Disjunctive Context Updating #
Michael Caie. Context Dynamics. Semantics and Pragmatics 16, Article 3: 1–37.
The Problem #
Standard accounts of conversational updating ([Sta78]) assume that, at each world w in the context set, there is a unique compositional context c_w interpreting an assertion of φ. The context set is updated by diagonalization: eliminate w iff ⟦φ⟧^{c_w} is false at w.
[Cai23] argues this uniqueness assumption fails for context-sensitive expressions like configurational predicates ("pair of socks"), where multiple compositional contexts may be available at a single world. Standard Updating combined with Minimal Symmetry and Preservation incorrectly predicts the falsity of Safe Information in natural discourses.
The Solution #
Disjunctive Multi-Context Updating: at each world w, there is a non-empty set I^φ_w of compositional contexts. A world w survives iff some context in I^φ_w makes φ true at w. When I^φ_w is a singleton, this reduces to Standard Updating.
Contextual Pruning: interpretation sets narrow across discourse. If β immediately follows α, the contexts available for β at w are exactly those that made α true at w.
Architecture #
Disjunctive Updating is an instance of ∃-projection over a fragment set
(§ 0): meaning depends on a parameter — here the compositional context C —
and the fragment set F c w := I w c says which contexts are available at
each world. disjunctiveUpdate = existentialUpdate and prune =
fiberwiseFilter (both with argument order swapped).
The § 0 substrate is deliberately framework-general (thresholds, precisifications, comparison classes, assignments are all parameters in the same sense), so the general results — De Morgan duality, monotone collapse, sequential update = single conjunctive update — apply directly.
Relationship to Existing Infrastructure #
ContextSet.update c pinCommonGround.leanis the special case where every world gets the same interpretation (no context sensitivity). SeedisjunctiveUpdate_constant.The
SpecSpaceinSupervaluation/Basic.leanis the ∀-dual: super-truth = true under ALL specs; Caie's survival = true under SOME.CCPinDynamic/Update.leanoperates on ⟨assignment, world⟩ pairs;ContextFragmenthere uses ⟨compositional-context, world⟩ — a structural analogue with different conceptual content.
Framework-agnostic infrastructure for parameter uncertainty: meaning depends on a parameter (threshold, compositional context, comparison class, variable assignment) and truth at a world involves quantifying over available parameters.
A fragment set F ⊆ P × W is a fiber bundle over worlds W; the fiber at w, F_w = {p : F(p, w)}, collects 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)}. Theories differ in how they project from the bundle back to worlds, via the ∃ ⊣ Δ ⊣ ∀ adjunction:
- ∃-projection ([Cai23], [Bar02a]): w survives iff ∃ p ∈ F_w, ⟦φ⟧(p, w)
- ∀-projection (supervaluation, [Fin75]): w survives iff ∀ p ∈ F_w, ⟦φ⟧(p, w)
- Σ-projection (RSA, [LG17]): score(w) = Σ_p weight(p) · ⟦φ⟧(p, w) — a soft interpolation, not formalized here
Comparison classes ([Kle80]) are ∃-projected parameters in the same
sense. When the semantics is antitone in the parameter (degree semantics:
d > θ), the projections collapse to extremal checks, and the gap between
min and max is the borderline region where ∃ and ∀ disagree.
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 [Cai23]
(argument order swapped).
Equations
- Caie2023.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 ([Cai23]): after asserting α, only parameters that made α true remain available.
Equations
- Caie2023.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 [Cai23]'s disjunctive updating and [Bar02a]'s dynamics of vagueness.
Equations
- Caie2023.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 ([Fin75]): truth under all admissible precisifications.
Equations
- Caie2023.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
- Caie2023.existentialUpdate cs F sem w = (cs w ∧ Caie2023.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
- Caie2023.universalUpdate cs F sem w = (cs w ∧ Caie2023.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 ([Cai23]): 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.
A context fragment: an ordered pair ⟨compositional context, world⟩.
Context fragments are the state representation for Disjunctive Updating. The compositional context determines how context-sensitive expressions (indexicals, gradable adjectives, configurational predicates) are interpreted; the world determines matters of fact.
Structurally analogous to Possibility W V M in Dynamic/Possibility.lean
(⟨world, assignment⟩ pairs in dynamic semantics), but the non-world
parameter is a compositional context rather than a variable assignment.
- ctx : C
- world : W
Instances For
An interpretation assignment maps worlds to predicates on compositional
contexts. I w c holds iff c is available to interpret an assertion at w.
[Cai23]: "for each world w in the relevant context set, a non-empty set of compositional contexts that interpret that assertion of φ at w: I^φ_w."
This is a FragmentSet C W with swapped argument order:
I w c ↔ F c w where F : FragmentSet C W.
Equations
- Caie2023.InterpAssignment C W = (W → C → Prop)
Instances For
Convert an InterpAssignment to a FragmentSet by swapping argument
order. This is the bridge between Caie's convention (index by world
first) and the § 0 convention (index by parameter
first).
Equations
- I.toFragmentSet c w = I w c
Instances For
Standard Updating with explicit diagonalization ([Sta78], formulated following [Cai23] §1).
At each world w, a unique compositional context c_w determines the proposition expressed. The diagonal proposition is {w ∈ C : w ∈ ⟦φ⟧^{c_w}}.
Note: Assertion.Stalnaker.assert in Stalnaker.lean implements the
degenerate case where the proposition is fixed across worlds (no
diagonalization needed). This definition makes the compositional
context parameter explicit.
Equations
- Caie2023.standardUpdate cs c_w sem w = (cs w ∧ sem (c_w w) w)
Instances For
Disjunctive Multi-Context Updating ([Cai23] §3).
A world w survives iff there exists some compositional context c in the
interpretation set I_w such that ⟦φ⟧^c is true at w. When I_w is a
singleton {c_w}, this reduces to standardUpdate.
Defined as existentialUpdate (§ 0) with the
interpretation assignment as the fragment set (argument order swapped).
[Cai23]: "The result of updating the context given the assertion is C^φ = {w ∈ C_φ : w ∈ ⟦φ⟧^c, for some c ∈ I^φ_w}."
Equations
- Caie2023.disjunctiveUpdate cs I sem = Caie2023.existentialUpdate cs I.toFragmentSet sem
Instances For
Contextual Pruning ([Cai23] §3): restrict interpretation sets to truth-making contexts.
If β immediately follows α in a discourse at world w, the compositional contexts available for β at w are exactly those that made α true at w.
Defined as fiberwiseFilter (§ 0, argument order
swapped).
[Cai23]: "if {c : c ∈ I^α_w and w ∈ ⟦α⟧^c} ≠ ∅, then I^β_w = {c : c ∈ I^α_w and w ∈ ⟦α⟧^c}."
Note: the paper's definition includes a non-emptiness precondition — pruning applies only when at least one context survives. This definition unconditionally restricts; in all applications here, the pruned set is non-empty by construction (both discourses have truth-making contexts at every world).
Equations
- Caie2023.prune I sem w c = Caie2023.fiberwiseFilter I.toFragmentSet sem c w
Instances For
The fragmentation of a context set: all ⟨c, w⟩ pairs where w is in the context set and c is an available interpretation at w.
[Cai23]: "Call a context fragment an ordered pair of a compositional context and a world, and call the fragmentation of C_φ the set of context fragments ⟨c, w⟩ such that w ∈ C_φ and c interprets φ in w."
Equations
- Caie2023.fragmentation cs I f = (cs f.world ∧ I f.world f.ctx)
Instances For
Project fragments to their world components.
Equations
- Caie2023.fragmentWorlds frags w = ∃ (c : C), frags { ctx := c, world := w }
Instances For
Disjunctive updating is equivalent to updating the fragmentation and projecting to worlds.
Standard Updating is the singleton case of Disjunctive Updating. When the interpretation set at each world is {c_w}, the existential in Disjunctive Updating collapses to a single check.
Expanding interpretation sets can only add worlds to the result.
When there is a single fixed interpretation for all worlds, disjunctive
updating reduces to propositional filtering — the mechanism formalized
as ContextSet.update in CommonGround.lean.
This witnesses the fact that ContextSet.update is the degenerate case
of Disjunctive Updating where context sensitivity plays no role: the
same proposition is expressed at every world.
Disjunctive updating with a fixed context reduces to ContextSet.update.
This explicitly connects the general framework to the infrastructure in
CommonGround.lean: context-insensitive assertions (same proposition at
every world) update via ordinary propositional filtering.
Generalized Preservation ([Cai23] §3): if there is a unique compositional context interpreting α at w, and it makes α true, then it persists as the unique context for subsequent assertions.
[Cai23]: "for each w ∈ C_α if there is a unique compositional context c that interprets an assertion of α in w, then if w ∈ C^α, then c uniquely interprets the subsequent assertion of β in w."
This follows directly from Contextual Pruning: when the input is a singleton and the element makes α true, pruning preserves it.
A discourse step: update the context set and prune interpretation sets. Returns the new context set and the narrowed interpretation assignment.
Equations
- Caie2023.discourseStep cs I sem = (Caie2023.disjunctiveUpdate cs I sem, Caie2023.prune I sem)
Instances For
Sequential discourse steps are monotonically restrictive in both the context set and interpretation sets.
Contextual Pruning reduces to the general sequential ∃-update theorem: asserting α then β (with pruned parameters) = single ∃-update checking both α and β under the same parameter.
This is [Cai23]'s central mechanism, obtained for free from
sequential_existentialUpdate (§ 0).
Sarah's Socks ([Cai23] §2.1) #
Tim has strong preferences about socks. Two discourses communicate that Tim likes matching socks and dislikes mixed ones:
Tim Likes Matching: (1) Sarah has two pairs of socks. (2) Tim likes both of them. (3) Both of them are matching.
Tim Dislikes Mixed: (1) Sarah has two pairs of socks. (4) Tim dislikes both of them. (5) Both of them are mixed.
Model #
- Worlds (
TimPref): Tim likes matching pairs, or likes mixed pairs. - Contexts (
DressInt): dressing intension is matching or mixed. - Initial interpretation set: both intensions at every world.
- 4 context fragments: {matching, mixed} × {likesMatching, likesMixed}.
Verification Table (from [Cai23]) #
For Tim Likes Matching:
| C⁽¹⁾ | C⁽¹⁾⁽²⁾ | C⁽¹⁾⁽²⁾⁽³⁾ | |
|---|---|---|---|
| ⟨matchInt, wMatch⟩ | ✓ | ✓ | ✓ |
| ⟨mixedInt, wMatch⟩ | ✓ | ✗ | ✗ |
| ⟨matchInt, wMixed⟩ | ✓ | ✗ | ✗ |
| ⟨mixedInt, wMixed⟩ | ✓ | ✓ | ✗ |
Only ⟨matchInt, wMatch⟩ survives: Tim likes matching socks.
Tim's preference: the world parameter.
Instances For
Equations
- Caie2023.instDecidableEqTimPref x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
Equations
- One or more equations did not get rendered due to their size.
- Caie2023.instReprTimPref.repr Caie2023.TimPref.likesMixed prec✝ = Repr.addAppParen (Std.Format.nest (if prec✝ ≥ 1024 then 1 else 2) (Std.Format.text "Caie2023.TimPref.likesMixed")).group prec✝
Instances For
Equations
- Caie2023.instReprTimPref = { reprPrec := Caie2023.instReprTimPref.repr }
Dressing intension: the compositional context parameter. Determines which non-overlapping pairings of Sarah's socks are in the domain of quantification.
Instances For
Equations
- Caie2023.instDecidableEqDressInt x✝ y✝ = if h : x✝.ctorIdx = y✝.ctorIdx then isTrue ⋯ else isFalse ⋯
Equations
- Caie2023.instReprDressInt.repr Caie2023.DressInt.matching prec✝ = Repr.addAppParen (Std.Format.nest (if prec✝ ≥ 1024 then 1 else 2) (Std.Format.text "Caie2023.DressInt.matching")).group prec✝
- Caie2023.instReprDressInt.repr Caie2023.DressInt.mixed prec✝ = Repr.addAppParen (Std.Format.nest (if prec✝ ≥ 1024 then 1 else 2) (Std.Format.text "Caie2023.DressInt.mixed")).group prec✝
Instances For
Equations
- Caie2023.instReprDressInt = { reprPrec := Caie2023.instReprDressInt.repr }
"Tim likes both of them": true when the intension picks the kind of pairs Tim likes.
Equations
Instances For
Equations
- Caie2023.SarahsSocks.instDecidableLikes Caie2023.DressInt.matching Caie2023.TimPref.likesMatching = isTrue trivial
- Caie2023.SarahsSocks.instDecidableLikes Caie2023.DressInt.matching Caie2023.TimPref.likesMixed = isFalse ⋯
- Caie2023.SarahsSocks.instDecidableLikes Caie2023.DressInt.mixed Caie2023.TimPref.likesMatching = isFalse ⋯
- Caie2023.SarahsSocks.instDecidableLikes Caie2023.DressInt.mixed Caie2023.TimPref.likesMixed = isTrue trivial
"Both of them are matching": true under matching intension.
Equations
Instances For
Equations
- Caie2023.SarahsSocks.instDecidableIsMatching Caie2023.DressInt.matching x✝ = isTrue trivial
- Caie2023.SarahsSocks.instDecidableIsMatching Caie2023.DressInt.mixed x✝ = isFalse ⋯
"Both of them are mixed": true under mixed intension.
Equations
Instances For
Equations
- Caie2023.SarahsSocks.instDecidableIsMixed Caie2023.DressInt.mixed x✝ = isTrue trivial
- Caie2023.SarahsSocks.instDecidableIsMixed Caie2023.DressInt.matching x✝ = isFalse ⋯
Tim Likes Matching result: w survives iff there exists a context c
such that likes c w (surviving (2)) AND isMatching c w (surviving (3)).
The conjunction arises from Contextual Pruning: only contexts that made
(2) true are available to interpret (3).
Equations
- One or more equations did not get rendered due to their size.
Instances For
Tim Dislikes Mixed result: w survives iff ∃ c, dislikes c w ∧ isMixed c w.
Equations
- One or more equations did not get rendered due to their size.
Instances For
Tim Likes Matching keeps the matching-preference world.
Tim Likes Matching eliminates the mixed-preference world.
Tim Dislikes Mixed keeps the matching-preference world.
Tim Dislikes Mixed eliminates the mixed-preference world.
Both discourses yield the same result.
The set of fact-worlds: those where Tim likes matching and dislikes mixed.
Equations
Instances For
Equations
Safe Information condition (i): the update result is a subset of the fact-worlds. Under Disjunctive Updating, asserting Tim Likes Matching eliminates all non-fact worlds.
Safe Information condition (ii): every fact-world where the discourse occurs is retained.
Safe Information condition (i) for Tim Dislikes Mixed.
Safe Information condition (ii) for Tim Dislikes Mixed.
[Cai23] §2.2, first Claim: Standard Updating + Preservation + Minimal Symmetry → ¬Safe Information.
Under Minimal Symmetry, the same dressing intension c interprets sentence (1) in both discourses (at fact-worlds w₁ and w₂ that agree on all pre-assertion facts). Under Preservation, c persists to interpret later sentences. Safe Information (ii) then requires:
likes c w(for TLM to retain a fact-world)dislikes c w(for TDM to retain a fact-world) Butdislikes = ¬likes, so no intension c satisfies both.
This is the paper's central argument against Standard Updating.
[Cai23] §2.2, second Claim: Standard Updating + Uniform Charity → ¬Safe Information (condition i).
Under Uniform Charity (prefer truth-making interpretations), each sentence individually has a truth-making context at every world. Under Standard Updating (unique context per sentence, shifts allowed), each sentence is interpreted by its truth-making context. No world is eliminated, so the update result includes non-fact worlds.
Witness: at .likesMixed, sentence (2) is true under mixed intension
(Tim likes mixed pairs in that world) and sentence (3) is true under
matching intension. Yet .likesMixed is not a fact-world.
The hand-computed Tim Likes Matching result agrees with the Prop-valued
disjunctiveUpdate applied via discourseStep.
This connects the hand-computed verification above to the general theory.
The hand-computed Tim Dislikes Mixed result agrees with the Prop-valued
framework. Mirror of tlm_agrees_with_framework.
isMatching and isMixed are complements.