I am interested in the following question: By virtue of what is the referent of a linguistic term fixed? The terms I’m concerned with here are proper names of type e.1 These are names whose semantic referent/value is an object. A semantic referent is one relatum in the three place relation of semantic reference: a relation that semanticists find helpful in positing between a name, context and object. Semantic reference is to be distinguished from speaker reference: a four place relation between speaker, term, object and audience. Roughly speaking, a speaker referent is an object a speaker intends their audience to think about.2 For the most part, I’ll be ignoring the question of how speaker referents are fixed. So the question I am concerned with here is this: by virtue of what is the semantic value of a proper name of type e fixed? This is a meta-semantic question. In this paper I help myself without argument to the content of the agent’s various propositional attitudes like knowledge. This parallels Lewis’s (1983) strategy in Languages and Language. There, he takes for granted the beliefs of the speakers/hearers and asks: by virtue of what do the sentences in, say, the English language get to have the meanings they actually have? Therefore, more foundational questions such as what constitutes knowledge or how the knowledge of an individual (as a physical system) is fixed are largely beyond the scope of this paper. With the content of the agent’s propositional attitudes in place, my main goal is to evaluate Knowledge Maximization (from now on KM) as an answer to the above meta-semantic question. KM is roughly the view that the semantic value of a proper name is the object such that assigning it as semantic value maximizes the knowledge expressed by the speaker’s sentences.3

The paper is structured as follows. In §2 I situate the general idea behind KM in a charity-based framework of interpretation and consider why one might opt for it. In §3 I formulate KM more precisely and evaluate individual variants of it and in §4 I evaluate communal variants. I observe that all variants suffer from serious problems. Still, in §5, I investigate whether any variant can at least satisfactorily explain the phenomenon of semantic change—a phenomenon that, on the face of it, KM seems well positioned to explain. However, I observe that KM does not ultimately offer such an explanation. In §6, I step back to consider a holistic strategy of charity-based interpretation—one that aims to fix both linguistic content and knowledge simultaneously by appealing to a principle of maximizing knowledge. I explain why the problems I raise persist even under such a holistic approach. I’ll also say why these issues easily extend to principles that appeal to true belief instead of knowledge. I take stock and conclude in §7.

The general idea behind KM, most famously associated with Davidson, is that meaning is determined by a principle of charity that

To motivate the above principle Davidson says:

The above charity principle is not necessarily the method by which the interpreter/hearer ascertains the content of an utterance or belief. According to Davidson, the principle is how sentences or beliefs get to have the content they have in the first place.4 Whether and how this content is ascertained by hearers are different issues.

The notion of ‘mostly right’ in “assigning truth conditions to alien sentences that make native speakers [mostly] right” can be developed in a variety of ways. Here are a few illustrated with an example by Williamson.5

Let’s look at this case through the lens of the framework I’m interested in where the agent’s propositional attitudes are assumed and it is various meta-semantic proposals about semantic reference fixing that are being investigated.6 In this framework we can easily see that various charity-based principles are inadequate. Take for example a principle that minimizes the expression of false beliefs. This principle is problematic. Suppose Celia and Elsie are our only candidate assignments. Since Emanuel has some true beliefs about Celia and some false beliefs about Celia, then given that Emanuel has no beliefs about Elsie, an assignment of Elsie to the term in question will in fact minimize the expression of false beliefs. This is because that assignment makes Emanuel’s sentences express fewer false beliefs by making his sentences express no beliefs about Celia or Elsie. Moreover, a principle that enjoins minimizing the expression of falsehoods will also be problematic assuming that under the Elsie assignment there are fewer expressed falsehoods than under the Celia assignment. And a principle that maximizes the expression of truths will be problematic for a similar reason. Indeed, on the assumption of bivalence, minimizing the expression of falsehoods and maximizing the expression of truths amount to the same thing.

KM on the other hand will do better than any of the above three charity-based principles and so we already see some initial motivation for it. Emanuel has no knowledge about Elsie because he has no beliefs about Elsie. Therefore, given that he has some knowledge about Celia, the Celia assignment will express more knowledge than the Elsie assignment. The astute reader however will notice that a principle that maximizes the expression of true beliefs (TBM) also does better than the other three charity-based principles mentioned in the previous paragraph. Therefore, one might wonder how to adjudicate here between KM and TBM. Assuming (as is orthodox) that knowledge and true belief are not the same thing, this is a good question.7 However, the problems I raise for KM in the next two sections also arise for TBM and therefore, since both KM and TBM are unacceptable, it’s not going to matter all that much to say which one is worse.

Williamson suggests that a communal approach to fixing linguistic content is more appropriate. He says: “each individual uses words as words of a public language; their meanings are constitutively determined not individually but socially, through the spectrum of linguistic activity across the community as a whole” (Williamson 2007: 98). There are some choice points with respect to what sort of communal knowledge maximization strategy to pursue. In what follows I consider two that seem like natural places to start. Moreover, the main lessons will likely carry over to other communal strategies.

In this section I present another toy model based on a well-known case in the literature on proper names: the case of ‘Madagascar’ which involves semantic change. On the face of it, KM seems to have a ready explanation for such cases. Intuitively, the object that becomes the new semantic value of the name is the one that ends up playing a suitably important role in the speaker’s life and that will naturally correspond to an increased amount of knowledge about the new object relative to the old. An account of fixing semantic values via maximizing the expression of knowledge might seem well positioned to get these cases right.

Evans (1973: 195–196) reports from Isaac Taylor’s (1898) book Names and their History, that ‘Madagascar’ was, a long time ago, only a name for a section of the African mainland (from now on X). Take the following variant of the case.

To keep predictions consistent with Leaf Raking, we will have to say that when first arriving on Y Marco Polo used the name ‘Madagascar’ with only X as semantic value. There are many ways of modelling the situation at the early stage. The below is one in which Marco Polo has a bit more knowledge about X than Y. Assume that Marco Polo’s AMP includes the following sentences only.

Suppose also that Marco Polo’s KMP includes the following known propositions only.

Now take the following candidate assignments of semantic value and summary table.

The correct semantic value of ‘Madagascar’ at the early stage is X: the semantic value given by assignment f. KM2, unlike KM1, secures this result. In the late stage however, a fairly widespread intuition is that the semantic value of ‘Madagascar’ changes to Y. In the late stage, when Marco Polo picks up much more knowledge about Y, many more sentence pairs will be added of the form ⌜Madagascar is F⌝ and ⌜This island is F⌝; ⌜Madagascar is G⌝ and ⌜This island is G⌝, etc. Here is a summary table illustrating the situation at the late stage.

Assignment g assigns Y to ‘Madagascar’ which is the correct semantic value at the late stage. This time KM1, unlike KM2, makes this prediction. Therefore, neither KM1 nor KM2 makes the intuitive predictions in Madagascar because neither predicts the right result at both stages. KM2 does better than KM1 at the early stage and KM1 does better than KM2 at the late stage.16

Of course KM1 lines-up with intuition reasonably well when a lot of time passes and there is more knowledge about the intuitively correct and new semantic referent—the one that is playing a suitably important role in the speaker’s life. Unfortunately however, as observed in §3 and §4, KM1 will not line-up with intuition if the massive amount of knowledge about Y is acquired in, say, a day. (This will essentially be a Madagascar version of the curiosity problem). After all, if Marco Polo learns a lot about Y in a day intuition says that he is still semantically referring to X by ‘Madagascar’ at the early stage. So KM1 by itself doesn’t really offer any insight as to whether and why time makes a difference in the above case of semantic reference change.17

Might communal variants of KM make better predictions in cases of semantic change? It seems not: observations parallel to those made in §4.2 will apply here.

Lewis (1974) formulated a version of the problem of radical interpretation by asking us to suppose that we have all the knowledge about some individual, but only as a physical system (e.g., how he moves, what forces he exerts on his surroundings, what light or sounds he absorbs or emits, etc.). Given all this knowledge, Lewis then asks, how might we know this individual’s beliefs, desires and utterance meanings? Lewis’s diagrams the problem of radical interpretation as follows: “given … the facts about [the individual] as a physical system, solve for the rest” (Lewis 1974: 331).18

But as Davidson (1974) and others are aware there is a prima facie challenge here. On the face of it, one cannot know the meanings of a speaker’s utterances without knowing their beliefs and one cannot know the speaker’s beliefs without knowing the meanings of their utterances. However, charity-based methods of interpretation are intended to meet this challenge: Davidson hopes that using charity we can solve for both belief and meaning simultaneously. As Glüer points out:

To see how this might work, take a charity-based principle that assigns meanings to sentences in a way that maximizes, say, truths only. The meanings this principle assigns are also beliefs that the principle attributes to the speaker. But left as it is this principle is obviously inadequate; some of the speaker’s beliefs are false and with no other constraints there is nothing stopping our charity principle from attributing beliefs that are all in fact true. Lewis (1974: 340) therefore considers a strategy of what he calls “successive approximation.” But even this strategy, he says, assumes at the beginning some ‘tentative’ speaker beliefs so that one may go and solve for meanings of utterances. When the initial results are in, he says, we may go on revising beliefs and meanings until a “satisfactory balance is struck” (Lewis 1974: 340). For various reasons Lewis thinks this strategy is inadequate and opts for a strategy under which the contents of the speaker’s attitudes are filled in completely and “no special attention is given to [the speaker’s] language at [the first] step” (Lewis 1974: 341). At the second step however, the content of the speaker’s attitudes are used to fill in the meanings of his utterances. Somewhat in the spirit of this strategy, I’ve been working on the following project: given facts about the speaker as a physical system and given their knowledge, solve for the meanings of their utterances. That is, I first filled in the speaker’s set of knowledge and then tried to solve for utterance meaning by maximizing knowledge expressed.

I should emphasize, however, that my central concerns will carry over to a strategy of using knowledge maximization to devise a method of interpretation where knowledge and utterance meanings are somehow fixed simultaneously. Let’s consider two competing interpretational packages in, say, Leaf Raking (though there will be an analogous issue for Madagascar). The first package uses assignment f to interpret the speaker’s words and includes a correct specification of the speaker’s knowledge. The second package uses assignment g to interpret the speaker’s words and includes a correct specification of the speaker’s knowledge. A simultaneous method of interpretation, however it’s formulated, should not find fault with the knowledge component of these packages. This is because, by hypotheses, the knowledge component of these packages are identical and correspond to the speaker’s actual knowledge. So the question arises: why should the holistic package with f in it be preferred to the holistic package with g in it? Since our two packages agree on the knowledge component, it’s the differing meaning assignments that are going to have to break the tie. But as we’ve seen, it’s very hard to see how anything in the vicinity of knowledge maximization would explain why the f package is preferred to the g package. This suggests that knowledge maximization cannot after all play the starring role in a holistic strategy that assigns knowledge and meaning simultaneously.

Finally, the incorrect predictions made by variants of KM weren’t especially due to choosing knowledge rather than true belief as the starring propositional attitude, since the problems I raised in §3 and §4 easily extend to true belief. One may simply re-label all the above tables (e.g., change “Total Under KM1” to “Total Under TBM1”) and use parallel cases to raise analogous problems. When the parallel cases are set up appropriately, the scores and consequently semantic predictions will remain exactly the same. Moreover, the remarks above can readily be adapted to a holistic version of the true belief strategy.

The challenge presented so far for KM is a significant one. Curiosity models made trouble for KM1. KM2 seemed like an improvement because it did better with that model. However, ultimately, KM2 confronted other problems like the semantic freedom problem and the reverse assignment problem (these also posed a challenge for KM1). One natural strategy was to switch from versions of KM that focused on the individual to versions that focused on the community, but this did not make the problems go away. And in cases of semantic change all versions of KM gave incorrect predictions at either the early or late stage. Moreover, as I showed in the previous section, the problems raised cast doubts about more holistic strategies that appeal to knowledge or true belief.

In this paper I have used candidate assignments with the intuitively correct semantic values for most terms in the set of accepted sentences (e.g., ‘is,’ ‘was,’ ‘market,’ ‘leaves,’ etc). Candidate assignments like f and g only differed with respect to what they assigned the name in question— be it ‘Jones’ or ‘Madagascar.’ When this assumption is relaxed, KM is in even greater trouble. For example, we would have to rule out assignments that assigned a different piece of mathematical knowledge to every sentence.19 And it’s a good question how various versions of KM are supposed to do this. It’s striking however that even when we assume the intuitively correct semantic values for nearly all terms, KM still cannot always deliver the right result.

Of course it remains possible that something in the vicinity of KM might figure in a much more complex charity-based theory of interpretation. Clearly, I’m in no position to refute this open-ended suggestion, though the lessons of this paper will be a helpful guide to anyone wishing to develop a theory along those lines.