On Quantifiers for Quantitative Reasoning
Not on the map. Why?
Abstract (arXiv)
We explore a kind of first-order predicate logic with intended semantics in the reals. Compared to other approaches in the literature, we work predominantly in the multiplicative reals [0,∞], showing they support three generations of connectives, that we call non-linear, linear additive, and linear multiplicative. Means and harmonic means emerge as natural candidates for bounded existential and universal quantifiers, and in fact we see they behave as expected in relation to the other logical connectives. We explain this fact through the well-known fact that min/max and arithmetic mean/harmonic mean sit at opposite ends of a spectrum, that of p-means. We give syntax and semantics for this quantitative predicate logic, and as example applications, we show how softmax is the quantitative semantics of argmax, and R'enyi entropy/Hill numbers are additive/multiplicative semantics of the same formula. Indeed, the additive reals also fit into the story by exploiting the Napierian duality -log dashv 1/exp, which highlights a formal distinction between 'additive' and 'multiplicative' quantities. Finally, we describe two attempts at a categorical semantics via enriched hyperdoctrines. We discuss why hyperdoctrines are in fact probably inadequate for this kind of logic.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Jun 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
- 2025Proof-Carrying Neuro-Symbolic Codecited by 1
- 2026Quantitative Linear Logic for Neuro-Symbolic Learning and Verificationcited by 0
- 2026Quantitative Linear Logic for Neuro-Symbolic Learning and Verification13 May 2026
- 2025Proof-Carrying Neuro-Symbolic Code16 Apr 2025