Answers to exam questions ; , and it is often not even the case for the speaker either, who might be uncertain about the precise actual value. i will show in the following section how these intrinsic problems can be solved within a framework of strategic communication. 4 conditional preferences in strategic communication let us assume a game-theoretic setting of strategic communication, as developed by parikh 2001 ; . this is not alien to the bidirectional approach to pragmatic tendencies of interpretation; in fact, dekker and van rooy 2000 ; have given a game-theoretic formulation in terms of nash equilibria for cases like the one depicted in diagram 7 ; . in this setting, the preference for approximate interpretations of simple measure terms can be derived under the assumption that addressees hypothesize about the coding strategies of speakers, and speakers make use of this hypothesizing in their coding. parikh investigates the coding of information in a setting in which the probability and or utility of a message is taken into consideration, an idea already put forward by shannon 1948 ; . for example, if an expression f is ambiguous between two meanings m, m , where m is, in the given context, much more likely than m , then a speaker can safely encode the meaning m by f the meaning m is less likely, then the speaker better refers to it by more complex expression f that denotes m but not m . for example, mother is usually applied to biological mothers, but also to step mothers, foster mothers, etc. in many cases, there is no need for further specification, but if there is a danger, expressions like biological mother can be used cf. horn, 1993 ; . in the case at hand, the idea of economical encoding allows us to explain the rnri phenomenon without a general bias toward approximate or precise interpretation. the only bias we have to assume is the uncontroversial one toward simple expressions. assume as before that measurements may be reported in precise or approximate ways, where reporting in an approximate way should mean that a reported value stands for range of possible values. the proper way of capturing this idea is to represent the range by a normal distribution; for example, if thirty-nine is reported in an approximate way, then it would optimally represent 39, less optimally 38 and 40, still less optimally 37 and 41, and so on. to keep things simple, i will continue representing approximate interpretations of i by interval [i - s, s], which might be interpreted by a normal distribution with mean i and standard deviation s. the magnitude of s indicates the coarseness of the interpretation; with smaller s, the interpretation gets more precise. with s 0, the interpretation is maximally precise. when we define levels of approximate interpretations, we have to allow higher standard deviations for higher means; the interpretation [998, . , 1002] is much more precise than the interpretation [8, . , 12]. a level of approximation can be defined by the quotient of standard deviation s and the mean i. for example, at a level of approximation of 1 10, a reported value ten would stand for [9, . , 11], whereas a reported value of one hundred would stand for [90, . , 110]. at a level of approximation of 0, numbers are interpreted in a precise way. cf. dehaene 1997 ; for further discussion of such relationships, which relate to the weberfechner law of discriminability of stimuli. 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