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Inquisitive semantics

Inquisitive semantics is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Inquisitive semantics rather than just read about it. In short: Inquisitive semantics is a framework in logic and natural language semantics. In inquisitive semantics, the semantic content of a sentence captures both the information that the sentence conveys and the issue that it raises.

Key takeaways

  • Inquisitive semantics belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Inquisitive semantics to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Inquisitive semantics from memory before moving on to harder problems.

Reference excerpt

Inquisitive semantics is a framework in logic and natural language semantics. In inquisitive semantics, the semantic content of a sentence captures both the information that the sentence conveys and the issue that it raises. The framework provides a foundation for the linguistic analysis of statements and questions. It was originally developed by Ivano Ciardelli, Jeroen Groenendijk, Salvador Mascarenhas, and Floris Roelofsen.

Basic notions The essential notion in inquisitive semantics is that of an inquisitive proposition.

An information state (alternately a classical proposition) is a set of possible worlds. An inquisitive proposition is a nonempty downward-closed set of information states. Inquisitive propositions encode informational content via the region of logical space that their information states cover. For instance, the inquisitive proposition { { w } , ∅ } {\displaystyle \{\{w\},\emptyset \}} encodes the information that {w} is the actual world. The inquisitive proposition { { w } , { v } , ∅ } {\displaystyle \{\{w\},\{v\},\emptyset \}} encodes that the actual world is either w {\displaystyle w} or v {\displaystyle v} . An inquisitive proposition encodes inquisitive content via its maximal elements, known as alternatives. For instance, the inquisitive proposition { { w } , { v } , ∅ } {\displaystyle \{\{w\},\{v\},\emptyset \}} has two alternatives, namely { w } {\displaystyle \{w\}} and { v } {\displaystyle \{v\}} . Thus, it raises the issue of whether the actual world is w {\displaystyle w} or v {\displaystyle v} while conveying the information that it must be one or the other. The inquisitive proposition { { w , v } , { w } , { v } , ∅ } {\displaystyle \{\{w,v\},\{w\},\{v\},\emptyset \}} encodes the same information but does not raise an issue since it contains only one alternative. The informational content of an inquisitive proposition can be isolated by pooling its constituent information states as shown below.

The informational content of an inquisitive proposition P is info ⁡ ( P ) = { w ∣ w ∈ t for some t ∈ P } {\displaystyle \operatorname {info} (P)=\{w\mid w\in t{\text{ for some }}t\in P\}} . Inquisitive propositions can be used to provide a semantics for the connectives of propositional logic since they form a Heyting algebra when ordered by the subset relation. For instance, for every proposition P there exists a relative pseudocomplement P ∗ {\displaystyle P^{*}} , which amounts to { s ⊆ W ∣ s ∩ t = ∅ for all t ∈ P } {\displaystyle \{s\subseteq W\mid s\cap t=\emptyset {\text{ for all }}t\in P\}} . Similarly, any two propositions P and Q have a meet and a join, which amount to P ∩ Q {\displaystyle P\cap Q} and P ∪ Q {\displaystyle P\cup Q} respectively. Thus inquisitive propositions can be assigned to formulas of L {\displaystyle {\mathcal {L}}} as shown below. Given a model M = ⟨ W , V ⟩ {\displaystyle {\mathfrak {M}}=\langle W,V\rangle } where W is a set of possible worlds and V is a valuation function:

[ [ p ] ] = { s ⊆ W ∣ ∀ w ∈ s , V ( w , p ) = 1 } {\displaystyle [\![p]\!]=\{s\subseteq W\mid \forall w\in s,V(w,p)=1\}}

[ [ ¬ φ ] ] = { s ⊆ W ∣ s ∩ t = ∅ for all t ∈ [ [ φ ] ] } {\displaystyle [\![\neg \varphi ]\!]=\{s\subseteq W\mid s\cap t=\emptyset {\text{ for all }}t\in [\![\varphi ]\!]\}}

[ [ φ ∧ ψ ] ] = [ [ φ ] ] ∩ [ [ ψ ] ] {\displaystyle [\![\varphi \land \psi ]\!]=[\![\varphi ]\!]\cap [\![\psi ]\!]}

[ [ φ ∨ ψ ] ] = [ [ φ ] ] ∪ [ [ ψ ] ] {\displaystyle [\![\varphi \lor \psi ]\!]=[\![\varphi ]\!]\cup [\![\psi ]\!]}

The operators ! and ? are used as abbreviations in the manner shown below.

! φ ≡ ¬ ¬ φ {\displaystyle !\varphi \equiv \neg \neg \varphi }

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Inquisitive semantics

Start with the simplest possible case. Write down what Inquisitive semantics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Inquisitive semantics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Inquisitive semantics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Inquisitive semantics

In research
Inquisitive semantics appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Inquisitive semantics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Inquisitive semantics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Intuitionism, Non-classical logic, Philosophical logic, so understanding it makes those chapters shorter.
In everyday life
Look for Inquisitive semantics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Inquisitive semantics in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Inquisitive semantics means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Inquisitive semantics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Inquisitive semantics in simple terms?

Inquisitive semantics is a framework in logic and natural language semantics. In inquisitive semantics, the semantic content of a sentence captures both the information that the sentence conveys and the issue that it raises.

Why does Inquisitive semantics matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Inquisitive semantics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Inquisitive semantics.

Tags

  • Intuitionism
  • Non-classical logic
  • Philosophical logic
  • Semantics
  • Systems of formal logic

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