Probabilistic Answerhood @cite{thomas-2026}

@cite{groenendijk-stokhof-1984}

Answerhood in terms of probability changes, following @cite{thomas-2026} "A probabilistic, question-based approach to additivity".

Core Definitions

Definition 61 - Relevance: A proposition P is relevant to question Q iff P changes the probability of some alternative:

Relevant(P, Q) ≡ ∃A ∈ Q: P(A|P) ≠ P(A)

Definition 62 - Probabilistic Answerhood: P probabilistically answers Q iff P raises the probability of some alternative:

ProbAnswers(P, Q) ≡ ∃A ∈ Q: P(A|P) > P(A)

Definition 63 - Evidences More Strongly: R evidences A more strongly than R':

EvidencesMoreStrongly(R, R', A) ≡ P(A|info(R)) > P(A|info(R'))

These probabilistic notions of answerhood are central to Thomas's analysis of additive particles like "too", "also", "either".

Connection to Mention-Some

Probabilistic answerhood generalizes the mention-some/mention-all distinction:

• Under uniform priors, probabilistic answerhood reduces to standard partial answerhood
• Non-uniform priors allow context-sensitive answerhood

@[reducible, inline]

abbrev Semantics.Questions.ProbabilisticAnswerhood.Prior (W : Type u_1) : Type u_1

A prior distribution as a mass function over worlds.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.Prior W = (W → ℚ)

def Semantics.Questions.ProbabilisticAnswerhood.probOfProp {W : Type u_1} [Fintype W] (prior : Prior W) (φ : W → Bool) : ℚ

Compute P(φ) - probability that φ is true.

This sums the probability mass over worlds where φ holds.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.probOfProp prior φ = ∑ w : W, if φ w = true then prior w else 0

def Semantics.Questions.ProbabilisticAnswerhood.conditionalProb {W : Type u_1} [Fintype W] (prior : Prior W) (condition target : W → Bool) : ℚ

Compute P(A | C) - conditional probability of A given C.

Returns P(A ∧ C) / P(C) when P(C) > 0, otherwise 0.

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@[reducible, inline]

abbrev Semantics.Questions.ProbabilisticAnswerhood.probOfState {W : Type u_1} [Fintype W] (prior : Prior W) (σ : Discourse.InfoState W) : ℚ

Probability of an info state being actual.

P(σ) = probability that the actual world is in σ.

This is identical to probOfProp — a convenience alias using info state vocabulary rather than proposition vocabulary.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.probOfState prior σ = Semantics.Questions.ProbabilisticAnswerhood.probOfProp prior σ

def Semantics.Questions.ProbabilisticAnswerhood.relevant {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Relevance: P changes the probability of some alternative in Q.

Simplified from @cite{thomas-2026} Definition 61 for the case where R is a declarative (single alternative P). Thomas's full definition quantifies over alternatives of both R and S: ∃A ∈ alt(R), A' ∈ alt(S) s.t. P_L(A'|A) ≠ P_L(A'). For declarative R with a single alternative P, this reduces to: ∃A' ∈ alt(Q): P(A'|P) ≠ P(A').

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def Semantics.Questions.ProbabilisticAnswerhood.irrelevant {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Non-relevance: P doesn't change any alternative's probability.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.irrelevant p q prior = !Semantics.Questions.ProbabilisticAnswerhood.relevant p q prior

def Semantics.Questions.ProbabilisticAnswerhood.probAnswers {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Probabilistic answerhood (simplified): P raises the probability of some alternative.

Simplified from @cite{thomas-2026} Definition 62, which additionally requires that the witnessed resolution is raised MORE than any other (in ratio terms): (b) for all A' ⊂ alt(Q), if ∩A' ⊉ ∩A, then P(∩A|info(R))/P(∩A) > P(∩A'|info(R))/P(∩A').

This implementation captures only condition (a): ∃A ∈ Q: P(A|P) > P(A). The full definition is stronger — it requires the supported answer to be maximally supported. For the cases we use (binary QUDs, single-alternative declaratives), the simplified version suffices.

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def Semantics.Questions.ProbabilisticAnswerhood.probAnswersFull {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Full probabilistic answerhood per @cite{thomas-2026} Definition 62.

R ANSWERS Q iff ∃ nonempty A ⊆ alt(Q) s.t. (a) P(∩A | info(R)) > P(∩A) (b) ∀A' ⊆ alt(Q), ∩A' ⊄ ∩A → P(∩A|info(R))/P(∩A) > P(∩A'|info(R))/P(∩A')

Condition (b) ensures that A is the maximally supported resolution: no other resolution whose intersection isn't already contained in ∩A has a higher Bayes factor. This prevents a proposition from "answering" a question by accidentally raising two unrelated alternatives equally.

For binary QUDs (|alt(Q)| = 2), conditions (a) and (b) coincide with probAnswers: raising P(H) necessarily lowers P(¬H), so the Bayes factor condition is automatic. See probAnswersFull_eq_simple_binary.

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theorem Semantics.Questions.ProbabilisticAnswerhood.probAnswersFull_eq_simple_binary {W : Type u_1} [Fintype W] (p h : W → Bool) (prior : Prior W) (hBinary : (Discourse.Issue.polar h).alternatives.length = 2) : probAnswersFull p (Discourse.Issue.polar h) prior = probAnswers p (Discourse.Issue.polar h) prior

The simplified probAnswers (condition (a) only) is equivalent to the full Thomas (62) probAnswersFull for binary QUDs.

For binary {H, ¬H}, raising P(H) necessarily lowers P(¬H), so the Bayes factor for H automatically exceeds the Bayes factor for ¬H. The only nonempty subsets with non-trivial intersections are {H} and {¬H} (since ∩{H,¬H} = H ∩ ¬H = ∅ has P(∅) = 0).

def Semantics.Questions.ProbabilisticAnswerhood.supportedAlternatives {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : List (Discourse.InfoState W)

Which alternative(s) are supported by P.

Returns the alternatives whose probability is raised by learning P.

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def Semantics.Questions.ProbabilisticAnswerhood.maxSupportedAlternative {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Option (Discourse.InfoState W × ℚ)

The maximally supported alternative: the one whose probability increases the most when P is learned.

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def Semantics.Questions.ProbabilisticAnswerhood.infoContent {W : Type u_1} (states : List (Discourse.InfoState W)) : W → Bool

Informational content of a resolving state.

For a single info state σ (representing a potential resolution), info(σ) is just σ itself - the proposition that the actual world is in σ.

For multiple resolving states, info({σ₁,..., σₙ}) is their union.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.infoContent states w = states.any fun (σ : Discourse.InfoState W) => σ w

def Semantics.Questions.ProbabilisticAnswerhood.evidencesMoreStrongly {W : Type u_1} [Fintype W] (r r' : List (Discourse.InfoState W)) (a : Discourse.InfoState W) (prior : Prior W) : Bool

Evidences more strongly: R evidences A more strongly than R' does.

Definition 63 from @cite{thomas-2026}:

EvidencesMoreStrongly(R, R', A) ≡ P(A|info(R)) > P(A|info(R'))

Used in the felicity conditions for additive particles: the prejacent combined with the antecedent must evidence some resolution more strongly than the antecedent alone.

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def Semantics.Questions.ProbabilisticAnswerhood.evidencesMoreStronglyProp {W : Type u_1} [Fintype W] (evidence evidence' conclusion : W → Bool) (prior : Prior W) : Bool

Simpler version: single propositions instead of state lists.

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def Semantics.Questions.ProbabilisticAnswerhood.evidentialBoost {W : Type u_1} [Fintype W] (evidence conclusion : W → Bool) (prior : Prior W) : ℚ

Compute how much evidence raises the probability of a conclusion.

This is P(A|E) - P(A), measuring the "boost" from learning E.

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def Semantics.Questions.ProbabilisticAnswerhood.isPositiveEvidence {W : Type u_1} [Fintype W] (evidence conclusion : W → Bool) (prior : Prior W) : Bool

Evidence is positive if it raises the probability of the conclusion.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.isPositiveEvidence evidence conclusion prior = decide (Semantics.Questions.ProbabilisticAnswerhood.evidentialBoost evidence conclusion prior > 0)

def Semantics.Questions.ProbabilisticAnswerhood.isNegativeEvidence {W : Type u_1} [Fintype W] (evidence conclusion : W → Bool) (prior : Prior W) : Bool

Evidence is negative if it lowers the probability of the conclusion.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.isNegativeEvidence evidence conclusion prior = decide (Semantics.Questions.ProbabilisticAnswerhood.evidentialBoost evidence conclusion prior < 0)

def Semantics.Questions.ProbabilisticAnswerhood.isUniformOver {W : Type u_1} [Fintype W] [DecidableEq W] (prior : Prior W) (worlds : List W) : Bool

Check if a prior is uniform over a world list.

For proving that probabilistic answerhood reduces to standard answerhood under uniform priors.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.isUniformOver prior [] = true
• Semantics.Questions.ProbabilisticAnswerhood.isUniformOver prior (w :: ws) = ws.all fun (v : W) => prior v == prior w

theorem Semantics.Questions.ProbabilisticAnswerhood.probAnswers_when_entailing {W : Type u_1} [Fintype W] [DecidableEq W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) (alt : W → Bool) (hAltMem : alt ∈ q.alternatives) (hEntails : ∀ (w : W), p w = true → alt w = true) (hPosP : probOfProp prior p > 0) (hNotCertain : probOfState prior alt < 1) : probAnswers p q prior = true

Entailing an alternative guarantees probabilistic answerhood.

If P entails some alternative A (every P-world is an A-world) and A is not already certain, then learning P raises A's probability to 1, which exceeds the prior P(A) < 1. This gives probAnswers (not just relevance).

Note: the weaker condition of mere consistency (P ∩ A ≠ ∅) does NOT suffice — a proposition balanced across alternatives (e.g., W={0,1,2,3}, Q={{0,1},{2,3}}, P={0,2}) can be consistent with every alternative without changing any conditional probability.

def Semantics.Questions.ProbabilisticAnswerhood.conjunctionStrengthens {W : Type u_1} [Fintype W] (p1 p2 conclusion : W → Bool) (prior : Prior W) : Bool

Check if conjunction of two propositions provides stronger evidence than the first proposition alone.

This is the core of Thomas's analysis of additive particles: TOO(π) requires that ANT ∧ π evidences some resolution more strongly than ANT alone.

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def Semantics.Questions.ProbabilisticAnswerhood.strengthenedResolutions {W : Type u_1} [Fintype W] (p1 p2 : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : List (Discourse.InfoState W)

Find resolutions that the conjunction evidences more strongly.

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def Semantics.Questions.ProbabilisticAnswerhood.someResolutionStrengthened {W : Type u_1} [Fintype W] (p1 p2 : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Check if there exists some resolution that is strengthened.

Equations

• Semantics.Questions.ProbabilisticAnswerhood.someResolutionStrengthened p1 p2 q prior = decide ((Semantics.Questions.ProbabilisticAnswerhood.strengthenedResolutions p1 p2 q prior).length > 0)

def Semantics.Questions.ProbabilisticAnswerhood.isProbMentionSomeAnswer {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Probabilistic mention-some: P gives a "partial" probabilistic answer.

P is a probabilistic mention-some answer to Q if:

1. P raises the probability of some alternative(s)
2. P doesn't resolve Q completely (doesn't make one alternative certain)

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def Semantics.Questions.ProbabilisticAnswerhood.isProbMentionAllAnswer {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : Bool

Probabilistic mention-all: P resolves Q completely.

P is a probabilistic mention-all answer if learning P makes exactly one alternative have probability 1.

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theorem Semantics.Questions.ProbabilisticAnswerhood.probAnswers_implies_relevant {W : Type u_1} [Fintype W] (p : W → Bool) (q : Discourse.Issue W) (prior : Prior W) : probAnswers p q prior = true → relevant p q prior = true

Probabilistic answerhood implies relevance.

If P raises the probability of some alternative, then P changes the probability of that alternative.

theorem Semantics.Questions.ProbabilisticAnswerhood.strongerEvidence_is_positive {W : Type u_1} [Fintype W] (r r' : List (Discourse.InfoState W)) (a : Discourse.InfoState W) (prior : Prior W) : evidencesMoreStrongly r r' a prior = true → conditionalProb prior (infoContent r) a > conditionalProb prior (infoContent r') a

Stronger evidence is positive evidence.

If R evidences A more strongly than R', then R is positive evidence for A relative to R'.

ℚ↔ℝ Probability Bridge

ProbabilisticAnswerhood uses Prior W := W → ℚ (exact rational arithmetic), while EntropyNPIs uses W → ℝ (for Mathlib's negMulLog/Real.log). To connect the two, cast via fun w => (prior w : ℝ). The identity probOfProp prior φ cast to ℝ equals ∑ w, if φ w then (prior w : ℝ) else 0 follows from Rat.cast_sum.

A formal bridge theorem (probOfProp_cast_eq_cellProb) is deferred until both modules share an import of Mathlib.Data.Real.Basic.