Argumentative Strength for Quantity Expressions #
@cite{cummins-franke-2021} @cite{merin-1999} @cite{macuch-silva-etal-2024}
@cite{merin-1999}'s log-likelihood ratio measure of argumentative strength, as applied to numerical quantity expressions in @cite{cummins-franke-2021} and quantifier choice in Macuch @cite{macuch-silva-etal-2024}.
Core Idea #
Rather than maximizing informativity (standard RSA), speakers choose quantity expressions to serve argumentative goals: making a conclusion G more or less credible. The argumentative strength of utterance u for goal G is:
argStr(u, G) = log₂(P(u|G) / P(u|¬G)) (C&F Eq. 17)
A pragmatic variant replaces literal truth with assertability:
pragArgStr(u, G) = log₂(P_a(u|G) / P_a(u|¬G)) (C&F Eq. 25)
Key Results #
- Semantic and pragmatic measures can reverse the ordering of utterances
- The Bayes factor P(u|G)/P(u|¬G) admits ordinal comparison without log
- argStr is a pointwise KL divergence (bridge to InformationTheory)
- At λ=1 in CombinedUtility, utility reduces to pure argumentative strength
An argumentative goal partitions worlds into G (goal-supporting) vs ¬G.
- goalTrue : W → Bool
Returns true for worlds where the goal holds
Instances For
Bayes factor: P(u|G) / P(u|¬G), the pre-log ratio.
This is the key quantity for argumentative strength. It measures how much more likely utterance u is to be true (or assertable) given G vs ¬G. C&F Eq. 17 (before taking log).
Equations
Instances For
Argumentative strength: log₂ of the Bayes factor.
argStr(u, G) = log₂(P(u|G) / P(u|¬G))
C&F Eq. 17. Positive values mean u supports G; negative means u supports ¬G.
Equations
- RSA.ArgumentativeStrength.argStr pGivenG pGivenNotG = Core.InformationTheory.log2Approx (RSA.ArgumentativeStrength.bayesFactor pGivenG pGivenNotG)
Instances For
Pragmatic argumentative strength using assertability probabilities.
pragArgStr(u, G) = log₂(P_a(u|G) / P_a(u|¬G))
C&F Eq. 25. Replaces literal truth with pragmatic assertability.
Equations
- RSA.ArgumentativeStrength.pragArgStr pAssertableGivenG pAssertableGivenNotG = Core.InformationTheory.log2Approx (RSA.ArgumentativeStrength.bayesFactor pAssertableGivenG pAssertableGivenNotG)
Instances For
Utterance has positive argumentative strength iff P(u|G) > P(u|¬G).
Ordinal characterization — no log needed.
Equations
- RSA.ArgumentativeStrength.hasPositiveArgStr pGivenG pGivenNotG = (pGivenG > pGivenNotG)
Instances For
Equations
- RSA.ArgumentativeStrength.instDecidableHasPositiveArgStr pGivenG pGivenNotG = inferInstanceAs (Decidable (pGivenG > pGivenNotG))
Utterance u₁ is argumentatively stronger than u₂ for goal G iff its Bayes factor is higher. Ordinal comparison — no log needed.
Equations
- One or more equations did not get rendered due to their size.
Instances For
Argumentative difficulty: proportion of truthful states where the strongest available quantifier is weak. From Macuch @cite{macuch-silva-etal-2024}.
For a given direction (positive/negative), difficulty measures how hard it is to frame the state in that direction truthfully. High difficulty → speakers must use weaker quantifiers.
- proportion : ℚ
Proportion of relevant items (e.g., correct answers out of total)
- isPositiveFrame : Bool
Whether the speaker is framing positively or negatively
- difficulty : ℚ
Difficulty value: 0 = easy (can use "all"), 1 = hard (must use "some")
Instances For
Rational hearer (semantic): increase belief in G upon hearing u iff argStr(u, G) > 0 (C&F Eq. 27).
Equations
- RSA.ArgumentativeStrength.rationalHearerSemantic pGivenG pGivenNotG = decide (RSA.ArgumentativeStrength.hasPositiveArgStr pGivenG pGivenNotG)
Instances For
Rational hearer (pragmatic): increase belief in G upon hearing u iff pragArgStr(u, G) > 0 (C&F Eq. 28).
Equations
- RSA.ArgumentativeStrength.rationalHearerPragmatic pAssertableGivenG pAssertableGivenNotG = decide (RSA.ArgumentativeStrength.hasPositiveArgStr pAssertableGivenG pAssertableGivenNotG)
Instances For
argStr is a pointwise KL divergence term.
The KL divergence D_KL(P_G || P_¬G) = Σ_u P_G(u) · log(P_G(u)/P_¬G(u)). Each summand P_G(u) · log(P_G(u)/P_¬G(u)) contains the argStr as the log factor. That is, argStr(u, G) = log₂(P(u|G)/P(u|¬G)) is the pointwise divergence.
Positive argStr iff Bayes factor > 1 (ordinal characterization).
When λ=1 in CombinedUtility.combined, utility reduces to pure U_B. If U_B is argumentative strength, this connects combined utility to argStr.
That is: combined 1 U_informative U_argumentative = U_argumentative
A goal-oriented speaker (β > 0) strictly prefers utterances with higher argumentative strength, connecting Cummins & Franke's argStr ordering to Barnett et al.'s goal-oriented utility framework.
When U_goal = argStr(u, G), higher argStr → higher goalOrientedUtility.