Lockean theories of personal identity maintain that we per-sist by virtue of psychological continuity, and most Lockeans say that we are material things coinciding with animals. Some animalists ar-gue that if persons and animals coincide, they must have the same intrinsic properties, including thinking, and, as a result, there are ‘too many thinkers’ associated with each human being. Further, Lockeans have trouble explaining how animals and persons can be numerically different and have different persistence conditions. For these reasons, the idea of a person being numerically distinct but coincident with an animal is rejected and animalists conclude that we simply are animals. However, animalists face a similar problem when confronted with the vagueness of composition. Animals are entities with vague boundaries. According to the linguistic account of vagueness, the vagueness of a term consists in there being a number of candidates for the denotatum of the vague term. It seems to imply that where we see an animal, there are, in fact, a lot of distinct but overlapping entities with basically the same intrinsic properties, including think-ing. As a result, the animalist must also posit ‘too many thinkers’ where we thought there was only one. This seems to imply that the animalist cannot accept the linguistic account of vagueness. In this paper the author argues that the animalist can accept the linguistic account of vagueness and retain her argument against Lockeanism.
The short-term predictions of annual and seasonal discharge derived by a modified TIPS (Tendency, Intermittency, Periodicity and Stochasticity) methodology are presented in this paper. The TIPS method (Yevjevich, 1984) is modified in such a way that annual time scale is used instead of daily. The reason of extracting a seasonal component from discharge time series represents an attempt to identify the long-term stochastic behaviour. The methodology is applied for modelling annual discharges at six gauging stations in the middle Danube River basin using the observed data in the common period from 1931 to 2012. The model performance measures suggest that the modelled time series are matched reasonably well. The model is then used for the short-time predictions for three annual step ahead (2013–2015). The annual discharge predictions of larger river basins for moderate hydrological conditions show reasonable matching with records expressed as the relative error from –8% to +3%. Irrespective of this, wet and dry periods for the aforementioned river basins show significant departures from annual observations. Also, the smaller river basins display greater deviations up to 26% of the observed annual discharges, whereas the accuracy of annual predictions do not strictly depend on the prevailing hydrological conditions.