In this paper, we study natural language constructions which were first examined by Barwise: The richer the country, the more powerful some of its officials. Guided by Barwise's observations, we suggest that conceivable interpretations of such constructions express the existence of various similarities between partial orders such as homomorphism or embedding (strong readings). Semantically, we interpret the constructions as polyadic generalized quantifiers restricted to finite models (similarity quantifiers). We extend the results obtained by Barwise by showing that similarity quantifiers are not expressible in elementary logic over finite models. We also investigate whether the proposed readings are sound from the cognitive perspective. We prove that almost all similarity quantifiers are intractable. This leads us to first-order variants (weak readings), which only approximate the strong readings, but are cognitively more plausible. Driven by the question of ambiguity, we recall Barwise's argumentation in favour of strong readings, enriching it with some arguments of our own. Given that Barwise-like sentences are indeed ambiguous, we use a generalized Strong Meaning Hypothesis to derive predictions for their verification. Finally, we propose a hypothesis according to which conflicting pressures of communication and cognition might give rise to an ambiguous construction, provided that different semantic variants of the construction withstand different pressures involved in its usage.

We provide a computational model of semantic alignment among communicating agents constrained by social and cognitive pressures. We use our model to analyze the effects of social stratification and a local transmission bottleneck on the coordination of meaning in isolated dyads. The analysis suggests that the traditional approach to learning-understood as inferring prescribed meaning from observations-can be viewed as a special case of semantic alignment, manifesting itself in the behaviour of socially imbalanced dyads put under mild pressure of a local transmission bottleneck. Other parametrizations of the model yield different long-term effects, including lack of convergence or convergence on simple meanings only.

In the framework of Stewart Shapiro, computations are performed directly on strings of symbols (numerals) whose abstract numerical interpretation is determined by a notation. Shapiro showed that a total unary function (unary relation) on natural numbers is computable in every injective notation if and only if it is almost constant or almost identity function (finite or co-finite set). We obtain a syntactic generalization of this theorem, in terms of quantifier-free definability, for functions and relations relatively intrinsically computable on certain types of equivalence structures. We also characterize the class of relations and partial functions of arbitrary finite arities which are computable in every notation (be it injective or not). We consider the same question for notations in which certain equivalence relations are assumed to be computable. Finally, we discuss connections with a theorem by Ash, Knight, Manasse and Slaman which allow us to deduce some (but not all) of our results, based on quantifier elimination.

One of the driving forces of language evolution is the selection of variants that suit the communicative needs of its users. Crucially, fitness of linguistic variants may largely depend on the structure of the environment in which language is learned, transmitted, and used. This hypothesis has gained support in various domains. We apply it in the context of scalar terms with a major focus on quantifiers, such as most. Based on a model that combines logic and evolutionary game theory, we argue that such signals might have evolved as stable semantic units through adaptation to general communicative principles and distributional properties of the environment such as normality. This paper is distributed under a Creative Commons CC-BY-ND license.