Parameterized Update #
Framework-agnostic infrastructure for semantics and pragmatics with parameter uncertainty: meaning depends on a parameter (threshold, compositional context, comparison class, variable assignment) and truth at a world involves quantifying over available parameters.
The Fiber Bundle #
A fragment set F ⊆ P × W is a fiber bundle over worlds W. The fiber at w is F_w = {p : F(p, w)} — the parameters available at that world. An assertion φ with parameterized semantics ⟦φ⟧ : P → W → Prop acts as a fiberwise filter: F' = {(p, w) ∈ F : ⟦φ⟧(p, w)}.
Different linguistic theories differ in how they project from the bundle back to worlds, using the ∃ ⊣ Δ ⊣ ∀ adjunction:
- ∃-projection (@cite{caie-2023}, Barker 2002): w survives iff ∃ p ∈ F_w, ⟦φ⟧(p, w)
- ∀-projection (supervaluation, Fine 1975): w survives iff ∀ p ∈ F_w, ⟦φ⟧(p, w)
- Σ-projection (RSA, @cite{lassiter-goodman-2017}): score(w) = Σ_p weight(p) · ⟦φ⟧(p, w)
The first two are De Morgan duals (not_forall): ¬(∀p.φ) ↔ ∃p.¬φ.
The third is a soft interpolation (not formalized here; see RSA).
Instances #
| Theory | P (parameter) | Projection |
|---|---|---|
| Caie 2023 | compositional context | ∃ |
| Supervaluation | precisification | ∀ |
| RSA (L&G 2017) | threshold θ | Σ |
| CCP | variable assignment | ∃ |
| Klein 1980 | comparison class | ∃ (comp.) |
Monotone Collapse #
When semantics is antitone in the parameter (e.g., d > θ for degree
semantics), the projections collapse to extremal checks:
- ∃ p ∈ F_w, sem(p, w) ↔ sem(min(F_w), w)
- ∀ p ∈ F_w, sem(p, w) ↔ sem(max(F_w), w)
The gap between min and max is the borderline region: worlds where ∃-projection and ∀-projection disagree.
Contextual Pruning #
Sequential assertion with pruning — the mechanism by which earlier assertions constrain which parameters are available for later ones — reduces to a single update checking both conditions. This holds for both ∃ and ∀ projection.
A fragment set: a relation between parameters and worlds. F(p, w) holds iff parameter p is available at world w. The fiber at w is F_w = {p : F p w}.
Generalizes InterpAssignment C W from @cite{caie-2023}
(argument order swapped).
Equations
- Core.Semantics.ParameterizedUpdate.FragmentSet P W = (P → W → Prop)
Instances For
Fiberwise filter: restrict a fragment set to parameter–world pairs where the semantics holds.
Generalizes Contextual Pruning (@cite{caie-2023}): after asserting α, only parameters that made α true remain available.
Equations
- Core.Semantics.ParameterizedUpdate.fiberwiseFilter F sem p w = (F p w ∧ sem p w)
Instances For
Existential projection: w survives iff some parameter in F_w makes the semantics true.
This is @cite{caie-2023}'s disjunctive updating and Barker 2002's dynamics of vagueness.
Equations
- Core.Semantics.ParameterizedUpdate.existentialProjection F sem w = ∃ (p : P), F p w ∧ sem p w
Instances For
Universal projection: w survives iff all parameters in F_w make the semantics true.
This is super-truth (Fine 1975): truth under all admissible precisifications.
Equations
- Core.Semantics.ParameterizedUpdate.universalProjection F sem w = ∀ (p : P), F p w → sem p w
Instances For
Existential update: restrict to the context set, then ∃-project.
existentialUpdate cs F sem w ↔ w ∈ cs ∧ ∃ p ∈ F_w, sem(p, w).
Equations
- Core.Semantics.ParameterizedUpdate.existentialUpdate cs F sem w = (cs w ∧ Core.Semantics.ParameterizedUpdate.existentialProjection F sem w)
Instances For
Universal update: restrict to the context set, then ∀-project.
universalUpdate cs F sem w ↔ w ∈ cs ∧ ∀ p ∈ F_w, sem(p, w).
Equations
- Core.Semantics.ParameterizedUpdate.universalUpdate cs F sem w = (cs w ∧ Core.Semantics.ParameterizedUpdate.universalProjection F sem w)
Instances For
De Morgan duality (∃-side): ∃-projection of sem ↔ negation of ∀-projection of the negation.
De Morgan duality (∀-side): ∀-projection of sem ↔ negation of ∃-projection of the negation.
Monotone collapse (∃): when sem is Antitone in p and F_w has
a least element, ∃-projection reduces to checking the minimum.
The Antitone condition on fun p => sem p w means p₁ ≤ p₂ → sem p₂ w → sem p₁ w — truth propagates downward in the parameter
ordering. This is the standard situation in degree semantics:
⟦tall⟧(θ, w) = degree(w) > θ is antitone in θ.
Monotone collapse (∀): when sem is Antitone in p and F_w has
a greatest element, ∀-projection reduces to checking the maximum.
For degree semantics: the ∀-projection ∀ θ ∈ Θ, degree(w) > θ
collapses to degree(w) > max(Θ).
Corollary: when sem is antitone and F_w has both a least and greatest element, the ∃ and ∀ projections agree iff w is outside the borderline region — either sem holds at the hardest parameter (clearly in) or fails at the easiest (clearly out).
The borderline region where projections disagree is precisely
sem p_min w ∧ ¬ sem p_max w.
Sequential ∃-update with pruning: asserting α then β (where β's parameters are pruned by α) equals a single ∃-update checking both α and β.
This is the general form of Contextual Pruning (@cite{caie-2023}): the two-step process (update context set by α, prune parameters by α, then update by β) is equivalent to a single update requiring both α and β under the same parameter.
Sequential ∀-update with pruning: asserting α then β (where β's parameters are pruned by α) equals a single ∀-update checking both α and β.
The ∀ case works because: if all parameters satisfy α (first step), then "pruned parameters" = "all parameters", so requiring β for pruned parameters = requiring β for all parameters.
∃-projection is Monotone in the fragment set: expanding available
parameters can only add surviving worlds. The FragmentSet P W
type P → W → Prop carries the pointwise → ordering, and
∃-projection preserves it.
∀-projection is Antitone in the fragment set: expanding available
parameters can only remove surviving worlds (more to check).
∃-update only removes worlds from the context set.
∀-update only removes worlds from the context set.
Fiberwise filter only removes parameters.
∀-update implies ∃-update when the fiber is non-empty. Super-truth implies disjunctive survival.
When F_w is a singleton {p₀}, both projections agree with a direct check of sem(p₀, w). No parameter uncertainty.
Singleton ∃-update reduces to propositional filtering.