there exist m,nm,n such that either m≃cnm\simeq_c n but m̸≃dnm\not\simeq_d n, or m̸≃cnm\not\simeq_c n but m≃dnm\simeq_d n. Architecture evolution can split or merge semantic equivalence classes.

6 Identity transport along architecture morphisms

Suppose u:c→du:c\to d induces a semantic translation u∗:M(d)→M(c)u^*:M(d)\to M(c).

Theorem 6.1 Identity transport

If x≃dyx\simeq_d y, then u∗x≃cu∗yu^*x\simeq_c u^*y.

Proof.

A witness of x≃dyx\simeq_d y is a path p:x≃yp:x\simeq y in M(d)M(d). Applying u∗u^* gives a path u∗p:u∗x≃u∗yu^*p:u^*x\simeq u^*y. Hence u∗x≃cu∗yu^*x\simeq_c u^*y.

This concerns functorial translation. It does not assert that arbitrary architecture changes preserve the same equivalence relation on an underlying presentation space.

7 Identity-preserving cognitive change

Definition 7.1 Semantically conservative transition

Suppose an architecture transition c→dc\to d comes with a translation Tcd:M(c)→M(d)T_{cd}:M(c)\to M(d). It is semantically conservative if

x≃cy  ⟺  Tcd(x)≃dTcd(y). x\simeq_c y \iff T_{cd}(x)\simeq_d T_{cd}(y).

Theorem 7.2 Identity invariance along conservative paths

Let c0→c1→⋯→cnc_0\to c_1\to\cdots\to c_n be a sequence of semantically conservative architecture changes. Then for all x,y∈M(c0)x,y\in M(c_0),

x≃c0y x\simeq_{c_0} y

if and only if

T0n(x)≃cnT0n(y), T_{0n}(x)\simeq_{c_n} T_{0n}(y),

where

T0n=Tcn−1cn∘⋯∘Tc0c1. T_{0n}=T_{c_{n-1}c_n}\circ\cdots\circ T_{c_0c_1}.

Proof.

Each transition preserves and reflects semantic equivalence. Apply this equivalence successively along the path.

Corollary 7.3 Where identity can change

Semantic identity can change along a cognitive trajectory only if at least one architecture transition fails to be conservative.

8 Reflexive memetic influence

A meme may influence the architecture through which it is interpreted.

Definition 8.1 Architecture transition kernel

A cognitive plasticity kernel is a Markov kernel

A:ΩM⟶P(C≃). A:\Omega_M\longrightarrow\mathbb{P}(\mathcal{C}^{\simeq}).

For (c,m)∈ΩM(c,m)\in\Omega_M, the distribution A(c,m)A(c,m) describes possible successor cognitive architectures.

Definition 8.2 Memetically induced cognitive plasticity

The cognitive transition is memetically induced if A(c,m)A(c,m) depends nontrivially on mm: there exist m,n∈M(c)m,n\in M(c) with A(c,m)≠A(c,n)A(c,m)\neq A(c,n). Different memes can alter the future cognitive architecture differently.

9 Joint transition kernel

Rather than factor architecture and meme transitions separately, the invariant formulation uses one kernel on the total state space.

Definition 9.1 Joint evolutionary kernel

A joint architecture–meme transition kernel is

K:ΩM⟶P(ΩM). K:\Omega_M\longrightarrow\mathbb{P}(\Omega_M).

For a state x=(c,m)x=(c,m), the distribution K(x)K(x) describes the joint law of successor architectures and successor memetic realizations. The architecture kernel is recovered by pushforward:

A=P(π)∘K. A=\mathbb{P}(\pi)\circ K.

10 Conditional factorization

Under a suitable disintegration, the joint kernel factors schematically as an architecture choice followed by a conditional memetic choice:

K((c,m),d(c′,m′))=A((c,m),dc′) B((c,m),c′,dm′). K\bigl((c,m),d(c',m')\bigr)=A\bigl((c,m),dc'\bigr)\,B\bigl((c,m),c',dm'\bigr).

Here A((c,m),dc′)A((c,m),dc') chooses the successor architecture, while B((c,m),c′,dm′)B((c,m),c',dm') chooses the successor memetic realization conditional on that architecture. The factorization is useful conceptually but is not needed for the invariant theory.

11 Joint populations

Assume a compatible probability monad P\mathbb{P}.

Definition 11.1 Joint cognitive–memetic population

A population is

ν∈P(ΩM). \nu\in\mathbb{P}(\Omega_M).

It describes the joint distribution of architectures and memetic states.

12 Architecture marginal

Define

a=π∗ν∈P(C≃). a=\pi_*\nu\in\mathbb{P}(\mathcal{C}^{\simeq}).

This is the distribution of cognitive architectures.

13 Conditional memetic populations

Where disintegration exists, write

dν(c,m)=a(dc) μc(dm), d\nu(c,m)=a(dc)\,\mu_c(dm),

where μc∈P(M(c))\mu_c\in\mathbb{P}(M(c)) is the conditional memetic distribution within architecture cc. A joint population is architecture frequencies together with architecture-conditioned meme distributions.

14 Endogenous joint fitness

Let EE be an environment object.

Definition 14.1 Joint fitness field

A joint fitness field is

w:ΩM×P(ΩM)×E→R>0. w:\Omega_M\times\mathbb{P}(\Omega_M)\times E\to\mathbb{R}_{>0}.

Thus w((c,m),ν,e)w((c,m),\nu,e) may depend on the architecture, the meme, the entire joint population, and the environment.

15 Joint selection

Define mean fitness

wˉ(ν,e)=∫ΩMw(x,ν,e) dν(x). \bar w(\nu,e)=\int_{\Omega_M}w(x,\nu,e)\,d\nu(x).

The selected population is

dSel⁡w(ν,e)(x)=w(x,ν,e)wˉ(ν,e) dν(x). d\operatorname{Sel}_w(\nu,e)(x)=\frac{w(x,\nu,e)}{\bar w(\nu,e)}\,d\nu(x).

16 Coevolutionary operator

Allow the transition kernel itself to depend on (ν,e)(\nu,e):

Kν,e:ΩM→P(ΩM). K_{\nu,e}:\Omega_M\to\mathbb{P}(\Omega_M).

Definition 16.1 Cognitive–memetic evolutionary operator

Define

R(ν,e)=(Kν,e)∗Sel⁡w(ν,e). \mathcal{R}(\nu,e)=(K_{\nu,e})_*\operatorname{Sel}_w(\nu,e).

Explicitly, for an event BB,

R(ν,e)(B)=∫ΩMw(x,ν,e) Kν,e(x,B) dν(x)wˉ(ν,e). \mathcal{R}(\nu,e)(B) = \frac{\displaystyle\int_{\Omega_M}w(x,\nu,e)\,K_{\nu,e}(x,B)\,d\nu(x)}{\bar w(\nu,e)}.

This is the central evolution equation.

17 Population-dependent transfer operator

For fixed (ν,e)(\nu,e), define Lν,e\mathcal{L}_{\nu,e} on finite positive measures by

(Lν,eρ)(B)=∫ΩMw(x,ν,e) Kν,e(x,B) dρ(x). (\mathcal{L}_{\nu,e}\rho)(B)=\int_{\Omega_M}w(x,\nu,e)\,K_{\nu,e}(x,B)\,d\rho(x).

Then

R(ν,e)=Lν,eνLν,eν(ΩM). \mathcal{R}(\nu,e) = \frac{\mathcal{L}_{\nu,e}\nu}{\mathcal{L}_{\nu,e}\nu(\Omega_M)}.

18 Coevolutionary equilibrium

Definition 18.1 Joint equilibrium

A population ν∗\nu_* is an equilibrium at environment ee if R(ν∗,e)=ν∗\mathcal{R}(\nu_*,e)=\nu_*.

Theorem 18.2 Nonlinear eigenmeasure theorem

A joint population ν∗\nu_* is an equilibrium if and only if

Lν∗,eν∗=λν∗, \mathcal{L}_{\nu_*,e}\nu_*=\lambda\nu_*,

where λ=wˉ(ν∗,e)\lambda=\bar w(\nu_*,e).

Proof.

As in the fixed-architecture theory, R(ν∗,e)=ν∗\mathcal{R}(\nu_*,e)=\nu_* if and only if Lν∗,eν∗=Lν∗,eν∗(ΩM) ν∗\mathcal{L}_{\nu_*,e}\nu_*=\mathcal{L}_{\nu_*,e}\nu_*(\Omega_M)\,\nu_*. Since the kernel is normalized,

Lν∗,eν∗(ΩM)=∫ΩMw(x,ν∗,e) dν∗(x)=wˉ(ν∗,e). \mathcal{L}_{\nu_*,e}\nu_*(\Omega_M)=\int_{\Omega_M}w(x,\nu_*,e)\,d\nu_*(x)=\bar w(\nu_*,e).

19 Evolution of the architecture marginal

Let a′=π∗ν′a'=\pi_*\nu', and write Aν,e=P(π)∘Kν,eA_{\nu,e}=\mathbb{P}(\pi)\circ K_{\nu,e}.

Theorem 19.1 Architecture marginal equation

The successor architecture distribution is

a′(D)=∫ΩMw(x,ν,e) Aν,e(x,D) dν(x)wˉ(ν,e). a'(D) = \frac{\displaystyle\int_{\Omega_M}w(x,\nu,e)\,A_{\nu,e}(x,D)\,d\nu(x)}{\bar w(\nu,e)}.

Proof.

By definition a′=π∗R(ν,e)a'=\pi_*\mathcal{R}(\nu,e), so a′(D)=R(ν,e)(π−1(D))a'(D)=\mathcal{R}(\nu,e)(\pi^{-1}(D)). Substitute Definition 16.1:

a′(D)=∫w(x,ν,e) Kν,e(x,π−1(D)) dν(x)wˉ. a'(D) = \frac{\displaystyle\int w(x,\nu,e)\,K_{\nu,e}\bigl(x,\pi^{-1}(D)\bigr)\,d\nu(x)}{\bar w}.

The inner kernel probability is Aν,e(x,D)A_{\nu,e}(x,D).

20 Memes can select cognitive architectures

Even if cognitive architectures have no fitness term independent of memes, architecture frequencies may change because fitness is correlated with the memes carried within each architecture. Suppose architecture never mutates, so A((c,m),D)=δc(D)A((c,m),D)=\delta_c(D). Then

a′(c)=∫M(c)w((c,m),ν,e) dν(c,m)wˉ. a'(c) = \frac{\displaystyle\int_{M(c)}w((c,m),\nu,e)\,d\nu(c,m)}{\bar w}.

Architectures correlated with highly reproducing memes increase in frequency.

21 Memes can directly transform architectures

If A((c,m),d)A((c,m),d) depends on mm, then architecture evolution occurs even without differential survival between architectures. This is a stronger feedback mechanism:

m→c′→Lc′→new semantic equivalence. m\to c'\to L_{c'}\to\text{new semantic equivalence}.

The meme changes the modality under which subsequent memes are classified.

22 Reflexive memes

Definition 22.1 Reflexive meme

A memetic state m∈M(c)m\in M(c) is reflexive if its transmission can alter the architecture in a way that changes semantic distinctions relevant to future memetic transmission. Formally, there exists an architecture dd in the support of A(c,m)A(c,m) and states x,yx,y such that x≃cyx\simeq_c y but Tcd(x)̸≃dTcd(y)T_{cd}(x)\not\simeq_d T_{cd}(y), or conversely. A reflexive meme modifies the equivalence relation used to classify later memes.

23 Semantic self-modification

The resulting causal structure is

mt⟶ct+1⟶≃ct+1⟶classification of mt+1. m_t\longrightarrow c_{t+1}\longrightarrow\simeq_{c_{t+1}}\longrightarrow\text{classification of }m_{t+1}.

This is stronger than ordinary frequency-dependent fitness. The state changes not merely its future reproduction probability but the semantic state space in which reproduction is evaluated.

24 Identity drift

Definition 24.1 Identity drift

For two persistent presentations x,yx,y, define their time-dependent semantic relation

It(x,y)=Id⁡LctM(ηct(x),ηct(y)). I_t(x,y)=\operatorname{Id}_{L_{c_t}M}\bigl(\eta_{c_t}(x),\eta_{c_t}(y)\bigr).

Identity drift occurs if the homotopy type of It(x,y)I_t(x,y) changes with tt. Identity can therefore itself be a dynamical variable.

25 Identity-drift theorem

Theorem 25.1 Conservative paths do not drift

Suppose every architecture transition along a trajectory is semantically conservative. Then no semantic identity drift occurs along that trajectory.

Proof.

By Theorem 7.2, every conservative transition preserves and reflects semantic equivalence. Therefore the truth and homotopy class of x≃ctyx\simeq_{c_t}y is invariant along the trajectory, up to the coherent transport supplied by the transitions.

Corollary 25.2 Drift requires a nonconservative step

Every identity-changing trajectory contains a nonconservative cognitive transition.

26 Dynamic coarse-graining

Suppose every architecture cc has a semantic quotient qc:M(c)→S(c)q_c:M(c)\to S(c). The semantic state at time tt is not merely q(mt)q(m_t), but

qct(mt). q_{c_t}(m_t).

Both arguments evolve.

27 Total semantic field

Let S:Cop→SS:\mathcal{C}^{\mathrm{op}}\to\mathcal{S} be the semantic quotient field, with a natural transformation q:M⇒Sq:M\Rightarrow S. Taking Grothendieck constructions yields

∫CM⟶∫CS. \int_{\mathcal{C}}M\longrightarrow\int_{\mathcal{C}}S.

Passing to cores gives

q^:ΩM→ΩS. \widehat q:\Omega_M\to\Omega_S.

The observer-relative semantic state of a joint architecture–meme state is a point of the total semantic space ΩS\Omega_S.

28 Semantic population

The semantic population induced by ν\nu is

σ=q^∗ν∈P(ΩS). \sigma=\widehat q_*\nu\in\mathbb{P}(\Omega_S).

A central question is whether σ\sigma evolves autonomously.

29 Dynamic semantic lumpability

Definition 29.1 Dynamic semantic lumpability

The joint evolutionary system is lumpable through q^:ΩM→ΩS\widehat q:\Omega_M\to\Omega_S if there exists an induced operator RS:P(ΩS)→P(ΩS)\mathcal{R}_S:\mathbb{P}(\Omega_S)\to\mathbb{P}(\Omega_S) such that

RS∘q^∗=q^∗∘R. \mathcal{R}_S\circ\widehat q_*=\widehat q_*\circ\mathcal{R}.

30 Closure theorem

Theorem 30.1 Exact semantic closure

Suppose fitness factors through the total semantic quotient,

w(x,ν,e)=wS(q^(x),q^∗ν,e), w(x,\nu,e)=w_S\bigl(\widehat q(x),\widehat q_*\nu,e\bigr),

and the transition kernel is lumpable,

P(q^)∘Kν,e≃Kq^∗ν, eS∘q^. \mathbb{P}(\widehat q)\circ K_{\nu,e}\simeq K^S_{\widehat q_*\nu,\,e}\circ\widehat q.

Then there exists a closed evolutionary operator RS\mathcal{R}_S on semantic populations satisfying

q^∗R(ν,e)=RS(q^∗ν,e). \widehat q_*\mathcal{R}(\nu,e)=\mathcal{R}_S(\widehat q_*\nu,e).

Proof.

The first condition ensures that selection weights depend only on the coarse semantic state and the coarse population, so semantic pushforward commutes with selection. The second ensures that pushforward commutes with transition. Composing these two commutative operations gives the stated equation.

31 Failure of semantic closure

If two joint populations satisfy q^∗ν=q^∗ρ\widehat q_*\nu=\widehat q_*\rho but

q^∗R(ν)≠q^∗R(ρ), \widehat q_*\mathcal{R}(\nu)\neq\widehat q_*\mathcal{R}(\rho),

then no autonomous first-order semantic dynamics exists on P(ΩS)\mathbb{P}(\Omega_S). The observer has discarded information needed to predict semantic evolution.

32 Architecture as hidden state

A common source of nonclosure is failure to retain architecture. Suppose two agents currently assign the same semantic class to a meme, qc(m)≃qd(n)q_c(m)\simeq q_d(n). If cc and dd induce different future distinctions or transition laws, then the current semantic class alone is not sufficient to predict the next semantic state. Architecture functions as a hidden dynamical variable.

The same current semantic state need not have the same future semantic dynamics.

33 Minimal sufficient semantic state

One may seek an augmented quotient r:ΩM→Zr:\Omega_M\to Z such that the intended semantic observable factors through rr and the dynamics is lumpable through rr. Then ZZ is a dynamically sufficient semantic state object:

P(ΩM)⟶P(Z)⟶P(Z) \mathbb{P}(\Omega_M)\longrightarrow\mathbb{P}(Z)\longrightarrow\mathbb{P}(Z)

closes. This generalizes the earlier principle: add hidden state until the dynamics becomes Markovian.

34 Finite architecture–meme model

Let cognitive architectures be indexed by α=1,…,r\alpha=1,\ldots,r and memetic states by i=1,…,ni=1,\ldots,n. Let xαix_{\alpha i} be the population fraction in the joint state (α,i)(\alpha,i). Then

xαi≥0,∑α,ixαi=1. x_{\alpha i}\geq 0,\qquad\sum_{\alpha,i}x_{\alpha i}=1.

35 Joint fitness matrix

Let wαi(x)>0w_{\alpha i}(x)>0 be endogenous fitness, and let Qαi,βj(x)Q_{\alpha i,\beta j}(x) be the probability that a reproducing state (α,i)(\alpha,i) produces successor state (β,j)(\beta,j). Thus

Qαi,βj≥0,∑β,jQαi,βj=1. Q_{\alpha i,\beta j}\geq 0,\qquad\sum_{\beta,j}Q_{\alpha i,\beta j}=1.

36 Finite coevolutionary replicator–mutator equation

Theorem 36.1 Finite joint update

The joint update is

xβj′=∑α,ixαiwαi(x)Qαi,βj(x)∑α,ixαiwαi(x). x'_{\beta j} = \frac{\displaystyle\sum_{\alpha,i}x_{\alpha i}w_{\alpha i}(x)Q_{\alpha i,\beta j}(x)}{\displaystyle\sum_{\alpha,i}x_{\alpha i}w_{\alpha i}(x)}.

Proof.

This is the finite-state expression of Definition 16.1 on the state space {1,…,r}×{1,…,n}\{1,\ldots,r\}\times\{1,\ldots,n\}.

37 Architecture marginal in the finite theory

Define aβ=∑jxβja_\beta=\sum_j x_{\beta j}. Then

aβ′=∑α,ixαiwαi(∑jQαi,βj)wˉ. a'_\beta = \frac{\displaystyle\sum_{\alpha,i}x_{\alpha i}w_{\alpha i}\Bigl(\sum_j Q_{\alpha i,\beta j}\Bigr)}{\bar w}.

Architecture frequencies change by both differential fitness and transition into new architectures.

38 Meme marginal

If the same meme label ii is meaningful across architectures, define mj=∑βxβjm_j=\sum_\beta x_{\beta j}. Then

mj′=∑α,ixαiwαi(∑βQαi,βj)wˉ. m'_j = \frac{\displaystyle\sum_{\alpha,i}x_{\alpha i}w_{\alpha i}\Bigl(\sum_\beta Q_{\alpha i,\beta j}\Bigr)}{\bar w}.

Neither marginal generally closes independently.

39 Marginal nonclosure theorem

Theorem 39.1 Marginals do not close

The architecture marginal ata_t generally does not determine at+1a_{t+1}, and the meme marginal mtm_t generally does not determine mt+1m_{t+1}.

Proof.

The formulas above depend on the full joint quantities xαix_{\alpha i}. Two joint distributions can have the same architecture marginal but different conditional meme distributions P(i∣α)P(i\mid\alpha). If fitness or transition depends on ii, they produce different architecture updates. The dual argument applies to the meme marginal.

Architecture and meme must generally be modeled jointly.

40 Continuous-time coevolution

Let z=(α,i)z=(\alpha,i) index joint states. Let fz(x)f_z(x) be Malthusian fitness and Lzz′(x)L_{zz'}(x) a population-dependent Markov generator on joint states. Then

x˙z′=xz′(fz′(x)−fˉ(x))+∑zxzLzz′(x). \dot x_{z'}=x_{z'}\bigl(f_{z'}(x)-\bar f(x)\bigr)+\sum_z x_z L_{zz'}(x).

Equivalently,

x˙βj=xβj(fβj(x)−fˉ(x))+∑α,ixαiLαi,βj(x). \dot x_{\beta j} = x_{\beta j}\bigl(f_{\beta j}(x)-\bar f(x)\bigr) + \sum_{\alpha,i}x_{\alpha i}L_{\alpha i,\beta j}(x).

41 Conservation theorem

Theorem 41.1 Conservation of total probability

If ∑z′Lzz′(x)=0\sum_{z'}L_{zz'}(x)=0, then ∑zxz=1\sum_z x_z=1 is preserved.

Proof.

Summing the replicator term gives fˉ−fˉ=0\bar f-\bar f=0. Summing the transition term gives

∑z′∑zxzLzz′(x)=∑zxz∑z′Lzz′(x)=0. \sum_{z'}\sum_z x_z L_{zz'}(x)=\sum_z x_z\sum_{z'}L_{zz'}(x)=0.

Hence ddt∑zxz=0\tfrac{d}{dt}\sum_z x_z=0.

42 Decomposition of architecture change

Let ϕ:ΩM→R\phi:\Omega_M\to\mathbb{R} be any architecture observable depending only on cc. The generalized Price equation gives

ΔE[ϕ]=Cov⁡(w,ϕ)wˉ+E[w(Kϕ−ϕ)]wˉ. \Delta\mathbb{E}[\phi] = \frac{\operatorname{Cov}(w,\phi)}{\bar w} + \frac{\mathbb{E}\bigl[w(K\phi-\phi)\bigr]}{\bar w}.

Architecture change decomposes into

Differential reproduction of architectures, plus within-lineage cognitive transformation.

43 Decomposition of semantic change

For a semantic observable ψ:ΩS→R\psi:\Omega_S\to\mathbb{R}, pull it back along q^\widehat q and apply the same identity:

ΔE[ψ∘q^]=Cov⁡(w,ψ∘q^)wˉ+E[w(K(ψ∘q^)−ψ∘q^)]wˉ. \Delta\mathbb{E}[\psi\circ\widehat q] = \frac{\operatorname{Cov}(w,\psi\circ\widehat q)}{\bar w} + \frac{\mathbb{E}\bigl[w\bigl(K(\psi\circ\widehat q)-\psi\circ\widehat q\bigr)\bigr]}{\bar w}.

The second term now includes both ordinary memetic mutation and changes caused by altered cognitive architecture.

44 Semantic mutation versus architectural reinterpretation

A change in semantic class may occur in at least two distinct ways.

Memetic mutation. (c,m)⟶(c,m′)(c,m)\longrightarrow(c,m') with qc(m)≄qc(m′)q_c(m)\not\simeq q_c(m').

Cognitive reinterpretation. (c,m)⟶(d,Tcdm)(c,m)\longrightarrow(d,T_{cd}m) with qc(m)≄qd(Tcdm)q_c(m)\not\simeq q_d(T_{cd}m), despite preservation of the underlying presentation. Semantic change need not require a change in the presentation itself.

45 Semantic conversion by architecture change

Definition 45.1 Pure reinterpretation event

A transition is a pure reinterpretation event if the presentation is transported without substantive alteration but its semantic class changes:

m′≃Tcdmwhile[m]c≠[m′]d. m'\simeq T_{cd}m\qquad\text{while}\qquad[m]_c\neq[m']_d.

This is impossible in a theory with a fixed semantic quotient, but natural in the present framework.

46 Memetic niche construction

A population can alter the cognitive architectures that determine its future fitness landscape. This is a semantic analogue of niche construction. The loop is

νt→Aνt→at+1→Lat+1→Qt+1→wt+1→νt+1. \nu_t\to A_{\nu_t}\to a_{t+1}\to L_{a_{t+1}}\to Q_{t+1}\to w_{t+1}\to\nu_{t+1}.

Selection acts on a landscape partly constructed by earlier memetic evolution.

47 Semantic niche

Definition 47.1 Semantic niche

The semantic niche of a joint population ν\nu is the architecture-weighted family of modalities

{Lc:c∈supp⁡(π∗ν)}, \bigl\{L_c:c\in\operatorname{supp}(\pi_*\nu)\bigr\},

together with their frequencies and translation structure. A change in architecture distribution changes the semantic niche.

48 Reflexive selection

Definition 48.1 Reflexive selection

Selection is reflexive when present memetic states influence future cognitive modalities in a manner that changes their own or their descendants’ future fitness. Schematically,

m→L′→[m]L′→w′(m). m\to L'\to[m]_{L'}\to w'(m).

This creates a higher-order feedback loop:

State, observer, classification, fitness, and then state again.

49 Fixed semantic theory as a special case

Suppose A(c,m)=δcA(c,m)=\delta_c for every (c,m)(c,m). Then architecture never changes. If the initial population is concentrated on one architecture c0c_0, then νt\nu_t remains supported on M(c0)M(c_0). The theory reduces to the endogenous memetic evolutionary system of Note V on the fixed semantic state space M(c0)M(c_0).

50 Architecture-only evolution as a special case

If every fiber M(c)M(c) is contractible, then no nontrivial memetic state remains. The total state space reduces to C≃\mathcal{C}^{\simeq}, and the theory becomes ordinary evolutionary dynamics on cognitive architectures. The coupled theory contains both meme evolution and architecture evolution as limiting cases.

51 Joint stability

Let F(ν)=R(ν,e)F(\nu)=\mathcal{R}(\nu,e). At equilibrium F(ν∗)=ν∗F(\nu_*)=\nu_*, perturbations satisfy

δνt+1=DFν∗ δνt. \delta\nu_{t+1}=DF_{\nu_*}\,\delta\nu_t.

Unlike fixed-architecture dynamics, the derivative contains architecture–meme cross terms. Schematically,

DFν∗=(Dmeme→memeDarch→memeDmeme→archDarch→arch). DF_{\nu_*} = \begin{pmatrix} D_{\mathrm{meme}\to\mathrm{meme}} & D_{\mathrm{arch}\to\mathrm{meme}} \\ D_{\mathrm{meme}\to\mathrm{arch}} & D_{\mathrm{arch}\to\mathrm{arch}} \end{pmatrix}.

The off-diagonal blocks encode the coevolutionary feedback.

52 Coevolutionary instability

Even if the meme subsystem and the architecture subsystem would each be stable when considered separately, the coupled system can be unstable because of the off-diagonal interaction terms. For a linearized block system

(ABCD)(ma), \begin{pmatrix} A & B \\ C & D \end{pmatrix} \begin{pmatrix} m \\ a \end{pmatrix},

stability depends on the spectrum of the full block operator, not merely the spectra of AA and DD. Feedback m→a→mm\to a\to m can create new unstable modes.

53 Higher-order cognitive evolution

Architecture itself may be represented by a hierarchy

c(0),c(1),c(2), … c^{(0)},\quad c^{(1)},\quad c^{(2)},\ \ldots

where c(1)c^{(1)} controls transformations of c(0)c^{(0)}, c(2)c^{(2)} controls transformations of c(1)c^{(1)}, and so forth. One may encode this by an iterated fibration Cn+1→Cn\mathcal{C}_{n+1}\to\mathcal{C}_n. The total evolutionary state then lies in the iterated Grothendieck construction

∫Cn⋯∫C1M. \int_{\mathcal{C}_n}\cdots\int_{\mathcal{C}_1}M.

This is a formal route toward evolution of meta-cognitive architecture.

54 Self-modeling

Suppose some memetic states encode aspects of the architecture cc itself. Then there exists a representation map

rc:M(c)→Rep⁡(c), r_c:M(c)\to\operatorname{Rep}(c),

where Rep⁡(c)\operatorname{Rep}(c) is an object of internal models of architecture cc. A state may influence architecture through an internally represented model:

m→rc(m)→c′→M(c′). m\to r_c(m)\to c'\to M(c').

This is the natural categorical setting for self-model-mediated cognitive modification.

55 Self-referential semantic dynamics

The coupled structure may now take the form

M(c)→Rep⁡(c)→C→M(c′)→Rep⁡(c′)→⋯ . M(c)\to\operatorname{Rep}(c)\to\mathcal{C}\to M(c')\to\operatorname{Rep}(c')\to\cdots.

The state modifies the interpreter, and the modified interpreter changes the meaning assigned to later states. This should be distinguished from ordinary self-replication. It is semantic self-modification.

56 Main theorem

Theorem 56.1 Cognitive–memetic coevolution theorem

Let (C,J)(\mathcal{C},J) be a cognitive ∞-site, M:Cop→SM:\mathcal{C}^{\mathrm{op}}\to\mathcal{S} a memetic field, ΩM=(∫CM)≃\Omega_M=\bigl(\int_{\mathcal{C}}M\bigr)^{\simeq}, and P\mathbb{P} a compatible probability monad. Suppose given

w:ΩM×P(ΩM)×E→R>0 w:\Omega_M\times\mathbb{P}(\Omega_M)\times E\to\mathbb{R}_{>0}

and

K:ΩM×P(ΩM)×E→P(ΩM). K:\Omega_M\times\mathbb{P}(\Omega_M)\times E\to\mathbb{P}(\Omega_M).

Then:

Proof.

Statements (i)–(x) follow from Sections 16–18, Theorem 19.1, Theorem 39.1, Definitions 8.2 and 22.1, Sections 5–7, Theorem 25.1, Theorem 30.1, Section 31, and Section 49.

57 Full formal specification

The coevolutionary theory is specified by

(C, J, M, L, ΩM, P, E, w, K). (\mathcal{C},\,J,\,M,\,L,\,\Omega_M,\,\mathbb{P},\,E,\,w,\,K).

The population equation is

νt+1=Lνt,etνtLνt,etνt(ΩM). \nu_{t+1} = \frac{\mathcal{L}_{\nu_t,e_t}\nu_t}{\mathcal{L}_{\nu_t,e_t}\nu_t(\Omega_M)}.

The semantic state of an individual is qct(mt)q_{c_t}(m_t). The architecture marginal is π∗νt\pi_*\nu_t.

58 Conceptual hierarchy

The complete framework developed through the successive extensions is now

memetic presentations↓capability-relative equivalence↓architecture-indexed sheaf semantics↓operadic recombination↓network and feedback dynamics↓stochastic replication↓frequency-dependent selection↓coevolution of memes and cognitive architectures. \begin{array}{c} \text{memetic presentations} \\ \downarrow \\ \text{capability-relative equivalence} \\ \downarrow \\ \text{architecture-indexed sheaf semantics} \\ \downarrow \\ \text{operadic recombination} \\ \downarrow \\ \text{network and feedback dynamics} \\ \downarrow \\ \text{stochastic replication} \\ \downarrow \\ \text{frequency-dependent selection} \\ \downarrow \\ \text{coevolution of memes and cognitive architectures.} \end{array}

The final step changes the logical status of semantic identity itself. Previously ≃c\simeq_c was fixed while states evolved. Now c=ctc=c_t also evolves, so ≃ct\simeq_{c_t} is time dependent.

59 Final principle

The strongest formulation is therefore not that memes evolve inside cognition, but that

Memes and the cognitive semantics that classify them coevolve.

A complete state is (c,m)(c,m), not merely mm. Its semantic interpretation is qc(m)q_c(m). Its reproduction may produce (c′,m′)(c',m'), and therefore change both m↦m′m\mapsto m' and qc↦qc′q_c\mapsto q_{c'}.

The full causal loop is

(ct,mt)⟶(ct+1,mt+1)⟶qct+1(mt+1)⟶wt+1⟶(ct+2,mt+2). (c_t,m_t) \longrightarrow (c_{t+1},m_{t+1}) \longrightarrow q_{c_{t+1}}(m_{t+1}) \longrightarrow w_{t+1} \longrightarrow (c_{t+2},m_{t+2}).

Memetic replication is evaluated relative to a semantic modality that can itself be modified by the replicating population. The resulting theory is a dynamics not merely of states, but of states together with their changing equivalence relations: dynamics of objects, dynamics of observers, and dynamics of identity.

Part 7: Evolution of the Semantic Universe

Abstract

We extend capability-relative memetic replication beyond evolution inside a fixed cognitive site. The category of cognitive architectures, its translation morphisms, and its Grothendieck topology are now themselves dynamical.

At time tt, let

Σt=(Ct,Jt) \Sigma_t=(\mathcal{C}_t,J_t)

be a cognitive ∞-site and let

Et=Sh⁡∞(Ct,Jt) \mathcal{E}_t=\operatorname{Sh}_{\infty}(\mathcal{C}_t,J_t)

be its semantic ∞-topos. A transition of semantic universes is a left-exact colimit-preserving functor

Ft,t′:Et⟶Et′, F_{t,t'}:\mathcal{E}_t\longrightarrow\mathcal{E}_{t'},

or, more generally, a correspondence of ∞-topoi. Memetic objects are sections of an indexed family of semantic universes rather than objects of one fixed ∞-topos.

We distinguish four phenomena: change of site presentation without semantic change; refinement of the Grothendieck topology; creation or deletion of cognitive architectures and translations; and genuine change of the semantic ∞-topos. Left-exact transport preserves identity witnesses, conservative transport preserves and reflects semantic identity, refinement of a topology induces a left-exact localization, and equivalent site presentations yield equivalent semantic theories.

Stochastic evolution across changing semantic spaces uses a variable-space mutation kernel

Kt:St⟶Pt+1(St+1), K_t:S_t\longrightarrow\mathbb{P}_{t+1}(S_{t+1}),

and the non-autonomous replicator–mutator equation

νt+1=(Kt)∗(wtνt∫Stwt dνt). \nu_{t+1} = (K_t)_* \left( \frac{w_t\nu_t}{\displaystyle\int_{S_t}w_t\,d\nu_t} \right).

Finally, the semantic universe itself may be selected or transformed by its resident memes. A total state is a point of a Grothendieck construction over an ∞-category of admissible semantic universes. The resulting hierarchy is

State evolution, inside observer evolution, inside identity evolution, inside semantic-universe evolution.

The preceding fixed-universe theory is Note VI.

Notation

1 Why the site must itself become dynamical

In the preceding theory, one fixed cognitive site (C,J)(\mathcal{C},J) determined one semantic universe Sh⁡∞(C,J)\operatorname{Sh}_{\infty}(\mathcal{C},J). Cognitive architectures could evolve inside C\mathcal{C}, but three things were held fixed: the objects of C\mathcal{C}, the admissible translations Map⁡C(c,d)\operatorname{Map}_{\mathcal{C}}(c,d), and the Grothendieck topology JJ. We now allow all three to vary. At time tt, write

Σt=(Ct,Jt). \Sigma_t=(\mathcal{C}_t,J_t).

Semantic evolution can alter objects, morphisms, covers, and sheaves, and therefore the ambient notion of semantic object itself.

2 Time as an indexing ∞-category

Let T\mathcal{T} be an ∞-category of times or evolutionary stages. For discrete time one may take T=N\mathcal{T}=\mathbb{N}, regarded as a category with a unique arrow n→mn\to m whenever n≤mn\leq m. More general choices permit branching timelines, partially ordered stages, or higher homotopies between evolutionary histories.

3 Dynamical cognitive sites

Definition 3.1 Time-indexed cognitive site

A time-indexed cognitive site is a family

(Ct,Jt),t∈T. (\mathcal{C}_t,J_t),\qquad t\in\mathcal{T}.

The associated semantic universe is

Et=Sh⁡∞(Ct,Jt). \mathcal{E}_t=\operatorname{Sh}_{\infty}(\mathcal{C}_t,J_t).

A raw family is not yet a dynamics. Transitions between semantic universes must still be specified.

4 Invariant semantic-universe transitions

Because a single ∞-topos can have many inequivalent-looking site presentations, the evolution of sites should be formulated at the level of ∞-topoi.

Definition 4.1 Forward semantic transport

A forward semantic transport t→t′t\to t' is a colimit-preserving left-exact functor

Ft,t′:Et⟶Et′. F_{t,t'}:\mathcal{E}_t\longrightarrow\mathcal{E}_{t'}.

Left exactness means that Ft,t′F_{t,t'} preserves finite limits. Such a functor may be regarded as the inverse-image part of a geometric morphism, with the displayed direction following evolutionary time.

Definition 4.2 Semantic-universe evolution

A semantic-universe evolution is a functorial assignment

E:T⟶PrlexL \mathscr{E}:\mathcal{T}\longrightarrow\mathbf{Pr}^{L}_{\mathrm{lex}}

with E(t)=Et\mathscr{E}(t)=\mathcal{E}_t an ∞-topos. For a composable pair t→t′→t′′t\to t'\to t'',

Ft′,t′′∘Ft,t′≃Ft,t′′. F_{t',t''}\circ F_{t,t'}\simeq F_{t,t''}.

5 Dynamic semantic objects

A memetic object can no longer be one fixed M∈EM\in\mathcal{E}. It must vary with the semantic universe.

Definition 5.1 Dynamical semantic object

A dynamical semantic object consists of objects Mt∈EtM_t\in\mathcal{E}_t together with coherent transition morphisms

θt,t′:Ft,t′Mt⟶Mt′. \theta_{t,t'}:F_{t,t'}M_t\longrightarrow M_{t'}.

If every θt,t′\theta_{t,t'} is an equivalence, the family is transport-cartesian. The family t↦Ett\mapsto\mathcal{E}_t determines an indexed ∞-category

p:∫TE⟶T. p:\int_{\mathcal{T}}\mathscr{E}\longrightarrow\mathcal{T}.

A transport-cartesian semantic object is a section of this fibration.

6 Identity transport

Let x,y:1→Xx,y:1\to X be points of X∈EtX\in\mathcal{E}_t. Their identity object is the pullback

Id⁡X(x,y)≃1×X×XX, \operatorname{Id}_X(x,y)\simeq 1\times_{X\times X}X,

along the diagonal.

Theorem 6.1 Left-exact transport preserves identity objects

Let F:Et→Et′F:\mathcal{E}_t\to\mathcal{E}_{t'} be left exact. Then

FId⁡X(x,y)≃Id⁡FX(Fx,Fy). F\operatorname{Id}_X(x,y)\simeq\operatorname{Id}_{FX}(Fx,Fy).

Proof.

The identity object is a finite-limit construction from the terminal object, the product X×XX\times X, and the diagonal. Since FF preserves terminal objects, products, and pullbacks,

F(1×X×XX)≃1×FX×FXFX, F\bigl(1\times_{X\times X}X\bigr)\simeq 1\times_{FX\times FX}FX,

which is Id⁡FX(Fx,Fy)\operatorname{Id}_{FX}(Fx,Fy).

Corollary 6.2 Forward persistence of equivalence

If x≃yx\simeq y in Et\mathcal{E}_t, then Fx≃FyFx\simeq Fy in Et′\mathcal{E}_{t'}. Legitimate semantic transport cannot destroy an already supplied identity witness. It may, however, identify distinctions that were previously visible.

7 Conservative evolution

Definition 7.1 Conservative semantic transport

A functor F:E→FF:\mathcal{E}\to\mathcal{F} is conservative if F(f)F(f) an equivalence implies that ff is an equivalence.

Theorem 7.2 Conservative transport preserves and reflects identity

If FF is left exact and conservative, then

x≃y  ⟺  Fx≃Fy. x\simeq y \iff Fx\simeq Fy.

Proof.

The forward implication is Corollary 6.2. Conversely, if Fx≃FyFx\simeq Fy, then the image of the identity object is inhabited. By Theorem 6.1,

FId⁡X(x,y)≃Id⁡FX(Fx,Fy). F\operatorname{Id}_X(x,y)\simeq\operatorname{Id}_{FX}(Fx,Fy).

Conservativity reflects the corresponding path. Hence x≃yx\simeq y.

Failure of conservativity is the mechanism by which semantic distinctions disappear.

8 Presentation change versus genuine universe change

A different site presentation does not necessarily produce a different semantic universe.

Definition 8.1 Presentational change

A transition (C,J)⇝(D,K)(\mathcal{C},J)\rightsquigarrow(\mathcal{D},K) is merely presentational if

Sh⁡∞(C,J)≃Sh⁡∞(D,K). \operatorname{Sh}_{\infty}(\mathcal{C},J)\simeq\operatorname{Sh}_{\infty}(\mathcal{D},K).

Definition 8.2 Genuine semantic-universe change

A site transition is genuine when the associated ∞-topoi are not equivalent.

Theorem 8.3 Invariance under equivalent site presentation

Suppose Φ\Phi is an equivalence of the two sheaf ∞-topoi. Then every semantic construction expressible internally using finite limits, colimits, mapping objects, identity types, and equivalence-invariant structure transports uniquely up to equivalence across Φ\Phi.

Proof.

An equivalence of ∞-categories preserves and reflects limits, colimits, mapping spaces, and equivalences. Constructions made from these are invariant.

Changing coordinates on the semantic universe is not the same as changing the semantic universe.

9 Evolution of the Grothendieck topology

One way to change a semantic universe without changing the architecture category is to modify which families count as covers. Let J⊆J′J\subseteq J' be Grothendieck topologies on the same ∞-category C\mathcal{C}, with J′J' finer than JJ.

Theorem 9.1 Topology refinement induces a left-exact localization

There is a reflective inclusion

Sh⁡∞(C,J′)↪Sh⁡∞(C,J) \operatorname{Sh}_{\infty}(\mathcal{C},J')\hookrightarrow\operatorname{Sh}_{\infty}(\mathcal{C},J)

whose left adjoint is J′J'-sheafification

LJ′:Sh⁡∞(C,J)→Sh⁡∞(C,J′). L_{J'}:\operatorname{Sh}_{\infty}(\mathcal{C},J)\to\operatorname{Sh}_{\infty}(\mathcal{C},J').

Moreover LJ′L_{J'} is left exact.

Proof.

Every J′J'-sheaf satisfies the descent conditions of JJ, since J⊆J′J\subseteq J'. The inclusion is fully faithful, and sheafification supplies the reflector. Sheafification of an ∞-topos is an accessible left-exact localization.

10 Interpretation of topology refinement

Adding covers imposes additional local-to-global compatibility equations. Thus

J⊆J′ J\subseteq J'

produces

EJ→LJ′EJ′. \mathcal{E}_J\xrightarrow{L_{J'}}\mathcal{E}_{J'}.

A semantic object that fails the new descent conditions is replaced by its closest J′J'-local approximation. Evolution of JJ can change which distributed representations count as legitimate global semantic objects.

11 Descent constraints as semantic laws

Suppose {Ui→U}\{U_i\to U\} becomes a cover only after the transition J⇝J′J\rightsquigarrow J'. Then a formerly unconstrained semantic object XX must after refinement satisfy

X(U)≃Tot⁡X(U∙). X(U)\simeq\operatorname{Tot} X(U_{\bullet}).

Evolution of the topology can create new equations among local cognitive perspectives.

New coverage imposes new semantic coherence laws.

12 Creation of new architectures

Changing C\mathcal{C} modifies the possible observer types. Suppose i:Ct→Ct+1i:\mathcal{C}_t\to\mathcal{C}_{t+1} describes surviving architectures.

Definition 12.1 Architectural birth

An object d∈Ct+1d\in\mathcal{C}_{t+1} is new relative to ii if it is not equivalent to any object in the essential image of ii:

d∉EssIm⁡(i). d\notin\operatorname{EssIm}(i).

New architectures can enlarge the space of possible semantic resolutions.

13 New translation morphisms

Even if the object set does not materially change, new morphisms may appear. Suppose cc and dd persist. If

Map⁡Ct(c,d)→Map⁡Ct+1(c,d) \operatorname{Map}_{\mathcal{C}_t}(c,d)\to\operatorname{Map}_{\mathcal{C}_{t+1}}(c,d)

fails to be essentially surjective, then new translation procedures exist at time t+1t+1. Semantic-universe evolution can create new architectures, or merely new ways of translating among old ones. The latter matters because descent depends on morphisms, not only objects.

14 Why ordinary functors do not model deletion symmetrically

A forward functor Ct→Ct+1\mathcal{C}_t\to\mathcal{C}_{t+1} models persistence and the creation of new target objects, but it does not symmetrically encode architectures that simply cease to participate. The more general formalism is a correspondence.

15 Site-transition correspondences

Definition 15.1 Architectural correspondence

A transition between architecture categories is a span

Ct←pRt→qCt+1. \mathcal{C}_t\xleftarrow{p}\mathcal{R}_t\xrightarrow{q}\mathcal{C}_{t+1}.

The intermediate category Rt\mathcal{R}_t represents architecture continuations. An architecture c∈Ctc\in\mathcal{C}_t is represented across the transition only if it lies in the essential image of pp, and a later architecture has a predecessor only if it lies in the essential image of qq.

Definition 15.2 Architectural death

An architecture c∈Ctc\in\mathcal{C}_t dies relative to the correspondence when c∉EssIm⁡(p)c\notin\operatorname{EssIm}(p).

Definition 15.3 Architectural birth, symmetrically

An architecture d∈Ct+1d\in\mathcal{C}_{t+1} is born when d∉EssIm⁡(q)d\notin\operatorname{EssIm}(q). Creation and deletion are then symmetric.

16 Semantic-universe correspondences

The same construction lifts from sites to their ∞-topoi. A general semantic transition may be

Et←PXt→QEt+1. \mathcal{E}_t\xleftarrow{P}\mathcal{X}_t\xrightarrow{Q}\mathcal{E}_{t+1}.

This is an object of a correspondence (∞,2)(\infty,2)-category of ∞-topoi. Such correspondences are useful when there is no preferred global functor Et→Et+1\mathcal{E}_t\to\mathcal{E}_{t+1}. They represent partial translation, shared semantic subuniverses, or many-to-many reconstruction.

17 Composition of semantic correspondences

Given spans through E1\mathcal{E}_1, their composite is the homotopy pullback

X02=X01×E1X12. \mathcal{X}_{02}=\mathcal{X}_{01}\times_{\mathcal{E}_1}\mathcal{X}_{12}.

This produces a span from E0\mathcal{E}_0 to E2\mathcal{E}_2. Associativity holds up to the coherent equivalence of homotopy pullbacks. Semantic-universe histories live in a higher category of correspondences.

18 Dynamic descent

Suppose Mt∈EtM_t\in\mathcal{E}_t satisfies descent for a cover U∙,t→UtU_{\bullet,t}\to U_t, and Ft,t′F_{t,t'} is left exact and colimit-preserving. If the image diagram remains a cover, descent transports.

Theorem 18.1 Transport of descent

Assume

Mt(Ut)≃Tot⁡Mt(U∙,t). M_t(U_t)\simeq\operatorname{Tot} M_t(U_{\bullet,t}).

Then

Ft,t′Mt(Ut)≃Tot⁡Ft,t′Mt(U∙,t), F_{t,t'}M_t(U_t)\simeq\operatorname{Tot} F_{t,t'}M_t(U_{\bullet,t}),

provided Ft,t′F_{t,t'} preserves the relevant totalization.

Proof.

Apply Ft,t′F_{t,t'} to the descent equivalence. If it preserves the limit defining the totalization, it passes inside Tot⁡\operatorname{Tot}.

For finite Čech descent, left exactness suffices for the finite-limit stages. Hyperdescent may require stronger limit preservation.

19 Semantic novelty

Definition 19.1 Semantic novelty

Let F:Et→Et+1F:\mathcal{E}_t\to\mathcal{E}_{t+1} be semantic transport. An object X∈Et+1X\in\mathcal{E}_{t+1} is novel relative to FF if

X∉EssIm⁡(F). X\notin\operatorname{EssIm}(F).

This is stronger than ordinary memetic novelty. Ordinary novelty is a new point inside the same universe. Semantic-universe novelty is an object not equivalent to the transport of any earlier semantic object.

20 Novel distinctions

A later universe may possess observables unavailable earlier. Let X,Y∈EtX,Y\in\mathcal{E}_t. Suppose their images are distinguished by a new observable

q:F(X)⨿F(Y)→Z q:F(X)\amalg F(Y)\to Z

that does not descend from any corresponding observable at time tt. The later universe has acquired a new semantic distinction: a structural increase in expressive power, not merely a new data value.

21 Semantic collapse

Definition 21.1 Semantic collapse

A semantic transport F:Et→Et+1F:\mathcal{E}_t\to\mathcal{E}_{t+1} exhibits collapse if it is not conservative. Equivalently, there exists a non-equivalence f:X→Yf:X\to Y such that F(f)F(f) is an equivalence. The transition is a loss of semantic distinctions.

22 Semantic phase transition

Definition 22.1 Semantic phase transition

A transition Et→Et+1\mathcal{E}_t\to\mathcal{E}_{t+1} is a semantic phase transition when at least one of the following occurs:

The terminology is structural. It denotes a change of semantic organization, not a thermodynamic claim.

23 Dynamic identity

Let Mt∈EtM_t\in\mathcal{E}_t be a dynamical memetic object and let xt,yt∈Mtx_t,y_t\in M_t. The identity space Id⁡Mt(xt,yt)\operatorname{Id}_{M_t}(x_t,y_t) now varies in a varying ∞-topos.

Theorem 23.1 Identity persistence under conservative universe evolution

Suppose every transition Ft,t+1F_{t,t+1} is left exact and conservative, and the memetic states are transported compatibly. Then xt≃ytx_t\simeq y_t if and only if xt+1≃yt+1x_{t+1}\simeq y_{t+1}.

Proof.

Left exactness preserves identity objects by Theorem 6.1. Conservativity reflects equivalence by Theorem 7.2. Compatibility identifies xt+1≃Ft,t+1xtx_{t+1}\simeq F_{t,t+1}x_t and yt+1≃Ft,t+1yty_{t+1}\simeq F_{t,t+1}y_t.

Corollary 23.2 When identity can change

A change in whether two persistent states count as identical requires a nonconservative semantic transition, a failure of compatible transport, or a change of the semantic objects being compared.

24 Variable semantic state spaces

Let St∈EtS_t\in\mathcal{E}_t be the observer-relative semantic state object at time tt. A population is

νt∈Pt(St), \nu_t\in\mathbb{P}_t(S_t),

where the probability structure may now vary with the semantic universe.

25 Compatible probability structure

Definition 25.1 Dynamic probability monad

A dynamic probability structure consists of probability monads

Pt:Et→Et \mathbb{P}_t:\mathcal{E}_t\to\mathcal{E}_t

and coherent comparison transformations

Ft,t′Pt⟹Pt′Ft,t′. F_{t,t'}\mathbb{P}_t\Longrightarrow\mathbb{P}_{t'}F_{t,t'}.

When these are equivalences, probability transport commutes with semantic transport. The hypothesis is not automatic. It is additional structure.

26 Mutation across different semantic universes

Definition 26.1 Variable-space mutation kernel

A one-step stochastic semantic transition is a kernel

Kt:St⟶Pt+1(St+1). K_t:S_t\longrightarrow\mathbb{P}_{t+1}(S_{t+1}).

For s∈Sts\in S_t, the distribution Kt(s)K_t(s) lives on the next semantic universe. This is strictly more general than an endomorphism S→P(S)S\to\mathbb{P}(S).

27 Variable-space selection

Let wt:St→R>0w_t:S_t\to\mathbb{R}_{>0} be the current fitness field. Define

wˉt=∫Stwt(s) dνt(s). \bar w_t=\int_{S_t}w_t(s)\,d\nu_t(s).

Selection yields

dν~t(s)=wt(s)wˉt dνt(s). d\widetilde\nu_t(s)=\frac{w_t(s)}{\bar w_t}\,d\nu_t(s).

28 Non-autonomous replicator–mutator equation

Definition 28.1 Population update

The population update across changing semantic universes is

νt+1=(Kt)∗ν~t, \nu_{t+1}=(K_t)_*\widetilde\nu_t,

equivalently

νt+1=(Kt)∗(wtνt∫Stwt dνt). \nu_{t+1} = (K_t)_* \left( \frac{w_t\nu_t}{\displaystyle\int_{S_t}w_t\,d\nu_t} \right).

Theorem 28.2 Normalization

If νt\nu_t is a probability state and Kt(s)K_t(s) is normalized for every ss, then νt+1\nu_{t+1} is normalized.

Proof.

Selection preserves total mass, so ν~t(St)=1\widetilde\nu_t(S_t)=1. Then

νt+1(St+1)=∫StKt(s,St+1) dν~t(s)=∫St1 dν~t(s)=1. \begin{aligned} \nu_{t+1}(S_{t+1}) &= \int_{S_t} K_t(s,S_{t+1})\,d\widetilde\nu_t(s) \\ &= \int_{S_t} 1\,d\widetilde\nu_t(s) \\ &= 1. \end{aligned}

29 Non-autonomous transfer operators

Define

(Ltν)(A)=∫Stwt(s) Kt(s,A) dν(s). (\mathcal{L}_t\nu)(A)=\int_{S_t}w_t(s)\,K_t(s,A)\,d\nu(s).

Then Lt\mathcal{L}_t maps positive measures on StS_t to positive measures on St+1S_{t+1}. The normalized update is

νt+1=LtνtLtνt(St+1). \nu_{t+1}=\frac{\mathcal{L}_t\nu_t}{\mathcal{L}_t\nu_t(S_{t+1})}.

There is no single fixed transfer operator. One has a cocycle Lt+n−1⋯Lt\mathcal{L}_{t+n-1}\cdots\mathcal{L}_t.

30 Iterated variable-universe evolution

Proposition 30.1 Projective iteration

Ignoring intermediate normalization factors, the state after nn transitions is

νt+n=Lt+n−1⋯Ltνt(Lt+n−1⋯Ltνt)(St+n). \nu_{t+n} = \frac{\mathcal{L}_{t+n-1}\cdots\mathcal{L}_t\nu_t}{(\mathcal{L}_{t+n-1}\cdots\mathcal{L}_t\nu_t)(S_{t+n})}.

Proof.

The scalar normalization factors cancel under projectivization, as in the fixed-space theory. Induct on nn.

Evolutionary dynamics on changing semantic spaces is projective dynamics generated by a sequence of positive operators between different cones.

31 No fixed equilibrium in general

When StS_t changes with time, the equation ν∗=R(ν∗)\nu_*=\mathcal{R}(\nu_*) is not even type-correct unless the semantic universes repeat or are canonically identified. The appropriate invariant is not necessarily a fixed point.

Definition 31.1 Evolutionary coherent family

A family {νt}\{\nu_t\} is evolutionarily coherent if νt+1=Rt(νt)\nu_{t+1}=\mathcal{R}_t(\nu_t) for every tt. For periodic universe evolution one can seek periodic population sections. For stationary universe evolution, ordinary equilibria are recovered.

32 The space of semantic universes

Semantic-universe evolution can itself be endogenous. Let U\mathfrak{U} be an ∞-category of admissible cognitive semantic universes. A point u∈Uu\in\mathfrak{U} may encode (Cu,Ju,Eu)(\mathcal{C}_u,J_u,\mathcal{E}_u) together with the probability, modality, and memetic structures required.

33 Indexed semantic universes

Suppose we have a functor

E:U⟶PrlexL \mathscr{E}:\mathfrak{U}\longrightarrow\mathbf{Pr}^{L}_{\mathrm{lex}}

with u↦Euu\mapsto\mathcal{E}_u. Choose in each universe a semantic state object Su∈EuS_u\in\mathcal{E}_u. These form an indexed semantic family.

34 Total universe–semantic state space

Straightening determines a total ∞-category ∫US\int_{\mathfrak{U}}S. Define

Ω=(∫US)≃. \Omega=\Bigl(\int_{\mathfrak{U}}S\Bigr)^{\simeq}.

A point is a pair (u,s)(u,s), a semantic universe together with a semantic state inside that universe.

35 Universe projection

There is a projection

πU:Ω→U≃. \pi_{\mathfrak{U}}:\Omega\to\mathfrak{U}^{\simeq}.

Every population on Ω\Omega has a universe marginal πU∗ν\pi_{\mathfrak{U}*}\nu. The system can contain a distribution over different semantic universes.

36 Universe-changing kernels

Definition 36.1 Universe transition kernel

A stochastic universe transition is

A:Ω×P(Ω)×E⟶P(U≃). A:\Omega\times\mathbb{P}(\Omega)\times E\longrightarrow\mathbb{P}(\mathfrak{U}^{\simeq}).

The probability of moving to a new semantic universe can depend on the current universe, the current semantic state, the total population, and the external environment.

37 Full universe–semantic kernel

The invariant dynamics is represented by

K:Ω×P(Ω)×E⟶P(Ω). K:\Omega\times\mathbb{P}(\Omega)\times E\longrightarrow\mathbb{P}(\Omega).

Its universe marginal is P(πU)∘K\mathbb{P}(\pi_{\mathfrak{U}})\circ K. One transition can simultaneously change u↦u′u\mapsto u' and s↦s′∈Su′s\mapsto s'\in S_{u'}.

38 Universe-dependent fitness

Let

w:Ω×P(Ω)×E→R>0. w:\Omega\times\mathbb{P}(\Omega)\times E\to\mathbb{R}_{>0}.

Then w((u,s),ν,e)w((u,s),\nu,e) measures the reproductive value of state ss as represented inside universe uu.

39 Semantic-universe coevolution operator

Define

wˉ(ν,e)=∫Ωw(x,ν,e) dν(x). \bar w(\nu,e)=\int_{\Omega}w(x,\nu,e)\,d\nu(x).

Then

R(ν,e)=(Kν,e)∗(w(−,ν,e) νwˉ(ν,e)). \mathcal{R}(\nu,e) = (K_{\nu,e})_* \left( \frac{w(-,\nu,e)\,\nu}{\bar w(\nu,e)} \right).

This is formally the joint replicator–mutator operator, but the state coordinate now includes the semantic universe itself.

40 Universe selection

The marginal distribution on semantic universes evolves according to

a′(B)=∫Ωw(x,ν,e) Aν,e(x,B) dν(x)wˉ(ν,e). a'(B) = \frac{\displaystyle\int_{\Omega}w(x,\nu,e)\,A_{\nu,e}(x,B)\,d\nu(x)}{\bar w(\nu,e)}.

Semantic universes can spread, disappear, or transform under selection and transition.

41 Memes that alter their semantic universe

Definition 41.1 Universe-reflexive meme

A semantic state s∈Sus\in S_u is universe-reflexive if the distribution of future universes depends nontrivially on ss: there exist s,s′∈Sus,s'\in S_u with

A((u,s),−)≠A((u,s′),−). A((u,s),-)\neq A((u,s'),-).

Such a state influences not merely an observer architecture inside one semantic universe, but the future semantic universe itself.

42 Strong semantic reflexivity

Definition 42.1 Strongly universe-reflexive state

A state is strongly universe-reflexive if it can induce a transition u→u′u\to u' for which Eu≄Eu′\mathcal{E}_u\not\simeq\mathcal{E}_{u'}. Its propagation can change the ambient ∞-topos. The causal loop is

s→u′→Eu′→new semantic objects→new descendants. s\to u'\to\mathcal{E}_{u'}\to\text{new semantic objects}\to\text{new descendants}.

43 Evolution of the coverage relation

A particularly important universe-reflexive transition changes only JuJ_u while leaving the underlying architecture category fixed. Suppose Ju⊊Ju′J_u\subsetneq J_{u'}. Then Eu′\mathcal{E}_{u'} is a left-exact localization of Eu\mathcal{E}_u. A resident semantic population can therefore induce new descent requirements.

Memetic activity can change what counts as sufficient agreement among observers.

44 Evolution of translation structure

Another transition changes Map⁡Cu(c,d)\operatorname{Map}_{\mathcal{C}_u}(c,d). A newly created translation may cause previously disconnected semantic regions to participate in common descent diagrams. New communication capability can change global semantic structure even when local observer objects remain unchanged.

45 Emergent global semantic objects

Suppose at time tt the architectures c1c_1 and c2c_2 have no relevant common cover. At t+1t+1, new morphisms produce a cover

{c1→c, c2→c}. \{c_1\to c,\ c_2\to c\}.

Compatible local states x1∈M(c1)x_1\in M(c_1) and x2∈M(c2)x_2\in M(c_2) may now descend to a global object x∈M(c)x\in M(c). Evolution of the site can make a new distributed semantic object globally definable.

46 Loss of global semantic objects

Conversely, changes in topology or architecture may destroy the compatibility conditions required for a previous global representation. A formerly meaningful global section may cease to admit transport into the new semantic universe. Universe evolution can produce both semantic emergence and semantic extinction.

47 Semantic extinction

Definition 47.1 Semantic extinction

Let F:Et→Et+1F:\mathcal{E}_t\to\mathcal{E}_{t+1} be the available transport. A semantic object X∈EtX\in\mathcal{E}_t is extinct relative to the transition when no admissible successor in the specified dynamical family is equivalent to F(X)F(X), or when the transition is represented only by a correspondence whose support contains no continuation of XX. This is stronger than population extinction. The semantic type itself ceases to have a continuation.

48 Creation of semantic dimensions

A new architecture or observable may introduce a semantic coordinate unavailable at earlier times. Model this by a map St+1→StS_{t+1}\to S_t whose homotopy fibers are noncontractible. Then multiple new semantic states project to one old state.

Definition 48.1 Semantic dimensional refinement

A transition r:St+1→Str:S_{t+1}\to S_t is a strict semantic refinement if some homotopy fiber of rr contains more than one connected component. This is the universe-level analogue of capability refinement.

49 Creation of new homotopy structure

The refinement need not occur only at the level of connected components. A semantic state may acquire nontrivial π1\pi_1, π2\pi_2, and higher information. Universe evolution can alter the relevant homotopy nn-type of semantic objects, gaining or losing higher coherence.

50 Dynamic Postnikov resolution

A simple example is a time-dependent truncation level ntn_t. Let Et\mathcal{E}_t retain semantic information only through τ≤nt\tau_{\leq n_t}. If nt+1>ntn_{t+1}>n_t, new higher identity structure becomes visible. If nt+1<ntn_{t+1}<n_t, previously visible higher distinctions collapse. The capability hierarchy is a one-dimensional special case of evolving semantic universes.

51 Path dependence

When the universe itself changes, the final semantic state may depend on the path. Suppose one history is u0→u1→u3u_0\to u_1\to u_3 and another is u0→u2→u3u_0\to u_2\to u_3. The composites

F13∘F01andF23∘F02 F_{13}\circ F_{01}\qquad\text{and}\qquad F_{23}\circ F_{02}

need not be equivalent unless the evolution diagram contains a specified coherent 2-cell between them. Semantic evolution is generally path dependent.

52 Coherent path independence

Definition 52.1 Path coherence

A semantic-universe diagram is path coherent on a square when there is a specified equivalence

F13∘F01≃F23∘F02. F_{13}\circ F_{01}\simeq F_{23}\circ F_{02}.

Higher-dimensional diagrams require higher coherence data. This is another reason the natural ambient structure is higher categorical.

53 Hysteresis

If different paths through semantic-universe space produce non-equivalent transports between the same endpoints, the system exhibits semantic hysteresis:

Fγ≄Fγ′ F_{\gamma}\not\simeq F_{\gamma'}

for two paths γ,γ′\gamma,\gamma' from a common start to a common end vv. Semantic history cannot be reconstructed from the endpoint universe alone.

54 Universe-level hidden state

If an observer sees only a coarse semantic object sts_t but not the current semantic universe utu_t, the observed dynamics can be non-Markovian even when (ut,st)(u_t,s_t) is Markovian. The semantic universe can itself function as latent state.

55 Universe lumpability

Let q:Ω→Zq:\Omega\to Z be a coarse observable that forgets some or all universe information.

Definition 55.1 Universe lumpability

The full dynamics is lumpable through qq when there exists RZ:P(Z)→P(Z)\mathcal{R}_Z:\mathbb{P}(Z)\to\mathbb{P}(Z) such that

RZ∘q∗=q∗∘R. \mathcal{R}_Z\circ q_*=q_*\circ\mathcal{R}.

If this fails, the forgotten semantic-universe data affects future observable evolution.

56 Universe-level Price equation

Let ϕ:Ω→R\phi:\Omega\to\mathbb{R} be an observable of the semantic universe, such as a numerical invariant of complexity or of the number of architecture types. Then

ΔE[ϕ]=Cov⁡(w,ϕ)wˉ+E[w(Kϕ−ϕ)]wˉ. \Delta\mathbb{E}[\phi] = \frac{\operatorname{Cov}(w,\phi)}{\bar w} + \frac{\mathbb{E}\bigl[w(K\phi-\phi)\bigr]}{\bar w}.

The first term measures selection among semantic universes. The second measures direct transformation of semantic universes within lineages.

57 Endogenous generation of semantic universes

The kernel KK may assign positive probability to universe states u′u' not previously represented in the support of the population. Then

supp⁡(at+1)⊈supp⁡(at). \operatorname{supp}(a_{t+1})\not\subseteq\operatorname{supp}(a_t).

Genuinely new semantic universes can enter the evolutionary process.

58 Semantic innovation rate

Given the current support Ut=supp⁡(at)U_t=\operatorname{supp}(a_t), the probability of semantic-universe innovation is

at+1(U≃∖Ut). a_{t+1}\bigl(\mathfrak{U}^{\simeq}\setminus U_t\bigr).

In continuous settings this requires a measurable notion of previous support. The quantity measures population flow into previously unoccupied semantic universes.

59 Fixed-universe theory as a special case

Suppose the universe transition kernel is δu\delta_u. Then ut=u0u_t=u_0 almost surely, and the theory reduces to memetic and cognitive evolution inside the fixed ∞-topos Eu0\mathcal{E}_{u_0}.

60 Fixed-site but evolving architecture theory

If the semantic universe uu is fixed but the state object includes an architecture coordinate c∈Cuc\in\mathcal{C}_u, we recover the cognitive–memetic coevolution theory of Note VI. The inclusions of generality are

fixed memetic state space⊂evolving cognitive architecture⊂evolving semantic universe. \begin{array}{c} \text{fixed memetic state space} \\ \subset \\ \text{evolving cognitive architecture} \\ \subset \\ \text{evolving semantic universe.} \end{array}

61 Four distinct notions of change

The theory now distinguishes four categorically distinct phenomena.

62 Main semantic-universe evolution theorem

Theorem 62.1 Evolution of semantic universes

Let T\mathcal{T} be an evolutionary indexing ∞-category and let E:T→PrlexL\mathscr{E}:\mathcal{T}\to\mathbf{Pr}^{L}_{\mathrm{lex}} be a diagram of semantic ∞-topoi. Then:

Proof.

Statements (i)–(x) are established in Sections 5–21, as cited.

63 Main evolutionary theorem

Theorem 63.1 Variable-universe replicator–mutator theorem

Let St∈EtS_t\in\mathcal{E}_t be semantic state objects with compatible probability structures, wt:St→R>0w_t:S_t\to\mathbb{R}_{>0}, and Kt:St→Pt+1(St+1)K_t:S_t\to\mathbb{P}_{t+1}(S_{t+1}). Then

νt+1=(Kt)∗(wtνt∫Stwt dνt) \nu_{t+1} = (K_t)_* \left( \frac{w_t\nu_t}{\displaystyle\int_{S_t}w_t\,d\nu_t} \right)

defines a normalized population evolution between varying semantic spaces. If Lt\mathcal{L}_t is the corresponding positive transfer operator, then

νt+n=Lt+n−1⋯Ltνt(Lt+n−1⋯Ltνt)(St+n). \nu_{t+n} = \frac{\mathcal{L}_{t+n-1}\cdots\mathcal{L}_t\nu_t}{(\mathcal{L}_{t+n-1}\cdots\mathcal{L}_t\nu_t)(S_{t+n})}.

Proof.

Normalization is Theorem 28.2. The iterated expression is Proposition 30.1.

64 Main reflexivity theorem

Theorem 64.1 Universe-reflexive memetic dynamics

Let U\mathfrak{U} be an ∞-category of semantic universes and Ω=(∫US)≃\Omega=\bigl(\int_{\mathfrak{U}}S\bigr)^{\simeq}. Suppose KK and ww are as in Sections 37–38. Then the normalized operator of Section 39 simultaneously evolves semantic universes, cognitive architectures, semantic states, and their population frequencies. If the universe marginal of KK depends on the resident semantic state, then semantic states can modify the future semantic universes in which their descendants are represented.

Proof.

The total state space Ω\Omega records both universe and semantic-state coordinates. The kernel outputs a distribution over the same total space, so its pushforward can change both coordinates. Dependence of P(πU)K((u,s),−)\mathbb{P}(\pi_{\mathfrak{U}})K((u,s),-) on ss is exactly the condition that semantic state influences semantic-universe transition.

65 Total hierarchy

The complete formal hierarchy is now

presentation↓semantic state↓observer-relative equivalence class↓cognitive architecture↓cognitive site↓semantic ∞-topos↓space of possible semantic ∞-topoi. \begin{array}{c} \text{presentation} \\ \downarrow \\ \text{semantic state} \\ \downarrow \\ \text{observer-relative equivalence class} \\ \downarrow \\ \text{cognitive architecture} \\ \downarrow \\ \text{cognitive site} \\ \downarrow \\ \text{semantic }\infty\text{-topos} \\ \downarrow \\ \text{space of possible semantic }\infty\text{-topoi.} \end{array}

Evolution can occur at every level.

66 Final principle

In the fixed-universe theory the question was whether a transformation preserves a meme. In the observer-relative theory it became whether it preserves the meme relative to a cognitive modality. In the coevolutionary theory it became how the meme alters the modality that later judges its identity. The present theory asks how the evolving population alters the entire semantic universe in which such questions can be formulated.

How does the population alter the semantic universe in which identity can be asked?

The causal chain is semantic state, architecture change, site change, new descent structure, a new ∞-topos, new identity types, and then new replication dynamics.

Memetic evolution can modify the universe of semantic possibility itself: dynamics of states, of observers, of descent, and of the ambient ∞-topos. In compact form,

(Et,Mt,νt)⟼(Et+1,Mt+1,νt+1), (\mathcal{E}_t,M_t,\nu_t)\longmapsto(\mathcal{E}_{t+1},M_{t+1},\nu_{t+1}),

where even the type of the state being evolved is permitted to change.