Basic dynamic entry

The picture that emerges is one where evaluative sentences are doing a sort of double duty:

they can give factual and practical information (or a mixture of both). So evaluative sentences can perform two semantic operations: (i) an operation of attributing certain factual properties to some object(s) and (ii) an operation of avowing the adoption of a plan. The first operation can be modelled in terms of operations over possible worlds, and the second can be modelled in terms of operations over hyperplans. More precisely, in the dynamic framework that we advocate, the first operation is understood as an update function on the context set parameter of the common ground, while the second can be understood as an update function on the hyperplan parameter.

But each of those operations relies on certain information to be available in the common ground:

the context-set update requires antecedently shared information about the shared car prefer-ences of speakers, which is represented in the hyperplan parameter. And the hyperplan update requires factual information about the cars, which is represented in the context set. This de-scribes the contrast between (Vis-à-vis) and (Experts): in the first situation, the fact that factual information about the cars is available causes the relevant update to be evaluative (an update of the hyperplan parameter); while in the second situation, having information about prefer-ences (i.e. having a common standard) causes the relevant update to be descriptive (an update of the context set). If both context set and hyperplan parameter provide only partial informa-tion, the update will coordinate both parameters appropriately. This will be made clearer in the following subsections.

As we just saw, we start off from the assumption that a sentence such as (6.7) has a double semantic value:

(6.24) The Mercedes is better than the Audi =

⎧⎪

⎪

⎨

⎪⎪

⎩

The Mercedes is F -er than the Audi it is good that P RO is F

On the one hand, (6.7) predicates that the Mercedes has more of a certain property F than the Audi. On the other hand, (6.7) expresses support for individuals that have property F .

How does (6.7) impact the common ground? It depends on the information that is previously available when (6.7) is uttered. When uttered, (6.7) will rule out certain possible worlds from the context set of the common ground if a certain condition is met by the hyperplan parameter;

and it will rule out certain hyperplans from the hyperplan parameter if that same condition is met by the context set. And if the condition is met equally well by the context set and the hyperplan parameter, it will rule out certain combinations of possible worlds and hyperplans.

We can write this as a disjunctive definedness condition to be satisfied either by the hyperplan parameter, the context set or both. Relative to a common ground G with context set C and hyperplan parameter P , the update instruction for (6.7) looks as follows:

(6.26) G[(6.7)]is defined only if C or P provides the stronger F -value; if defined, then:

a. if C provides a stronger F , then G[(6.7)] =G[it is good that P RO is F ]

b. if P provides a stronger F , then G[(6.7)] =G[The Mercedes is F -er than the Audi]

c. if C & P provide equally strong F , then G[(6.7)] =

G[The Mercedes is F -er than the Audi and it is good that P RO is F ] for any stronger value of F .

In words: in order to be defined, an utterance of (6.7) at a common ground G requires that either the context set or the hyperplan parameter of G provide the stronger value or specification for the property F that (6.7) makes reference to, or that both parameters provide equally strong values for F . Stronger here means logically stronger: for example, if the context set provides information to the effect that F stands for sporty or reliable and the hyperplan parameter is such that F stands for sporty, then the value of F in (6.7) will be sporty, because being sporty logically entails being sporty or reliable.

If the context set provides such specification, then we use that information, which is factual information, to update the common ground with the evaluative component of the meaning of (6.7), namely that it is good that P RO is F (where F is the property supplied by the context set). If the hyperplan parameter offers a stronger value for F , then we use that information to update the common ground with the descriptive component of (6.7), namely that the Mercedes isF -er than the Audi (where the value of F is provided by the hyperplan parameter).

And finally, if both the context set and the hyperplan parameter provide equally strong specifi-cations for F , then the update consists in ruling out from the common ground the appropriate discrepancies between the context set and the hyperplan parameter vis-à-vis (6.7), namely sit-uations where the Mercedes has property F but being F is not considered good (for whatever maximally specific value F could take). Suppose, again, that the common ground is such that F can only stand for sporty or reliable. This means two things: first, the context is such that the Mercedes is sportier or more reliable than the Audi. And second, the hyperplan parameter is such that sportiness or reliability are supported. In this common ground, if someone utters (6.7), the resulting update will be to rule out worlds w and hyperplans h (ignoring alternatives) such that either the Mercedes is sportier than the Audi at w and reliability is supported at h or the Mercedes is more reliable than the Audi at w and sportiness is supported at h. Note, however, that this update will not reduce our factual uncertainty about the cars nor our practical uncertainty about car standards: after the update, it’s not known yet whether the Mercedes is sportier or more reliable, nor whether reliability or sportiness are supported. What are ruled out in this case are certain combinations of factual and practical states of affairs.

According to the semantics we proposed in §4.2-4.3, each of the semantic components of (6.24) will have a different kind of semantic value: the descriptive component has a descriptive propo-sition as its semantic value, while the evaluative component has an evaluative propopropo-sition.

These are both sets of world-hyperplan-alternative triplets, but while a descriptive proposition is world-sensitive but hyperplan- and alternative-insensitive, an evaluative proposition is the other way around: it is hyperplan- and alternative-sensitive, but world-insensitive. This is the semantics that we assigned to each type of sentence in §4.2-4.3:

(6.27) a. [[The Mercedes is F -er than the Audi]] =

{⟨w, h, a⟩ ∶ The Mercedes is F -er than the Audi at (w)(h)(a) = 1}

b. [[it is good that P RO is F ]] =

{⟨w, h, a⟩ ∶ {w^′∶ [[P RO is F ]]_⟨w^′_,h,a⟩=1} ∈ h(a[P RO is F ](w_i))}

(6.27a) is a set of world-hyperplan-alternative triplets containing all hyperplans and alterna-tives, but only worlds in which the Mercedes is F -er than the Audi. And (6.27b) is a set of world-hyperplan-alternative triplets containing all worlds, but only hyperplans and alternatives such that being F is supported.

As we saw in §6.5, if we interpret these propositions dynamically, we will obtain an update instruction that will either rule out some hyperplans from the hyperplan parameter while leav-ing the context set untouched, or will rule out worlds from the context set while leavleav-ing the hyperplan parameter untouched:

(6.28) G[The Mercedes is F -er than the Audi] =

⎧⎪

The third type of update, in which neither the context set nor the hyperplan parameters provides a stronger value for property F , results in a partial update of both C and P . The resulting update instructions can be paraphrased like this:

(6.30) a. Instruction on C: rule out all but worlds relative to which the Mercedes is F -er than the Audi.

b. Instruction on P : rule out all but hyperplans relative to which being F is sup-ported.

c. Instruction on C, P : rule out all but worlds and hyperplans relative to which the Mercedes is F -er than the Audi and being F is supported (for all maximally stronger values of F ).

The update on the hyperplan parameter is tantamount to adopting a plan to support cars with the features of which the Mercedes has more than the Audi, and what those features are is determined by the context set. In order to know what hyperplans to rule out, we need to know what the cars are like.

Things work exactly the other way around with the worldly update: it is an instruction to rule out worlds where the Mercedes does not have a higher degree than the Audi of the features that are supported. And what features of cars are supported is determined by the hyperplan parameter. In order to know what worlds to rule out, we need to know what the hyperplan parameter looks like.

And finally, in case that there is an “informational stand-off” between context set and hyperplan parameter, so that neither parameter supplies a stronger value to substitute for F , the update consists in coordinating both parameters: for any possible strengthening F^′ of the value of F , it will be the case that all worlds in the context set are worlds where the Mercedes is F^′-er than the Audi and all hyperplans in the hyperplan parameter are hyperplans that support being F^′. Let us expand on each of these options.