Adjectival vagueness in a Bayesian model of interpretation∗
Daniel Lassiter
Stanford University
danlassiter@stanford.edu

Noah D. Goodman
Stanford University
ngoodman@stanford.edu

Abstract We derive a probabilistic account of the vagueness and context-sensitivity of scalaradjectives from a Bayesian approach to communication and interpretation. We describe an iteratedreasoning architecture for pragmatic interpretation and illustrate it with a simple scalar implicature
example. We then show how to enrich the apparatus to handle pragmatic reasoning about the valuesof free variables, explore its predictions about the interpretation of scalar adjectives, and show how
this model implements Edgington’s (1992; 1997) account of the sorites paradox, with variations. The
Bayesian approach has a number of explanatory virtues: in particular, it does not require any specialpurpose machinery for handling vagueness, and it is integrated with a promising new approach topragmatics and other areas of cognitive science.

Edgington (1992, 1997) proposes an attractive unified approach to the Sorites, Lottery, andPreface paradoxes. According to Edgington, these puzzles are all explained by a generalization ofclassical logic which has the formal structure of the probability calculus, with an accompanying
generalized notion of valid reasoning. She gives a number of strong arguments to the effect that a
degree-based theory of vagueness with the formal structure of probabilities is preferable to one withthe structure of classical fuzzy logic. However, she explicitly disavows the idea that the degrees
involved in her account of vagueness are probabilities in the usual sense of rational degrees ofcredence; instead, she labels them verities and leaves to the side the question of what exactly they
are, or why they display the logical structure that they do.
As Douven & Decock (2014) note, the lack of a clear interpretation of Edgington’s verities
seems to have hindered acceptance of an otherwise very promising theory of vagueness. Douven& Decock propose an interesting, psychologically-oriented answer to these questions by deriving
verities from a version of the Conceptual Spaces model of concepts (Gärdenfors 2000). As theynote, a convincing derivation of this type is crucial for the overall plausibility of a probabilistic
model of vagueness.
In this paper we propose an alternative explanation for the probabilistic structure of verities,
focusing on the case of (relative) scalar adjectives such as tall, heavy and happy. We show that
∗ Thanks to Michael Franke, Chris Potts, Chris Kennedy, Adrian Brasoveanu, Paul Egré, Alexis Wellwood, Lenhart
Schubert, Richard Dietz, two Synthese reviewers, three SALT 23 reviewers, participants in our 2013 ESSLLI course“Probability in semantics and pragmatics”, participants in Lassiter’s 2014 NASSLLI course “Language understanding
and Bayesian inference”, and audiences at SALT 23, Stanford, Northwestern, Brown, U. Chicago, and UT-Austin. This
paper is modified and extended from Lassiter & Goodman 2013, which appeared in the proceedings of the conference
Semantics & Linguistic Theory 23. This work was supported by a James S. McDonnell Foundation Scholar Award to
NDG and by ONR grant N00014-13-1-0788.

1these adjectives’ context-sensitive interpretation, sorites sensitivity, and the existence of borderline
cases can be derived from a general Bayesian theory of pragmatics (Goodman & Stuhlmüller 2012;
Frank & Goodman 2012; Goodman & Lassiter 2015). This theory is framed within a Bayesian
approach to cognitive science that has flourished in recent years (Tenenbaum, Kemp, Griffiths &Goodman 2011), and has been used recently to account for a wide variety of cognitive activities(e.g., learning, reasoning, categorization, vision, motor control) as well as a number of detailed
pragmatic and psycholinguistic phenomena.
The theory’s application to vague terms depends on the specific lexical semantics of expressions
like heavy, which are generally thought in linguistic semantics to rely on a free threshold variable:“heavy” is interpreted as “heavier than θ ”. In order to assign an interpretation to utterances
containing heavy, listeners must use the available information — context and knowledge of the
speakers’ beliefs and goals — to estimate this latent variable. We propose an approach to thisestimation problem and use computer simulations to demonstrate its predictions and show that theempirical phenomena under consideration can be derived. In effect, our model suggests credence
functions which are able to do most of the work of Edgington’s verities, where the relevant kindof uncertainty is uncertainty about a speaker’s intended message — i.e., about the use to which
the speaker intends to put the semantically flexible linguistic resources that their language makes
available.
This way of approaching the problem has a number of conceptual advantages. In particular, it
allows us to make headway on the difficult question of how communication with vague expressions
is possible, in the information-theoretic sense. Second, it provides answers to two questions which
are not usually considered in the literature on degree-theoretic (including probabilistic) accounts ofvagueness: How are degree functions for specific expressions derived? And how and why do thedegree functions of relative adjectives shift in response to a choice of reference class — in particular,
in response to statistical properties of a reference class? Finally, the theory does not rely on any
ad hoc semantic or pragmatic machinery for vague expressions; rather, it is a direct applicationof principles that have much independent motivation from recent work in formal semantics andpragmatics and in cognitive science.
Several caveats are in order here. First, our model is not in competition with an approach tovagueness based on Conceptual Spaces or other probabilistic theories of conceptual structure. While
our account extends readily to scalar expressions that are not adjectives—for instance, quantifiers
such as many and few, and verbs such as love and fear—it does not account for the vagueness ofexpressions that lack a scalar basis, such as fruit, cup, or bird. A probabilistic account of non-scalarvagueness would mesh well with our theory, but must be derived from additional assumptions,such as those of the Conceptual Spaces theory. Second, we simplify the empirical picture byconsidering only relative adjectives like tall and heavy, leaving aside the interestingly different classof “absolute” adjectives such as full and empty, which was described and connected with vaguenessphenomena in an important paper by Kennedy (2007). (We believe that our theory does a good jobof accounting for absolute adjectives as well, though: see Lassiter & Goodman 2013 for the details.)
Third, the present account is intended as an answer to the psychological question of how people
understand and use scalar adjectives. We do not propose an answer to the metaphysical questions
that have occupied much of the discussion of vagueness, involving when a scalar adjective really2

is applicable to an object, and to what degree. (The model is compatible with various proposals
about the latter questions, including a degree theory along Edgington’s lines, as we discuss briefly.)
On some plausible assumptions about the nature of meaning, the latter type of question should beilluminated — perhaps even resolved — by an answer to the former. However, we will not attempt
this philosophical project here.
1

Explananda

Scalar adjectives occur in a basic “positive” form — tall, short, happy, sad, heavy, light — as wellas various modified forms: exactly 4 feet tall, shorter than Harry, very happy, at least as sad asJane, extremely heavy, much lighter than a truck, etc. In this paper we focus on the positive form,
which is the usual example in discussions of adjectival vagueness. (It is certainly not the only kindof vague adjective, though: in the list above, very happy, extremely heavy, and much lighter thana truck are vague as well.) In the positive form, these adjectives display a number of interesting
empirical and theoretical properties which we will attempt to explain in a unified way.1
First, the meanings of relative adjectives in the positive form are highly context-dependent. A
cheap house is likely to be much more expensive than an expensive book. Similarly, the naturalinterpretation of “big” displays enormous variation among the following noun phrases: big microbe,
big finger, big baby, big football player, big tree, big building, big city, big planet, big star. The
driving force behind this variation seems to be the fact that these adjectives are interpreted in a“norm-related” way (Fara 2000): they indicate that the object that the noun phrase is predicated of
has a degree of the scalar property in question (cost, size) which is somehow significantly greaterthan the norm for a reference class (a.k.a. comparison class). More specifically, the interpretation ofthese adjectives is relativized to statistical properties of a reference class. So, for example, a house
which counts as “expensive” in Atlanta might be cheaper than a house which counts as “cheap” in
San Francisco, because house prices in San Francisco are generally much higher. The comparison
class is usually supplied implicitly, but can be explicit: for example, someone could be big for a
16-year-old but at the same time not big for a football player. On these properties of vague scalaradjectives see Kamp 1975; Klein 1980; Kennedy 2007; Bale 2011; Solt 2011; Lassiter 2015 among
many others.
Second, vague adjectives admit of borderline cases, as illustrated in (1).
(1)

[Almost all houses in this neighborhood cost $300,000-$600,000.]a. The Williams’ $1,000,000 house is expensive.
b. The Clarks’ $75,000 house is not expensive.
c. The Jacobsons’ $475,000 house is?
.

In this kind of context, speakers who are asked “Is the Jacobsons’ house expensive?” frequently
express uncertainty, hedge, or refuse to answer the question. This suggests that the Jacobsons’
1 More precisely, these are characteristics which are robust especially (perhaps only) for the relative adjectives on which
we focus here. Absolute adjectives such as full, empty, wet, dry, open, closed, safe, dangerous are somewhat different:
see Kennedy & McNally 2005; Kennedy 2007; Lassiter 2015; Morzycki to appear.

3

house is a borderline case of expensive — one for which neither “expensive” not “not expensive”
feels like an appropriate description.
Second, vague adjectives are susceptible to the sorites paradox.
(2)

a. A house that costs $10,000,000 is expensive (for this neighborhood).
b. A house that costs $1 less than a house that is expensive (for this neighborhood) is also
expensive (for this neighborhood).
c. ∴ A house that costs $1 is expensive (for this neighborhood).

Finally, there is a deep puzzle brought out by the observation that much of ordinary language isvague. If the meanings of vague expressions are indeterminate, how can they be used to communicate
meaningful information? The most precise and practically useful model of communication available
to us — the noisy-channel model due to Shannon (1948) — does not extend in an obvious way
to communication with expressions with indeterminate meanings. This could be construed as anargument against the application of information theory to the study of communication with natural
languages. We prefer to view it as a challenge to state a model of vagueness which is compatible
with the only realistic model of communication available, and which makes precise predictions
about what information vague terms convey — the mapping from prior to posterior — relative to acontext.
2

Bayesian Pragmatics

Throughout the paper we will assume a common variant of Montague’s (1973) framework forcompositional semantics, with modifications due to Gallin (1975) and the addition of a basic type dfor degrees/thresholds. We also simplify by ignoring intensionality where it is not directly relevant.
The details of our semantic assumptions will not play a major role, however: the main convention
that should be kept in mind is the assumption that the language is interpreted by a function J⋅K whichmaps English expressions to expressions in a simply typed λ -calculus.
Our pragmatic theory builds on accounts which emphasize the importance of coordination, inparticular developments of Grice 1957, 1989 on game-theoretic principles (Lewis 1969; Clark 1996;
Benz, Jäger & van Rooij 2005; Jäger 2007; Potts 2008; Franke 2009, 2011; Jäger & Ebert 2009).
We follow closely recent work in Bayesian pragmatics (Frank & Goodman 2012; Bergen, Goodman& Levy 2012; Goodman & Stuhlmüller 2012; Smith, Goodman & Frank 2013; Goodman & Lassiter2015), which combine Gricean and game-theoretic influences with an approach to inference anddecision-making under uncertainty which has been very influential in recent cognitive science (Pearl
2000; Griffiths, Kemp & Tenenbaum 2008; Tenenbaum et al. 2011). Related ideas can be foundin Golland, Liang & Klein 2010; Lassiter 2012; Franke 2012a,b; Kehler & Rohde 2013; Vogel,Bodoia, Potts & Jurafsky 2013a; Vogel, Potts & Jurafsky 2013b.
Bayesian models in cognitive science rely on two crucial formal assumptions, familiar from
Bayesian epistemology. First, an agent’s subjective uncertainty is represented as a probability
distribution P(⋅) over a set of propositions (sets of worlds). Second, the effect on the agent oflearning some proposition A is to cause her to adjust her prior probability distribution P(⋅) to aposterior P(⋅∣A), i.e., to employ Bayesian inference.

4

2.1

Bayesian inference in brief

This section reviews some basic notions in probability and Bayesian inference that will be used
repeatedly in later parts of the paper. Readers familiar with this apparatus will be able to skip tosection 2.2.
Probabilistic information states are an enrichment of the sets-of-worlds picture of information
states familiar from formal epistemology and formal pragmatics. The addition of a measure P(⋅)
over propositions allows us to make fine-grained epistemic statements, not only about what is
possible, impossible, or necessary, but also about what is more or less likely. P(⋅) is constrained asfollows:(3)

a. For all A ⊆ W , P(A) ∈ [0,1].
b. P(W ) = 1.
c. For any disjoint A,B ⊆ W , P(A ∪ B) = P(A) + P(B).

Second, Bayesian models assume that, upon learning some proposition A, agents update theirinformation state by conditioning on A, yielding a new probability distribution which continues toobey the constraints in (3).2(4)

P(B∣A) =P(B ∩ A)P(A)In many cases, it is more straightforward to calculate P(B∣A) using Bayes’ rule (but note that
this rule is a straightforward consequence of the definition of conditional probability in (4)). If
B = {B1 ,B2 ,...} is a partition of W , then we have:(5)

P(Bi ∣A) =P(A∣Bi ) × P(Bi )∣B∣∑ j=1 P(A∣B j ) × P(B j )Suppose that the elements of B are hypotheses about the process by which observation A wasgenerated. We can then think of the core Bayesian assumption as follows: the probability that
hidden cause Bi is true, given that we have made observation A, is proportional to the product of
two terms: (i) the probability that we would have observed A if hidden cause Bi were true, and (ii)the probability that we assigned to hidden cause Bi before we observed A.
(6)

P(Bi ∣A) ∝ P(A∣Bi ) × P(Bi )For convenience, we will sometimes write instances of Bayes’ rule in this form, without specifying
the full space of alternative hypotheses B j which we would have to consider in order to calculate
P(Bi ∣A). The missing denominator is a constant which will be the same regardless of the choice ofBi ; it is required to ensure that the resulting distribution sums to 1.
2 The ratio definition of conditional probability is convenient and simple, but not at all crucial for us. We would also becontent to take conditional probability as basic, as many philosophers of probability have recommended (e.g., Hájek
2003).

5

2.2

Motivation and assumptions

In applying this model to linguistic communication, we assume that speakers and listeners maintainprobabilistic models of each others’ utterance planning and interpretation processes, and that thesemodels drive pragmatic language use. In particular, listeners use their models of speakers’ utterance
choice to make more informed interpretive choices than would be possible if they simply updated
their information states with the information that the utterance’s semantic interpretation is true. The
model thus encodes back-and-forth pragmatic reasoning as pictured in Figure 1. Later in the paper
we will also propose a way for listeners to use a model of the speaker to resolve context-sensitivity
on Bayesian principles, by jointly inferring the state of the world and the values of semantic
variables.

Figure 1

Recursive pragmatic reasoning.

The most straightforward way to implement recursive reasoning of this type would be along
the following lines: a listener L updates her information state, given that some utterance has been
made, by reasoning about how the speaker would have chosen utterances or other actions in various
possible worlds, and weighting the result by the probability that those worlds are indeed actual.
(7)

PL (w∣u) ∝ PS (u∣w) × PL (w)Conversely, a speaker chooses utterances by reasoning about how the listener will interpret theutterance, together with some private utterance preferences PS (u) (representing, for example,
frequency effects or a preference for brevity and ease of retrieval).
(8)

PS (u∣w) ∝ PL (w∣u) × PS (u)These equations are both instantiations of Bayes’ rule. However, since they are mutually recursive
the reasoning could go on forever, unless we impose some bound. In addition, it is not obvious
where in (7) and (8) literal meaning, as studied in compositional semantics, intrudes (cf. Franke
2009: §1).
The solution adopted here goes back to chapter 1 of Lewis’s (1969) Convention: A PhilosophicalStudy. Iterated reasoning grounds out in first-order expectations about others’ likely actions, andlayers of reasoning about others’ reasoning are built on top of this basic expectation. Recently
this idea has been developed in multiple, partly overlapping lines of research in game-theoretic
pragmatics and in cognitive science (Franke 2009, 2011; Jäger & Ebert 2009; Xu & Tenenbaum
2007; Goodman & Stuhlmüller 2012; Frank & Goodman 2012; Goodman & Lassiter 2015).
In this paper we focus on a particularly simple version of this model: the interpretation processof a listener who uses literal interpretation as a base case and reasons to some finite depth, as
6described in detail by Franke (2009). The pragmatic listener L1 reasons about the utterance choicesof a simulated speaker S1 , who reasons about the interpretation of a literal listener L0 , who does
not reason pragmatically. The literal listener provides a hook for the compositional semantics toprovide conventialized semantic information to the pragmatic reasoning process. The model iseasily extended to speakers and listeners who reason to greater depths, but, as we will see, robust
pragmatic effects can arise already at level 1. A graphical depiction of the model is given in Figure
2. We describe these model components in detail in the next subsection.

Figure 2

Bounded pragmatic reasoning grounds out in first-order expectations.

Importantly, the non-maximal speaker and listener models — S1 and L0 below — exist only as
part of the pragmatic listener’s psychology: we do not want to commit to the existence of ultra-naïve
listeners, or to speakers who believe that they are speaking to such listeners.3 This point was madeby Lewis in his analysis of basic coordination behavior, where he pointed out that such iterated
reasoning
is not an interaction back and forth between people. It is a process in which one
person works out the consequences of his beliefs about the world—a world hebelieves to include other people who are working out the consequences of theirbeliefs, including their belief in other people who ... By our interaction in the world
we acquire various high-order expectations that can serve us as premises. In our
3 We will not address speaker modeling here, but our model does make predictions: to the extent that listeners do reason
to level 1, a reflective speaker should choose utterances by reference to an L1 model who is reasoning about an S1 who
is reasoning about an L0 . See also Qing & Franke 2014 for a model of vague interpretation which builds on the one
described here, but places greater emphasis on speaker modeling.

7

subsequent reasoning we are windowless monads doing our best to mirror eachother, mirror each other mirroring each other, and so on. (Lewis 1969: 32)
2.3

Literal listener

The literal listener L0 is defined as an agent who responds to an utterance u in two steps: calculate
JuK, the literal interpretation of u in the relevant language, and condition the prior information stateon the truth of JuK.
PL0 (A∣u) = PL0 (A∣JuK = 1)(9)

L0 is essentially a probabilistic version of the interpreter discussed by Stalnaker (1978) and inmuch work in dynamic semantics, who responds to utterances by simply assuming that they aretrue. There is even a close relationship between the update operations: Stalnakerian update is set
intersection, and conditionalization is equivalent to intersection followed by renormalization of
the measure. (We consider the case in which JuK contains free variables below. See Goodman &Lassiter 2015 for discussion of lexical and syntactic ambiguities, which we do not deal with here.)
2.4

Speaker model

We model a generic speaker S1 as an agent who attempts to make statements which are informative
relative to the current topic of conversation/Question Under Discussion (QUD: Ginzburg 1995a,b;
Roberts 1996). Equivalently, the conversation determines a random variable for which the speakerknows the true value, but the listener does not, and attempts to select an utterance which will transmit
as much information as possible about this variable. The random variable/QUD-relativity of the
speaker model is important because we do not wish to predict that speakers will say things that arehighly informative in a global sense if they are irrelevant to the current conversation. In our model,
the QUD provides a set of possible answers A over which the informativity of a potential utterance
is calculated. A QUD might be “Who came to the party?”, or “How many people came?”, or “Howtall is Al?”. The choice of QUD is constrained, but not fully determined, by overt questions, theinformation structure (e.g., prosody) of the utterance, and various other aspects of the history ofthe discourse. When further specification is required, we would opt for an expansion of the model
presented here to include inference of the QUD, as discussed by Kao, Wu, Bergen & Goodman(2014). We will not deal with this additional complication here, though.4
The speaker and listener share the goal of coordinating utterance and interpretation so as tomaximize the probability that the listener will infer the correct answer to the QUD. We thus define
the utility of u for speaker S1 to be proportional to its informativity to the literal listener L0 about the
4 On the equivalence between random variables and question denotations (on the Groenendijk & Stokhof 1984 interpretation), see van Rooij 2003. Note in particular that an answer A is just a set of possible worlds, i.e., a proposition which isa cell in the partition which the question denotes.
A notable simplification in our model is the assumption that the speaker knows the true answer with certainty. This
assumption could be relaxed either by allowing that the speaker samples a possible world from his personal probability
distribution and then proceeds with the calculations described here, or by using expected informativity instead of simple
informativity in the utility function. For relevant discussion see Goodman & Lassiter 2015.

8

true answer A, minus a non-negative cost C(u). Following Frank & Goodman (2012), we quantify
the informativity of u as the negative surprisal (positive log probability, Shannon 1948) of the true
answer for L0 , once L0 has conditioned on the literal truth of the utterance. In addition, there is aterm C(u) representing the intrinsic cost of producing u for the speaker. Relevant factors might
include difficulty of articulation and difficulty of retrieval. Here we assume that cost increases
monotonically with difficulty of articulation, which we approximate very roughly as length inwords.
For the S1 model this gives us the following utility function, where US1 (u;A) is the utility ofutterance u for speaker S1 on the assumption that the answer to the QUD is A.
(10)

US1 (u;A) = ln(PL0 (A∣u)) −C(u)With this utility function in hand we turn to a specification of the speaker’s choices given somepossible utterances u, each with a utility US1 (u;A). Most work in decision theory and game theory
assumes that agents deterministically choose the action with the highest utility, or choose randomly
among maximal options if there is a tie. We employ a relaxed version of this model according towhich agents choose stochastically, i.e., that speakers sample actions with the probability of making
a choice increasing monotonically with its utility. Soft-max choice rules of this type are widely
employed in psychology and machine learning (Luce 1959; Sutton & Barto 1998). Apparently
sub-optimal choice rules of this type have considerable psychological motivation. They can also be
rationalized in terms of optimal behavior for an agent whose computational abilities are bounded bytime and resource constraints, but who can efficiently approximate optimal choices by samplingfrom a probability distribution (Vul, Goodman, Griffiths & Tenenbaum 2014).
(11)

PS1 (u∣A) ∝ exp(α × US1 (u;A))

This choice rule has a parameter α > 0 which determines how closely stochastic choice approximates
deterministic utility-maximization. With α = ∞, we would recover the choice rule typically usedin game theory. In simulations reported below we set α to a lowish value of 4. The qualitative
results reported are not extremely sensitive to the value of this parameter, though very high andvery low settings would yield (respectively) over- and under-informative interpretations for vague
expressions.
We must assume a space of alternative utterances u′ ∈ ALT in order to find the normalizing
constant for (11).
(12)

PS1 (u∣A) =exp(α × US1 (u;A))∑ exp(α × US1 (u′ ;A))u′ ∈ALT

This choice can influence the qualitative behavior of the model, and it is not currently wellinvestigated from an empirical or computational perspective. For present purposes we will assume(essentially following Fox & Katzir (2011)) that the alternative utterances considered are a subsetof the possible answers to the QUD. We will also assume that speakers have the option of saying
nothing. In particular, we will assume that a sentence with a scalar adjective such as Al is tall isinterpreted by consulting two alternatives: the utterance Al is short and the action of saying nothing9(∅). Similarly, I ate some of your cookies might be evaluated relative to the simplified alternativeset {I ate some of your cookies, I ate all of your cookies,∅}. The qualitative results reported below
do not differ in major respects if we also consider other well-chosen alternatives, e.g. Al is verytall/short and Al is medium height; however, it is possible to induce counter-intuitive model behavior
with certain alternative sets. A major desideratum in future work will be to get a clearer pictureof how speakers and listeners choose a realistic but manageable set of alternatives for pragmatic
reasoning, and more generally how people choose an action set under relatively unconstrained
conditions.
2.5

Pragmatic listener

The pragmatic listener L1 interprets utterances u using Bayesian inference, assigning to each A a
probability proportional to the product of (a) the probability that the speaker would have chosen to
employ u if A were the true answer, and (b) the prior probability that A is true.
(13)

PL1 (A∣u) ∝ PS1 (u∣A) × PL1 (A)PL1 (A) specifies L1 ’s background knowledge about answers to the QUD. For example, if the QUD
is How tall is Al? and L1 knows only that Al is an adult man, then PL1 (A) is an estimate of the
distribution of heights among adult men. (14) is normalized by the total posterior probability of all
possible answers A′ given that u was in fact chosen.
(14)

PL1 (A∣u) =PS1 (u∣A) × PL1 (A)∑ PS1 (u∣A′ ) × PL1 (A′ )A′

3

Example application: Scalar implicature

To illustrate the workings of the model, we work through a simple scalar implicature example indetail, showing how the model accounts for defeasible pragmatic enrichment in a case where freevariables are not relevant.5 The next section will show how to adapt this pragmatic reasoning toinfer the value of the free threshold variable that is present in vague scalar adjectives.
Emma is saving 6 cookies for dessert — two for each member of her family. She leaves themon the kitchen counter while she goes to the bank. She calls home while she is waiting in line. Her
husband Dan reports to her: “Charlie ate some of the cookies.” From the literal meaning of thissentence, there is a very strong inference that (assuming Dan is reliable and well-informed) the
number of cookies Charlie ate is greater than zero. Emma will typically acquire more information
than this, though: she will also learn that the number he ate is less than six, i.e., that he didn’t eat allof the cookies. Of course this is a defeasible inference — conceivably, Dan is lying or confused.
5 Plausibly, there is a domain restriction variable in the quantifier’s meaning which must be inferred (Stanley & Szabó
2000). In the case at hand, the domain restriction does not vary across the alternatives under consideration, and so
we can safely ignore this variable. However, there are many cases in which this variable is not clearly given, and its
inference will likely interact with other aspects of the pragmatic reasoning considered here.

10Nevertheless, given what Dan said, Emma can reasonably expect some dessert to be left when she
gets home.
Where does this additional, defeasible information come from? The usual story in the pragmatics
literature, going back to Grice (1975), goes roughly as follows. Emma reasons that, if Charlie had
eaten none of the cookies, Dan — being a reliable type — would have said “Charlie ate none of thecookies”. If Charlie had eaten all of the cookies, Dan could truthfully say “Charlie ate some of thecookies”, as he did; but he could also have said “Charlie ate all of the cookies”, and this utterance
would have been strictly more informative about the family’s dessert prospects. Furthermore, there
is no obvious alternative explanation of why Dan would have chosen to say “some” if “all” were
true: they are about equally effortful, and no obvious considerations of (e.g.) politeness seem to be
relevant. So, given Dan’s choice to use the sentence with “some”, Emma concludes (tentatively anddefeasibly) that the sentence with “all” replacing “some” would not be true. So, Charlie ate some of
the cookies, but he didn’t eat all of them: at least one is left.
The key to the above reasoning is that the listener enriches the interpretation beyond the literalmeaning on the basis of a rationalization of the speaker’s observed choice, based on a backgroundmodel of the speaker’s decision-making processes. Bayesian back-and-forth reasoning captures theinformal Gricean explanation in a precise way. It also explains the defeasibility of this pragmatic
inference, since the result is a probabilistic inference about the speaker’s choices in various possible
scenarios, and choice is assumed to be stochastic rather than deterministic.
Let the QUD be How many cookies did Charlie eat?, and let L1 be Emma, whose interpretation
process invokes a simulated version S1 of Dan. Emma must have assumed that Charlie wouldn’t eat
any of her cookies (otherwise, she wouldn’t have left them unprotected), but we can imagine that she
had no expectations about how many he would eat if he did have some. So, let the prior probabilityof Charlie ate n cookies be high for n = 0 and distributed uniformly over n ∈ {1,2,3,4,5,6}.
⎧⎪⎪.94 if n = 0
PL0/1 (A = Charlie ate n cookies) = ⎨⎪⎪⎩.01 if n ∈ {1,2,3,4,5,6}(15)

Fix a set of alternative utterances ALT = {NONE, SOME, ALL}. Referring to equation (??), we
see that, in order to find the posterior PL1 (A∣u), we need to know PS1 (u′ ∣A) for each u′ in this ALT.
Recall (combining equations (10) and (11)) that this quantity can be found as
PS1 (u′ ∣A) ∝ exp(α × [ln(PL0 (A∣u′ )) −C(u′ )]).

(16)

To illustrate the qualitative pattern, let’s set α = 4 and C(u) = 2/3 × length(u), where length ismeasured in words. So, C(u′ ) = 4 for all u′ ∈ ALT. Then L1 goes through the following reasoning inattempting to rationalize Dan’s choice to utter u = SOME (i.e., Charlie ate some of the cookies).
All of the conclusions described here are summarized in graph form in Figures 3 and 4, which thereader may wish to consult as she works through the example.
Suppose n = 0.

The truth is that A = Charlie ate 0 cookies.

• Since NONE entails n = 0 and excludes n > 0, the literal listener’s posterior probability ofthe true state n = 0 given u = none is PL0 (n = 0∣u = NONE) = 1.
11• Since SOME entails n > 0, PL0 (n = 0∣u = SOME) = 0.
• Since ALL entails n > 0, PL0 (n = 0∣u = ALL) = 0.
Plugging these results into equation (16), we find:• PS1 (NONE∣n = 0) ∝ exp(4 × [ln(1) − 4]) ≈ 1.1 × 10−7 .
• PS1 (SOME∣n = 0) ∝ exp(4 × [ln(0) − 4]) = 0.
• PS1 (ALL∣n = 0) ∝ exp(4 × [ln(0) − 4]) = 0.
The normalized probability that S1 will produce each utterance is computed by dividing its nonnormalized probability by the sum of the non-normalized probabilities of all u′ ∈ ALT. This sum isapproximately 1.1 × 10−7 + 0 + 0 = 1.1 × 10−7 . So, PS1 (NONE∣n = 0) = (1.1 × 10−7 )/(1.1 × 10−7 ) = 1,
and SOME and ALL have probability zero under this scenario.
In other words, S1 might say NONE if n = 0 and definitely wouldn’t say SOME or ALL, sincethese utterances would lead the literal listener to assign probability zero to the true state. So, of thealternatives that the pragmatic L1 is considering in attempting to rationalize the observed utterance,
only NONE would be possible if n = 0. Given this, the posterior probability of n = 0 for L1 , given
the observed utterance SOME, will be(17)

PL1 (n = 0∣u = SOME) ∝ PS1 (u = SOME∣n = 0) × PL1 (n = 0)
= 0 × .94
=0Suppose n = 1. Here NONE and ALL exclude the true state, so PL0 (n = 1∣u = NONE) = PL0 (n =1∣u = ALL) = 0. In the case of SOME, we can find the literal listener’s posterior probability as theprior probability normalized by the total probability of the states where this utterance is true, using
the information in (15).

(18)

PL0 (n = 1∣u = SOME) = PL0 (n = 1∣JSOMEK = 1)
= PL0 (n = 1∣n ∈ {1,2,3,4,5,6})= PL0 (n = 1)/PL0 (n ∈ {1,2,3,4,5,6})= .01/(6 × .01)
= 1/6

Now we can plug this calculation into the speaker model:• PS1 (NONE∣n = 1) ∝ exp(4 × [ln(0) − 4]) = 0.
• PS1 (SOME∣n = 1) ∝ exp(4 × [ln(1/6) − 4]) ≈ 8.7 × 10−11 .
• PS1 (ALL∣n = 1) ∝ exp(4 × [ln(0) − 4]) = 0.

12And since again only one option has positive probability, the normalized probabilities are 0, 1, and 0
respectively. This information is used by the pragmatic listener to find the non-normalized posterior
probability of n = 1 given SOME:(19)

PL1 (n = 1∣u = SOME) ∝ PS1 (u = SOME∣n = 1) × PL1 (n = 1)
= 1 × .01
= .01

Suppose n ∈ {2,3,4,5}. These are precisely parallel to n = 1, since NONE and ALL are false in
all of these scenarios as well, and their prior probabilities are equal to that of n = 1. Each of thesescenarios has posterior probability proportional to .01.
Suppose n = 6. Now things get interesting: we have two true utterances in our alternative set,
SOME and ALL. If u = SOME, then L0 ’s posterior probability of the true state will be 1/6, for thesame reasons spelled out above: L0 simply conditions on the truth of SOME, and this is true whenn = 6. However, suppose that u = ALL: then only n = 6 is compatible with the observed utterance,and so L0 will assign probability 1 to n = 6 and 0 to all other states. As a result, there is a huge
difference in informativity between SOME and ALL — the true state has probability six times higher
if u = ALL than it does if u = SOME.
This asymmetry leads to a large difference in S1 ’s production probabilities when n = 6:
• PS1 (NONE∣n = 6) ∝ exp(4 × [ln(0) − 4]) = 0.
• PS1 (SOME∣n = 6) ∝ exp(4 × [ln(1/6) − 4]) ≈ 8.7 × 10−11 .
• PS1 (ALL∣n = 6) ∝ exp(4 × [ln(1) − 4]) ≈ 1.1 × 10−7 .
As a function of the informativity difference, S1 is much more likely to produce ALL than SOME
when ALL is true. The normalized probabilities are• PS1 (NONE∣n = 6) = 0.
• PS1 (SOME∣n = 6) ≈ .001.
• PS1 (ALL∣n = 6) ≈ .999.
That is, a speaker who is trying to be informative almost certainly won’t use SOME if they have theoption to use ALL and ALL is true.6 Using this information, the pragmatic speaker L1 can derive
6 Note that the precise numerical values derived here depend on the Luce choice parameter α, here set to 4. The qualitative
preference for ALL does not depend on this parameter, though. For example, with α = 10, the dispreference for ALL oversome when n = 6 would not be on the order of .001/.999, but a much stronger [1.7 × 10−8 ]/[1 − 1.7 × 10−8 ]—almost
indistinguishable from a deterministic preference for the maximum utility choice. With α = 1, the dispreference for
SOME has the more moderate value .16/.84. Ultimately we can only fit this model parameter to data, or attempt tomanipulate it qualitatively in an experimental setting.

13

much more information about the true state than a literal listener could, using the observation thatthe utterance chosen was SOME.
PL1 (n = 6∣u = SOME) =(20)

PS1 (u=SOME∣n=6)×PL1 (n=6)∑ PS1 (u=SOME∣n′ )×PL1 (n′ )n′ ∈{0,...,6}.001×.01≈ 0+.01+.01+.01+.01+.01+.001×.01≈ .015

L1 ’s posterior probabilities for the other states (1-5) are much higher, around .197 each.
Upshot. The posterior probability of n = 6, given the observed utterance SOME, is much lower
for the pragmatic listener L1 than it is for a literal listener. The difference is driven by the fact thatthe pragmatic listener considers not only what the speaker actually chose to say, but also otherthings that the speaker could have chosen. The pragmatic listener reasons about the latent causes ofthe speaker’s observed choice, using a model which predicts what choices would likely have been
observed given various possible configurations of the latent causes (states of the world). This typeof reasoning derives the intuitive inference that n is probably not six when SOME is used — i.e.,that some strongly implicates not all.

Figure 3

Posterior distributions of the literal (left) and pragmatic (right) listeners, given thevarious possible utterances under consideration. When u = SOME, the pragmatic
listener uses the speaker’s utterance preferences (Figure 4) to draw a strong “not all”
inference.

Figures 3 and 4 summarize the results just described. Note that the difference between the L0
and L1 posteriors in Figure 3 lies entirely in P(n∣u = SOME): the probability that n = 6 is much lower
at L1 , with the leftover probability divided evenly among the other true states 1-5. This difference isdriven by the fact that L1 ’s reasoning about the actual state n is “fed through” the speaker model in
Figure 4, in which there is a large difference in the probability of using SOME vs. ALL in this state.
14

Figure 44Speaker probability of selecting utterance u, given that the number of cookies eaten
is n, for various u and n. The SOME-related difference between the listener models
in Figure 3 is driven by the fact that P(u = SOME∣n = 6) is almost, but not quite, zero.

Application to scalar adjectives

Unlike quantity implicatures triggered by the use of quantifier phrases, the interpretation of expressions containing scalar adjectives crucially requires a listener to fill in a free threshold variablewhich is left unspecified in the adjective’s semantic interpretation. Here we describe briefly thesemantic motivation for this claim, and then describe an expanded pragmatic model in which thepragmatic listener estimates the values of free variables in much the same way that she estimatedthe underlying world state in the scalar implicature example just reviewed. The final interpretationreflects a balance between two countervailing pressures: the listener’s preference for interpretations
which are likely to be true, and the speaker’s preference for interpretations that are informative.
In our model, this balancing process is responsible for the extreme sensitivity of scalar adjectives’contextual meanings to statistical priors.
4.1

Semantic backgroundWe adopt a degree semantics in which scalar adjectives relate individuals to a threshold value,schematically:(21) JAK = λ θA λ x[µA (x) > θA ]

θA is a degree on A’s scale—the threshold—and µA (x) is the measure of x on this scale. For
example, if A is tall, then the scale is the range of possible heights (0,∞), and µA is a function
mapping objects to values in this range. The adjective denotes a function which receives a thresholdargument (say, 3 feet) and an individual argument, returning true if the associated measure function

15

returns a value which exceeds the threshold when applied to the argument.7
We assume that the lexical entry of a gradable adjective contains two components: a specificationof the relevant scale — an ordered set of degrees — and an indication of the adjective’s polarity
along that scale. That is, antonym pairs such as tall/short and dangerous/safe live on scales which
are identical except that the ordering is reversed. Since we are reasoning only about positive-form
adjectives here, we can simplify by maintaining the scale’s intrinsic ordering but reversing thedirection of the comparison.
(22) JtallK = λ θtall λ x[µheight (x) > θtall ](23) JshortK = λ θshort λ x[µheight (x) < θshort ]Note that we are assuming that tall and short have unrelated thresholds, and so that their meanings
are independent except for their shared but inverted scales.8
In formal semantics the threshold is usually treated as an argument rather than a contextual
parameter because this choice makes it simple to account for uses in which the threshold issemantically rather than pragmatically controlled, such as two feet tall or taller than Charlie is.
However, the result of its presence in (21)-(23) is that we cannot directly compose tall and Al
in order to form a sentence such as Al is tall. A fairly standard solution to this problem is toposit a silent morpheme POS which binds the value of θtall to a contextual parameter stall (e.g.,
von Stechow 1984; Kennedy 2007). For this approach we need to assume that each adjective A
is supplied with a dedicated contextual parameter sA , or some variable-assigning function which
determines one.
(24)

a. JtallKstall ,sbig ,sheavy ,... = λ θtall λ x[µtall (x) > θtall ]b. JPOSKstall ,sbig ,sheavy ,... = λ Aλ x[A(sA )(x)]c. JPOS tallKstall ,sbig ,sheavy ,... = λ x[µtall (x) > stall ]d. JAl is POS tallKstall ,sbig ,sheavy ,... = µtall (Al) > stall

The result is that Al is tall is true iff Al’s height is greater than stall , whatever that is.
An alternative account is to treat POS as a type-shifter which reverses the order of the arguments,
as in (25). (Additional type-shifters would be needed to pass up the unsaturated variable in nonpredicative uses and non-matrix contexts; the intended account is a generalization of the treatmentof free anaphors in variable-free semantics (Jacobson 1999).)(25)

a. JtallK = λ θtall λ x[µtall (x) > θtall ]b. JPOSK = λ Aλ xλ θA [A(θA )(x)]

7 This is a ⟨d,⟨e,t⟩⟩ semantics for adjectives in the style of von Stechow (1984). For current purposes, it does not matter
whether we use this analysis or an ⟨e,d⟩ treatment as recommended by Bartsch & Vennemann (1973); Kennedy (1997,2007). The only modification needed would be in the definition of the POS morpheme/type-shifter.
8 In the context of our pragmatic model, assuming two unrelated thresholds for tall and short is enough to derivereasonable interpretations, as we will see below; it even derives the fact that tall and short are contraries without
stipulation. We could, if we wanted, add a lexical stipulation to this effect (θtall ≥ θshort ), but we do not know of any
compelling reason to do so. It would also be possible to assume that these expressions are contradictories (θtall = θshort ,so that short ≡ not tall). Whether the latter assumption is reasonable is a matter of debate: see Horn 1989; Heim 2006,
2008; Büring 2007a,b among many others for arguments pro and con.

16

c. JPOS tallK = λ xλ θtall [µtall (x) > θtall ]d. JAl is POS tallK = λ θtall [µtall (Al) > θtall ]These accounts are really not very different: both provide, in compositional fashion, a sentence
meaning which does not determine a truth-value until the value of a certain free/unsaturated variable
is determined. In either case, pragmatic inference is required to determine which proposition thesentence expresses. Our approach could also be modified for theories which countenance degrees
only in the metalanguage (Lewis 1970; Barker 2002) or not at all (Klein 1980), but we will not spellout the details here. (Indeed, we believe that the depth of the differences between these approaches
has been exaggerated in the literature: see Klein 1991; Lassiter 2015 for relevant discussion.)
4.2

Bayesian inference of free variables

Information from the compositional semantics—such as the semantics for adjectives just described—
enters into our pragmatic model via equation (9), which instructs the literal listener to condition on
the truth of the observed utterance.
(26)

PL0 (A∣u) = PL0 (A∣JuK = 1)

But conditioning is defined only for propositions, and so this model is only appropriate if JuK
contains no free variables. That is, L0 cannot condition on the truth of an utterance like He is in
Paris without first filling in a referent for he. Similarly, L0 cannot condition on the truth of Al istall without first filling in the threshold value θtall which specifies how tall one must be in order
to count as tall. Once this is done, for whatever value of θtall is supplied, L0 can condition on theinformation that Al’s height is greater than θtall .
More generally, if u’s meaning does make reference to some set of free variables V , the listenermust find some way to infer values for these variables. We propose to estimate these values by
having the pragmatic listener consider as possible interpretations all possible literal meanings that
could be expressed by an utterance, relative to all possible assignments of values to variables. Thatis, our pragmatic listener will now instantiate assignments of values V from a prior distribution
PL1 (V ) and thread them through the iterated reasoning procedure, considering how likely it is thatthe speaker would have produced the utterance that he did, if the variables were resolved as they arein V.9Let V be a function which assigns values to all variables in the language, and let JuKV be thelanguage’s interpretation function as parametrized by V . The literal listener is as in the simple
model, except that he conditions on JuKV , given a value of V which is provided by the speaker
9 We do not, of course, want to claim that listeners making such inferences in real time actually consider an infinite set of⟨A,V ⟩ pairs as hypotheses. The problem is, however, a very general one about how humans make inferences with a verylarge hypothesis space—something that we are surprisingly good at. The model presented in this paper is a high-levelcomputational theory for which a variety of techniques are available that can make inference tractable: this includes
lazy computation (reason only about variables that are actually present in the utterance and need to be inferred) andMarkov Chain Monte Carlo techniques such as the one that we use below to simulate Bayesian posteriors. For further
discussion see, for example, Griffiths, Vul & Sanborn 2012; Vul et al. 2014; Goodman & Tenenbaum electronic.

17

model.
(27)

PL0 (A∣u,V ) = PL0 (A∣JuKV = 1)

The variable-sensitive S1 model also takes a given value of V and produces utterances stochastically,on the assumption that the variables are valued as they are in V .
(28)

PS1 (u∣A,V ) ∝ exp(α × ln [PL0 (A∣u,V ) −C(u)])

The pragmatic listener then derives a variable-sensitive interpretation by considering how likely it isthat the speaker would have said u if the answer were A and the variables were as in V — and, as
usual, multiplying this value by the prior probabilities of A and V . This gives us a function whichassigns a joint posterior probability to all possible combinations of A and V .10(29)

PL1 (A,V ∣u) ∝ PS1 (u∣A,V ) × PL1 (A) × PL1 (V )For example, if the utterance is “Al is tall” V will determine a value for θtall and no otherrelevant variable. L1 ’s pragmatic interpretation will thus proceed by considering, for θtall ∈ (0,∞)
and heights h ∈ (0,∞), how likely it is that the speaker would have said this if “tall” were interpreted
as meaning “taller than θtall ”. (Note, by the way, that this is not a claim about the algorithm bywhich this computation is implemented: a well-designed algorithm for computing this inference
will not waste effort considering heights and values of θtall which are so far beyond the normal
range of heights for the class of objects in question that they cannot possibly be relevant to the issue
at hand. See also footnote 9.)
The prior term PL1 (V ) specifies L1 ’s background knowledge about the interpretation of freevariables. We will make the assumption that the listener has no relevant background knowledge
about the resolution of free variables, and so no reason to favor any choice of V . PL0/1 (V ) is thus
uniform for all possible combinations of values for the elements of V , and the PLn (V ) terms drop outof the reasoning everywhere.11 The assumption of uniform priors on semantic variables means that,
if u = Al is tall, all possible thresholds for tall are equally good candidates a priori; we do not, forexample, build in a preference for interpretations which are statistically more frequent in uses of tall.
This assumption seems to be justified at least in the case of scalar adjectives, where non-uniform
priors would limit the flexibility of interpretation: in other words, if PL1 (θtall ) were strongly biased
toward human-like heights, this bias would influence the interpretation of tall skyscraper in strange
ways. We eliminate this possibility by employing uniform priors. However, it is an empiricalquestion whether this assumption holds for all types of free variables that occur in natural languages.
10 We are assuming that A and V are independent for the listener in the prior, and are related only via the interpretation
process.
Note that we could continue to iterate to some higher Ln and pass the variable up. It is also possible to marginalize
at non-maximal levels. We have found that, under many conditions, scalar adjectives receive implausibly weakinterpretations if marginalization happens at L0 (Goodman & Lassiter 2015). However, many other possibilities remain
to be explored, e.g., a level-3 pragmatic intepreter with level-1 or -2 marginalization.
11 Note that PL0/1 (V ) is an improper prior if the range of V extends to infinity in either direction.

18

4.3

Scalar adjective interpretation: The intuition

To get an intuition about how the various model components play into scalar adjective interpretation,
we will consider in detail several sample interpretations against the following background: u is “Alis tall”, we know nothing about Al except that he is an adult man, and ALT = {Al is tall, Al is short,∅}.
• Consider very large values of θtall , for instance 7 feet. In this case the probability of theinterpretation is low because of the prior on heights: it is very unlikely that Al is more than
7 feet tall, and our model encodes an assumption that speakers do not make false utterances.
This fact leads to a low probability of this interpretation even though, if Al were in fact taller
than 7 feet, the utterance would be extremely informative; in this case, the strength of thedispreference generated by u’s low prior probability of truth (low P(h > 7 feet) outweighs
the informativity preference, leading to a low posterior density of θtall = 7 feet.
• Consider very low values of θtall , for instance 1 foot. In this case the interpretation receives very low probability because of the speaker’s preference for informative utterances.
Specifically, the probability that “Al is tall” is true relative to this tall-threshold is effectively
1: all adult men are more than 1 foot tall. However, the probability that the speaker willproduce this utterance is very low, because conditioning on the truth of a known proposition does not influence one’s probability distribution. Thus the posterior distribution
PL0 (h∣u = “Al is tall”,θtall = 1 foot) is effectively the same as the prior, and no information isconveyed. In this case, the speaker will prefer to say nothing, since this is at least a cost-free
way to convey no information. In other words, the speaker’s observed choice to utter “Al istall” can only be rationalized if it increases the probability of the true answer considerably,
relative to what the prior probability of this answer is, and low values of θtall do not meetthis requirement.
• Suppose θtall has some intermediate value, say, 6 feet. Then we have a reasonable compromise: saying “Al is tall” will convey a reasonable amount of information, but the priorprobability that Al’s height is greater than 6 feet is not so low that the pragmatic listenerwill discount it as probably false.
More generally, the joint posterior on interpretations and answers to the QUD that this model derives
reflects a balancing process between the speaker’s informativity preference and the listener’s beliefs
about which utterances would be true. This results in a probabilistic “sweet spot” interpretation forscalar adjectives which is highly sensitive to the statistical information encoded in the prior. Veryweak interpretations, with θtall falling in the lower region of the height prior, are probably true;
however, the informativity preference entails that speaker would probably not have chosen to use
the utterance in such a situation. Conversely, very strong interpretations (with θtall in the extreme
upper tail of the height prior) are dispreferred because they make the utterance very likely to befalse — even though they would be extremely informative if true. The effect is a preference forinterpretations which make Al fairly tall, but not implausibly so.

19

4.4

Scalar adjective interpretation: Simulations

The simulated marginal posteriors of h = height(Al) and θtall , given the utterance u = “Al is tall”,
are plotted in Figure 5. This and all following simulations use Metropolis-Hastings, a Markov
Chain Monte Carlo algorithm (Neal 1993; MacKay 2003), to approximate the posterior since it is
not possible to solve the model analytically. As above, we set α = 4 and C(u) = 2/3 × length(u),with length measured in number of words: for example, C(Sam is tall) = 2. The action ∅ (saying
nothing) has cost 0. We use ALT = { Apos , Aneg , ∅}, where Apos and Aneg are an antonym pair suchas tall/short.
All plots below show the marginal kernel densities of A and θtall in our samples, along with
the input priors PL0/1 (h) and PL0/1 (θtall ).12 These are approximations to the the marginal posterior
density PL1 (θtall ∣u) and PL1 (h∣u), as described by equations 30 and 31 respectively.
(30)

PL1 (θtall ∣u) = ∫(31)

PL1 (h∣u) = ∫

0

∞
0

∞

PL1 (θtall , h∣u = “Al is tall”) dh

PL1 (θtall , h∣u = “Al is tall”) dθtall

We assume that heights (e.g., the heights of adult men) are approximately normally distributed:
P(h) = N(µ,σ ) for a specified mean µ and standard deviation σ .13 The choice of µ and σ matter
only for scale: multiplying these parameters by a constant has the effect of multiplying posteriors
by the same constant (cf. Figure 7).
The inferred meaning of “tall” is essentially “significantly greater than average height”, an
intuition which has been expressed in the literature in various forms (e.g., Fara 2000; Kennedy2007). Crucially, though, the resulting interpretation remains vague: the interpretation process takes
us from knowing nothing about the contextual meaning of “tall” to knowing quite a lot, but there is
still remnant uncertainty in PL1 (θtall ∣u).
Since the lexical entries of tall and short in (22) and (23) differ only in the direction of thecomparison with their estimated threshold, and since the prior is symmetric, we should expect theinterpretation of Al is short to be symmetric along the prior mean. This is indeed the case: see
Figure 6.
These simulations shed light on several of the explananda discussed in section 1. There webriefly described four puzzles: information transmission despite uncertainty about interpretation,
context-dependence, borderline cases, and the sorites. This model accounts for the possibility of
12 The kernel density is a nonparametric estimate of the density of a continuous function from a finite number of samples,
given certain assumptions about the smoothness of the function. See, for example, Silverman 1986.
Simulations took 5,000,000 samples from PL1 (h,θtall ∣ u) with a burn-in of 5000 (i.e., the first 5000 samples werediscarded because to avoid dependence on the starting point of the simulation). Unlike Lassiter & Goodman (2013)
we do not rescale variables to fall within [0,1], since we are not concerned with the difference between bounded andunbounded scales here.
13 “Approximately” because the normal distribution has support over the entire real line, but it does not make sense totalk about heights less than zero. However, for the priors that we will consider, only a negligible portion of the priorprobability mass falls below zero.

20

Figure 5

Simulated posterior given utterance “Al is tall”, plotting the marginals of tallthresholds and answers (heights) separately. The model predicts several core properties of relative adjectives: significant information conveyed despite vague meaning,sensitivity to statistical priors, the existence of borderline cases, and a plausible
account of the sorites.

information transmission using vague expressions in a precise way. In information theory (Shannon
1948), transmission of information is interpreted as modification of a probability distribution. Theinterpretation of a vague adjective in our model is a function from priors to posteriors, and clearlysignificant information has been gained in the interpretation: the posteriors on Al’s heights in Figures
5 and 6 are shifted substantially away from the prior, and have lower variance. In other words, eventhough the meaning of “tall” remains uncertain, a listener can gain significant information about theworld when a speaker uses it to describe something.
Second, the context-sensitivity of vague scalar adjectives is predicted because the interpretation
process is highly sensitive to the form of the input prior. In Figure 5, for example, the inferredmeaning of tall is a distribution centered around 1.3 standard deviations above the prior mean. Since
the scale in these simulations is arbitrary, the model predicts that a normal prior with a different meanand standard deviation would lead to a qualitatively similar but quantitatively different posterior,differing in mean and variance. Figure 7 illustrates how interpretation is affected when we interpret“tall” relative to two prior distributions with equal variance but different means.
This style of interpretation helps us to understand the sense in which the concept “tall” hasa stable meaning, even though its interpretation can vary widely in different contexts (tall boy,tall tree, tall building, etc.). Assuming that heights are normally distributed in each class but
differ in mean and standard deviation, the posterior on θtall and A will be shifted accordingly,
while maintaining the same shape relative to the prior mean and standard deviation. Backgroundknowledge, in the form of a statistical prior on answers to the QUD, thus interacts with lexical
meaning and the pragmatic preference for informativity to yield a context-sensitive probabilistic
meaning.
Third, borderline cases of “tall” are individuals whose probability of counting as “tall” isintermediate. This is itself vague, of course, but that is as it should be given the existence of21

Figure 6

Threshold and degree priors and simulated posteriors given utterance “Al is short”.

higher-order vagueness.14 The key problem, though, it to fix what the probability of counting as“tall” is — or, more precisely, how to interpret “the probability that an individual with height hcounts as ‘tall”’. We suggest the following interpretation, where PT is the metalinguistic probability
that we, as theorists of vagueness, feel appropriate to assign here:(32)

height(a)PT (a is tall) = ∫0PL1 (θtall ∣u = “a is tall”) dθtall .

The intuition behind equation 32 is this. If we know an individual’s height and want to know how
appropriate it would be to describe him as “tall”, we imagine ourselves using “tall” in communicatingwith a listener with an appropriate prior distribution PL0/1 (h). Using the interpretation that thislistener would arrive to resolve the underspecified meaning of “tall”, we then compute the probability
that the utterance is true relative to the context-sensitive posterior on θtall that this L1 derives.15
In the case at hand (Figure 5), this means finding the area under the curve of the thresholdposterior which falls to the left of a’s height. If a is 5 feet 9 inches, then a probably does not countas “tall” (specifically, PT (a is tall) ≈ .02). If a is 6 feet 6 inches tall, then a almost certainly counts
as “tall” PT (a is tall) ≈ .97). But if a is 6 feet 2 inches tall, then a is a near-perfect borderline case
— PT (a is tall) ≈ .55). This all depends on the choice of prior distribution and model parameters,
but it seems to yield roughly reasonable results — even though the prior and model parameters were
not fitted to empirical data in our simulations.
Crucially, this proposal is intended as a psychological theory of the intuitive strength of sentences
involving vague scalar adjectives, and as one which could play a role in a theory of people’s(including theorists’) metalinguistic judgments about the degree to which a sentences is supported
14 We will not deal with higher-order vagueness in detail here, but two compatible lines of attack suggest themselves.
First, we could treat definitely as a vague modal, following Lassiter (2011). Second, we could allow uncertainty about
the relevant prior distribution, which would generate meta-uncertainty about the interpretation.
15 We call this inference “metalinguistic” because it requires us to reflect directly on linguistic interpretation and usage.
Unfortunately for psycholinguists and field linguists, this is a difficult and fairly unusual task for most people: usuallyinferences about language serve as a means to communicate about the non-linguistic world, rather than as an end inthemselves, and we have to figure out some more indirect way to get at them.

22Figure 7

Simulated posteriors given utterance “Al is tall”, using two input priors with different
means but equal variance.

in a context. It is not meant as an answer to the metaphysical question of when a sentence such as“Al is tall” is in fact true. We suspect that many of the semantic approaches commonly exploredin the literature of vagueness are compatible with this theory. For example, we could convert it
into an Edgington-style degree theory by tying the degree of truth of a sentence in a context to thePT (⋅) function that an ideal interpreter would derive in this context. This would be rather different
from her original conception, since the probabilities that our model derives involve a speaker’s
intended meaning, rather than a description’s truth. However, if the only relevant contextual factor
were a speaker’s intended meaning, it might be appropriate to use a rational listener’s probabilistic
estimate of intended meaning to fix degrees of truth. The epistemic theory of vagueness is also
clearly compatible with this account: we simply assume that there is a fixed, unknown fact about
the true value of θtall , and both speaker and listener are trying to estimate it probabilistically anduse these estimates to inform their usage. Interpretations in terms of supervaluations or three-valued
logic also seem to be available. We will not take a stand on which of these interpretations is best
because our interest is primarily in the psychology and behavior of language users, and it is unclear
to what extent an answer to the metaphysical question would be illuminating about these cognitive
issues.16
Fourth, there is the sorites paradox. The next section describes several possible approaches andour model’s predictions for each.
The model of pragmatic interpretation sketched here, with threshold values and other semantic
variables passed up to the pragmatic listener, interacts with our simple free-variable semantics to
16 A cognitive theory of the interpretation of vague expressions does not answer the metaphysical question, but it does
perhaps sharpen the subject matter of this question. If we had a satisfying probabilistic theory of how listeners interpretvague expressions and what information they extract from them, as well as an account of why and when speakers choose
to use vague expressions, what phenomena would remain to be explained by an answer to the metaphysical question?

23

predict several of the key properties of relative adjectives in the positive form. Crucially, the model’s
predictions vary depending on the prior on answers to the QUD, PL0/1 (h). While a normal prioris natural for, e.g., the distribution of heights and temperatures in a natural class, there are otherproperties for which this is not a reasonable assumption (for example, the properties associated withdangerous/safe and full/empty). In Lassiter & Goodman 2013 we explore the predictions of thismodel for a broader range of priors. We propose there that the differences in posterior derived byour model, depending on prior, can account for several phenomena discussed in the literature on
absolute scalar adjectives including the latter’s apparent lack of borderline cases and insensitivity to
the sorites paradox (Kennedy 2007).
5

The sorites

In this section we explore several possible psychological accounts of the sorites paradox that are
made available by the model of interpretation that we have described. The first two follow closely
ideas in Edgington 1992, 1997, and focus on the marginal posterior distribution on thresholds.17
The third suggested interpretation is new here, and makes use of the fact that our model delivers ajoint distribution on tall-thresholds and on the heights of individuals described as “tall”. Using theBayesian model of adjective interpretation together with some auxiliary assumptions about how
argument strength tracks probability, we can extract from each a (non-paradoxical) explanationof why the sorites feels compelling, as well as specific quantitative predictions about how compelling the premises should be depending on the set-up of the sorites sequence and our semantic
assumptions.
We will not come to a firm conclusion about how the sorites should be understood, since there
are many options and little empirical data available on which to base a conclusion. However, thefact that we can assign quantitative strengths to the premises of the sorites suggests a reorientation
toward precise, experimentally testable theories of the sorites, as we describe briefly at the end of
the section.
We set up a sorites sequence as follows. The first member xn is an individual who is clearly Adj,
and the last, x0 , is an individual who is clearly not Adj. Each non-initial member of the sequence hasa degree of the scalar property underlying Adj which is exactly ε greater than the previous member.
The general form of the sorites paradox is as in (33). (34) illustrates with some familiar characters,
the stock adjective tall, and ε = 1 mm.
(33)

a. xn is Adj.
b. For all m s.t. 0 < m ≤ n, if xm is Adj, then xm−1 is Adj.
c. ∴ x0 is Adj.

(34)

a. André the Giant is tall.

17 Our discussion follows Edgington’s seminal work most closely. For other related work connecting probability withphilosophical and linguistic questions involving vagueness, see Borel 1907 (with translation and commentary in Égré &Barberousse 2014); Black 1937; Kyburg & Schubert 1993; Kyburg 2000; Lawry 2008; Frazee & Beaver 2010; Lassiter2011; Égré 2011; Sutton 2013; Egré to appear. A detailed comparative analysis of these accounts would take us too far
afield here; see however Égré & Barberousse 2014; Egré to appear for some discussion along these lines.

24b. For any two people, if the first is tall and the second is 1 mm. shorter, then the second isalso tall.
c. ∴ Danny DeVito is tall.
The argument is valid but its conclusion is absurd. The problem is to explain how the first premise
can be true and the conclusion false, while doing justice to the intuition that the inductive premise isintuitively compelling in concrete examples such as (34b).
Edgington (1997) argues that the basic form of the sorites — the form in which people reason
intuitively about it and find the premises compelling — is not the highly compressed universallyquantified version in (33) but the equivalent, more expansive version in (35). (For a relatedsuggestion see Sorensen 2012.)(35)

a. xn is tall.
b. If xn is tall, then xn−1 is tall.
c. If xn−1 is tall, then xn−2 is tall.
d. If xn−2 is tall, then xn−3 is tall.
e. ...
f. If x2 is tall, then x1 is tall.
g. If x1 is tall, then x0 is tall.
h. ∴ x0 is tall.

Of course, it is crucial in resolving the paradox in this form to fix our understanding of the conditionals. Edgington considers two options, a material conditional interpretation and a probabilistic
interpretation, and argues that the probabilistic account is able to dissolve the paradox on either
interpretation. We will give our own gloss on this reasoning, which is (we hope) faithful to the spiritof Edgington’s account.
Classically, belief is thought of as an all-or-nothing matter. On this interpretation, a valid
argument constrains a rational individual who fully believes the premises to fully believe theconclusion as well. But what can we do with deductive validity if we think of belief as a graded,probabilistic concept? Adams (1966) proves that validity constrains probability assignments in thefollowing way:
If (and only if) an argument is deductively valid, the uncertainty of the consequent(i.e., 1 minus its probability) cannot exceed the sum of the uncertainty of the premises(1 minus their individual probabilities) under any probability distribution P(⋅).
So a rational Bayesian ought to pay attention to deductively valid arguments like the sorites.
However, deductive validity’s constraining effects on rational belief are not as extreme when belief
admits of degrees: rather, deductively valid arguments merely tend to lead to reasonable conclusions
and can fail to do so under two kinds of conditions. First: we have to be fairly confident about
the premises. Second: there cannot be too many premises. (How many are “too many” depends
on how confident we are about the premises, of course.) If either of these conditions is violated, adeductively valid argument can lead us to erroneous conclusions.

25

As Edgington points out, a sorites argument like (35) is designed so that — if it is valid — itmust violate one of these conditions: either the gap ε is large (so that we are not very confident
about the premises), or the number of premises is large (so that probability shades off graduallyfrom 1 to 0). Either way, the air of paradox dissolves once we jettison the assumption that belief isan all-or-nothing matter: we should think of the sorites primarily as a demonstration of the dangersof this assumption.
Structurally, the explanation is identical to a Bayesian account of the Lottery and Preface
paradoxes, as Edgington emphasizes. I may believe, for each of the statements S in my book, that
S is true; and yet I also believe that there is a false statement lurking undetected in the book. (AsEdgington (1997: 295) puts it: “Who would be so rash as to claim that he has no false beliefs?”)
On a Bayesian interpretation, this is a consistent set of beliefs if there are many statements in thebook and some of them have high but non-maximal probability. Paradox arises only if belief is
all-or-nothing (or if there is a fixed context-insensitive probability threshold for belief: see Lin &Kelly 2012; Leitgeb 2014).
Implicit in this explanation is a strong claim about the psychological aspect of the sorites (cf.
Fara 2000). Edgington assumes that the reason we find the premises compelling is that they havehigh (conditional) probability. That is, the intuitive strength of a premise is taken to be directly
correlated with its probability, rather than, for example, corresponding to a step function whichfalls off sharply with the transition from probability 1 to probability less than 1. In full detail, theimplicit assumption seems to be:
Consider a sentence A (which may be a conditional sentence). Our intuitions about
the strength of A do not track the binary question of whether we have “full belief”
in A — that is, there is not a sharp difference in the felt strength of A between thesituation in which we are completely certain and a situation in which we are almost,but not quite, certain. Rather, intuitions about the strength of a premise decline
gradually with the degree of belief that we have in A — that is, with its probability.
Bracketing the issue of how to assign probabilities to conditional sentences (more below), it seems
clear that this assumption is very important for the overall plausibility of Edgington’s theory.
Without it, her explanation would fail to account for the psychological aspect of the sorites — even
if it is logically impeccable — since it would not yet give us an explanation of why people findpremises (35b)-(35g) compelling. Fortunately, this interpretation of intuitive strength has received agreat deal of support in the recent psychological literature on reasoning, which has recently moved
away from a binary concept of strength connected with the binary concept of belief. The dominant
approach in this literature, as well, is a graded notion of strength which is closely connected to(conditional) probability (Oaksford & Chater 2007; Over 2009).
Taking for granted that our intuitions about premise strength track the premises’ probability,
then, what remains to be shown is that there is a semantically and pragmatically well-motivated
way to assign high probability to each of the premises of (33) while also assigning low probability
to the conclusion. Take first the material conditional interpretation of conditionals (MC). On thisinterpretation the paradox is logically equivalent to the expanded version of the negative existential
sorites, viz.:

26

(36)

a. xn is tall.
b. It’s not that case that xn is tall and xn−1 is not tall.
c. ...
d. It’s not that case that x1 is tall and x0 is not tall.
e. ∴ x0 is tall.

We can use equation (32) to find the probability that an individual xm is tall while xm−1 is not: it isjust the probability that θtall falls between height(xm ) and height(xm−1 ).
(37)

PT (xm is tall and xm−1 is not tall) = ∫height(xm )height(xm−1 )PL1 (θtall ∣u = “xm is tall”) dθtall .

The probability of the negation of this statement is 1 minus this value. Since they are logicallyequivalent, this is also the probability of the material conditional interpretations of premises (36b)(36d).18 In general, if the gap ε is small then this probability will also be small. This is clear if we
imagine dividing the posterior on θtall in Figure 5 into k rectangles of equal width along the x-axis.
When k is large, very little of the posterior mass of θtall will reside in any single rectangle. Figure 8
shows the result with ε set to the generous value of .5 inch.
Figure 9 plots the probabilities of the MC-sorites premises in (35) using the probabilistic
interpretation that was plotted in Figure 5, with ε = .5 inch. We start with an individual xn who isclearly tall (6 feet 9 inches) and end with an individual x0 who is clearly not tall (4 feet 9 inches).
In our simulation, the probability that xn is tall is indistinguishable from 1 (solid red line on theright edge of the graph in Figure 9), and the probability of the other premises never drops below .97(dashed green line along top). So, the premises are felt as compelling. Nevertheless, the conclusionof the argument has probability indistinguishable from 0 (solid red line on the left edge of the graphin Figure 9). If this is the indeed the way that people understand the sorites, this account — which
follows Edgington’s closely — appears to account for the logical and psychological aspects of theparadox.
What about the probability conditional interpretation (PC)? On the version of this account that
Edgington presumably has in mind, conditionals do not have truth-conditions, but they are assigned
probabilities according to Adams’ Thesis (a.k.a. “The Equation”: see Adams 1975; Edgington 1995
for explication).
(38)

P(If A then B) = P(B∣A).

On this theory, the probability of If A then B can be interpreted roughly as the degree of belief thatan individual whose degrees of belief are as in P(⋅) would assign to B on the supposition that A is
18 We simplify here by assuming that the value of θtall is fixed for all instances of “tall” in a single sentence whose
arguments are drawn from a common prior. This entails that “y is tall and z is not” has probability 0 when y and z havea common prior and z is taller than y. The account of the sorites is not much different qualitatively if we relax thisassumption, allowing that θtall is drawn independently for the two instances of θtall .
Note also that, given the assumption of common prior and θtall , it doesn’t matter whether the utterance u referred toin equation 32 is “xm is tall” or “xm−1 is tall”. On the Edgington-inspired account of the sorites that we are considering,
the prior is what drives the interpretation, not the individuals’ actual height.

27

Figure 8

Interpretation of tall from Figure 5, with vertical lines overlaid representing theheights of individuals in the sorites sequence. Each premise of the MC-sorites isrepresented by a rectangle formed from two adjacent lines, and its probability is equal
to the probability that the tall-threshold does not fall inside that rectangle. The filledin area, with about 3% of the area under the curve of the threshold posterior, gives
the failure probability for the least likely premise. This premise still has probability
greater than .97.

true. So, in particular, the premises of the PC-sorites will be compelling as long as the following
conditions hold:(39)

a. P(xn is tall) is high.
b. P(xn−1 is tall∣xn is tall) is high.
c. P(xn−2 is tall∣xn−1 is tall) is high.
d. P(xn−3 is tall∣xn−2 is tall) is high.
e. ...
f. P(x1 is tall∣x2 is tall) is high.
g. P(x0 is tall∣x1 is tall) is high.

As before, we have rigged things up so that “P(xn is tall)” is indistinguishable from 1, andP(x0 is tall) is indistinguishable from 0.
We can inspect our simulation results to discover the probabilities of the intermediate premises
on current assumptions. Building on the proposal above that equation 32 is an appropriate way tounderstand the metalinguistic judgments that (39) requires, we suggest that premises (39b)-(39g)
can be unpacked as in equation 40.
(40)h(xm−1 )
PL1 (θtall ∣u=“xm is tall”) dθtall

P(xm−1 is tall∣xm is tall) = ∫0 h(xm )∫0

PL1 (θtall ∣u=“xm is tall”) dθtall

In the case of the MC-sorites, the strength of a premise involving xm and xm−1 was given by the
proportion of the total area under the curve of P(θtall ∣u) which fell either below h(xm−1 ) or above28

Figure 9

Probability of the premises in the expanded sorites sequence when the conditionalsin (35) are interpreted as material conditionals.

h(xm ). In the PC-sorites, the strength of a premise is the proportion of the mass which falls belowh(xm ) which does not also fall between the heights of these two individuals. In fact, a given
PC-premise is guaranteed to have probability at most as great as the corresponding MC-premise:
the PC-sorites is a more stringent test of the probabilistic theory’s explanatory power.
Our model’s predictions about this version of the sorites are plotted in Figure 10, again assuming
a rather large gap size ε = .5 inch.
Initially this version looks less promising: the probability of the inductive premises begins to drop
off sharply when the taller of the two individuals has height less than 5.5 feet. However, something
subtle is going on here: the premises maintain high probability (> .9) over the entire range in which
the antecedent “xm is tall” has even a tiny chance of being true. The probability of the premise first
drops below .9 when xm is about 5 feet 7 inches tall, and xm−1 is 5 feet 6.5 inches. By this point,
the probability that xm is tall is vanishingly small, less than .01. Here, and at lower values, we arereasoning about conditionals whose antecedents are almost certainly false. (Indeed, the curve ends
abruptly soon after because we were unable to estimate the probability: out of 5 million samples,
there were none in our simulations in which individuals this short counted as “tall”.) Perhaps thisproperty can be used to explain away the low probability of the PC-premises at very low heights, on
the grounds that these cases are too remote to play a role in our intuitive reasoning about the sorites.
Of course, these are just two of the many possible interpretations of the conditional available.
There are modal, non-probabilistic, non-material-conditional interpretations, and there are probabilistic interpretations that do not satisfy Adams’ Thesis. While we cannot survey the space ofpossible theories exhaustively, we are optimistic that the success of the probabilistic account of
the sorites in these two, very different theories of the conditional is indicative of its robustness tomodifications in the details of conditional semantics.
We assumed above that the function of interest for the interpretation of the sorites was the29Figure 10

Probability of the premises in the expanded sorites sequence when the conditionalsin (35) are assigned probabilities by Adams’ Thesis (equation 38).

metalinguistic probability PT (xi is tall), computed using the marginal probability that the thresholdfalls below xi ’s height. However, the model has access to the joint posterior PL1 (h,θtall ∣u), which
includes information not only about what “tall” means but also about the likely heights of people
who have been described as “tall”, relative to a certain prior. Our model makes room for an
additional possible interpretation of the sorites which draws on this joint posterior, which we
describe briefly in closing.
A natural alternative framing of the sorites is to use neither explicit quantification nor a long listof fully resolved premises, but rather a free variable in the statement.
(41)

a. xn is tall.
b. If y is tall and z is ε shorter than y, then z is tall.
c. ∴ x0 is tall.

Standardly, a statement like (41b) would be understood as implicitly universally quantified. However, our probabilistic account suggests a different interpretation, related to the widely held (but notuncontroversial) claim that indefinites are interpreted as variables of some sort whose values must
be inferred using global pragmatic mechanisms (Kamp 1981; Heim 1982; Kratzer 1998, etc.). If
this is right, then (41b) is interpreted along the lines of (42):(42)

If someone is tall and someone else is ε shorter, then the latter person is also tall.

On this account, we can keep the variables in the inductive premise. The conditional is understoodas an instruction to suppose not just that the antecedent is true, but also that it has just been asserted:a cooperative speaker S1 has uttered “y is tall”, and the listener is to use this supposition to inform
inferences about y’s height, the meaning of tall, and the probability of the consequent. (41b) should
then be intepreted as: what is the probability that a person z, who is just ε shorter than someone
who has been described as “tall”, also counts as “tall”?
30

It is worth pausing here to consider the plausibility of this psychological hypothesis in aslightly broader context not involving vagueness. Could language users’ instinctive reactions to theconditional premise (41b) by attributed to a general strategy for conditional interpretation? Consider
example (43) (inspired by discussion of tolerance principles in Egré to appear).
(43) If x can drink n glasses of wine and still be sober, then x can drink n glasses of wine plus
one ounce without becoming ill.
(43) seems like a reasonable claim. Yet, if we universally close over the variables, it is simply
false: we only have to set n = 0, and consider the small but significant number of individuals wholack the ability to digest alcohol, and become ill with any amount of wine. Nevertheless, (43)strikes us as an intuitively compelling premise—at least as much as (41b). We can understandwhy (43) feels compelling if we suppose that listeners generate interpretations for conditionals byimagining situations in which the antecedent has been asserted, and considering the truth-value ofthe consequent in the simulated situation. In this case, our pragmatic model predicts that the listenerwill tend to consider values for the variables x and n for which a speaker would be likely to comment“x can drink n glasses of wine and still be sober”. Since this sentence is uninformative with n = 0— anyone can stay sober by drinking no wine — a speaker would be unlikely to make such an
assertion in this case, and so the listener will tend not to consider such scenarios when interpretingthe consequent. Instead, listeners will tend to consider values of n which are significant enoughto be worth comment. Furthermore, since the base rate of alcohol intolerance is extremely low,these individuals will tend not to be considered when interpreting the consequent. A metalinguistic,
simulation-based account of conditional interpretation along these lines would thus be able topredict the intuitive strength of (43).
Continuing with this hypothesis about the interpretation of conditionals containing free variables,
we can derive a prediction about the intuitive strength of the version of the sorites in (41). The
fact that an individual y has been described as “tall” places constraints on y’s height: y is probably
not 5 foot 10, even though this is a perfectly common height, because someone of that heightprobably wouldn’t be called “tall”. When the listener samples thresholds and heights from the joint
distribution PL1 (height(y),θtall ∣u = “y is tall”) that our model provides, she will find that the heightof an individual just ε shorter than y nearly always falls above θtall as well.
(44)

PJ (Premise (41b)) = PL1 (height(z) > θtall ∣ u = “y is tall”)= PL1 (height(y) − ε > θtall ∣ u = “y is tall”)= PL1 (θtall ∉ [height(y) − ε,height(y)) ∣ u = “y is tall”),

where the last line follows on the assumption that the speaker is being truthful, so that height(y) >θtall . This interpretation is rather different from the “metalinguistic” probabilities that were computed above by sampling from the distribution PL1 (θtall ∣u) which results from marginalizing out
h. Assuming that the relevant prior is as in Figure 5, we can compute the probability describedin equation 44 from our sampled joint posterior by asking in what proportion of samples the difference between the sampled height and θtall exceeds ε. The probability that (41b) is true on thisinterpretation, setting ε = .5 inch, is approximately .91.
On this interpretation, then, the inductive premise receives quite high probability even when the
gap size ε is fairly large. We can also explore how the probability of the inductive premise varies31

as a function of ε. Figure 11 illustrates the results for a grid of values between .01 inches and 4
inches. The probability assigned to the inductive premise is very or fairly high for a wide range ofvalues. In fact, for this choice of prior and model parameters the inductive premise has probability.5 for all gap sizes less than 3.1 inches.

Figure 11

Probability of the inductive premise of the sorites as interpreted in equation 44, as afunction of gap size (.01 to 4 inches). The vertical line represents ε = .5, the primary
example in the main text, which yields probability .91.

We certainly do not wish to argue that this interpretation is the only way to understand thesorites. However, it does seem to be a reasonable candidate for how people approach the paradox
on an intuitive level, and it is not (to our knowledge) one that has been discussed in the previous
literature. One particular advantage of this interpretation over the previous two that we consideredis that it predicts, quite plausibly, that people will primarily consider relatively tall individuals when
considering the inductive premise of a tall-sorites. In contrast, for the other two interpretations
considered above all instantiations of the inductive premise are on a par, including those for which
the antecedent is extremely implausible — for example, when the taller individual is 5 feet tall.
We are not able to decide conclusively among these three interpretations of the sorites, andthere are no doubt additional plausible candidates.19 Especially worth noting here is that our model
derives specific quantitative predictions about the strength of the sorites premises which dependon the choice of assumptions about the interpretation of the sorites. These predictions could betested in behavioral experiments using the methods of experimental psychology. This opens up theinteresting possibility that a philosophical theory of the interpretation of the sorites paradox could
be subject to experimental verification or falsification — at least, inasmuch as a theory of the sorites
is a theory of how language users understand the paradox and why they find it paradoxical.
19 Considerations specific to the theory of conditionals are of great relevance here: for example, the inadequacies ofthe material conditional interpretation of English if are well-known, and we do not know whether the metalinguistic,
suppositional account just sketched will ultimately be viable.

32

6Conclusion

Our model of adjective interpretation is an application of a general pragmatic model which emphasizes the continuity of language understanding and uncertain reasoning and decision-making in otherareas of cognition. Adopting a simple free-variable semantics for adjectives, we have used concepts
from game-theoretic pragmatics and tools from Bayesian modeling to predict context-sensitive
vague interpretations. The model we proposed derives novel quantitative and qualitative predictions
about adjective interpretation and suggests new approaches to a number of crucial issues around thevagueness and context-sensitivity of relative adjectives and especially the sorites paradox.
Some of the choices that we have made in setting up the model are overly simplified, suchas the assumption that speakers care only about informativity and cost. The interpretive effectsof rich speaker goals remain to be explored, as does varying the Question Under Discussion andnumerous other possible enrichments and modifications of the model. Likewise, our approach relies
crucially on intuitions about reasonable priors; it will be necessary in future work to use empiricalmeasures to validate the choice of priors and check the predictions about their mapping to contextualinterpretations. Other natural extensions of the model include inferring the QUD (Kao et al. 2014)
and choosing the relevant scale with adjectives for which there are several options (Kennedy 1997;
Sassoon 2013).
We hope that this work will be seen as a demonstration of the potential for fruitful interaction
between philosophy of language and logic, formal semantics and pragmatics, and computational
cognitive science (see also Goodman & Lassiter 2015). Bayesian modeling makes it possible
to combine logical and probabilistic reasoning seamlessly, and the recent growth of this style ofmodeling in cognitive science has opened up new directions which we hope will prove to be usefulfor linguistics and philosophy as well. Most relevantly for this paper, we believe that this style ofmodeling has great potential to illuminate the ways in which speakers and listeners use context
and background knowledge to communicate rich context-sensitive meanings despite ever-present
uncertainty.
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