Proposition

Abstract: A conditional sentence expresses a proposition which is a function of two other propositions, yet not one which is a truth function of those propositions I may know the truth values of “Willie Mays played in the American League” and “Willie Mays hit four hundred” without knowing whether or not Mays, would have hit four hundred if he had played in the American League This fact has tended to puzzle, displease, or delight philosophers, and many have felt that it is a fact that calls for some comment or explanation It has given rise to a number of philosophical problems; I shall discuss three of these

Abstract: If I hear the patter of little feet around the house, I expect Bruce. What I expect is a cat, a particular cat. If I heard such a patter in another house, I might expect a cat but no particular cat. What I expect then seems to be a Meinongian incomplete cat. I expect winter, expect stormy weather, expect to shovel snow, expect fatigue-a season, a phenomenon, an activity, a state. I expect that someday mankind will inhabit at least five planets. This time what I expect is a state of affairs. If we let surface grammar be our guide, the objects of expectation seem quite a miscellany. The same goes for belief, since expectation is one kind of belief. The same goes for desire: I could want Bruce, want a cat but no particular cat, want winter, want stormy weather, want to shovel snow, want fatigue, or want that someday mankind will inhabit at least five planets. The same goes for other attitudes to the extent that they consist partly of beliefs or desires or lacks thereof. But the seeming diversity of objects might be an illusion. Perhaps the objects of attitudes are uniform in category, and it is our ways of speaking elliptically about these uniform objects that are diverse. That indeed is our consensus. We mostly think that the attitudes uniformly have propositions as their objects. That is why we speak habitually of "propositional attitudes." When I hear a patter and expect Bruce, for instance, there may or may not be some legitimate sense in which Bruce the cat is an object of my attitude. But, be that as it may, according to received opinion my expectation has a propositional object. It is directed upon a proposition to the effect that Bruce is about to turn up. If instead I expect a cat but no particular cat, then the object of my expectation is a different proposition to the effect that some cat or other is about to turn up. Likewise for our other examples. The case of expecting a cat shows one advantage of our policy of uniformly assigning propositional objects. If we do not need a Meinongian incomplete cat as object of this attitude, then

Abstract: PRUF—an acronym for Possibilistic Relational Universal Fuzzy—is a meaning representation language for natural languages which departs from the conventional approaches to the theory of meaning in several important respects. First, a basic assumption underlying PRUF is that the imprecision that is intrinsic in natural languages is, for the most part, possibilistic rather than probabilistic in nature. Thus, a proposition such as “Richard is tall” translates in PRUF into a possibility distribution of the variable Height (Richard), which associates with each value of the variable a number in the interval [0,11 representing the possibility that Height (Richard) could assume the value in question. More generally, a proposition, p , translates into a procedure, P, which returns a possibility distribution, Π p , with P and Π p representing, respectively, the meaning of p and the information conveyed by p . In this sense, the concept of a possibility distribution replaces that of truth as a foundation for the representation of meaning in natural languages. Second, the logic underlying PRUF is not a two-valued or multivalued logic, but a fuzzy logic, FL, in which the truth-values are linguistic, that is, are of the form true, not true, very true, more or less true, not very true , etc., with each such truth-value representing a fuzzy subset of the unit interval. The truth-value of a proposition is defined as its compatibility with a reference proposition, so that given two propositions p and r, one can compute the truth of p relative to r . Third, the quantifiers in PRUF—like the truth-values—are allowed to be linguistic, i.e. may be expressed as most, many, few, some, not very many, almost all, etc. Based on the concept of the cardinality of a fuzzy set, such quantifiers are given a concrete interpretation which makes it possible to translate into PRUF propositions exemplified by “Many tall men are much taller than most men,” “All tall women are blonde is not very true,” etc. The translation rules in PRUF are of four basic types: Type I—pertaining to modification; Type II—pertaining to composition; Type III—pertaining to quantification; and Type IV—pertaining to qualification and, in particular, to truth qualification, probability qualification and possibility qualification. The concepts of semantic equivalence and semantic entailment in PRUF provide a basis for question-answering and inference from fuzzy premises. In addition to serving as a foundation for approximate reasoning, PRUF may be employed as a language for the representation of imprecise knowledge and as a means of precisiation of fuzzy propositions expressed in a natural language.

Abstract: This paper presents an experimental test of the proposition that government contributions to public goods, funded by lump-sum taxation, will completely crowd out voluntary contributions. It is found that crowding-out is incomplete and that subjects who are taxed are significantly more cooperative. This is true even though the tax does not affect the Nash equilibrium prediction. This result is taken as evidence for alternative models that assume people experience some private benefit from contributing to public goods. Copyright 1993 by American Economic Association.