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在生态学领域中,存在着生态系统整体论与还原论的争论。Tansley A.G.提出,生态系统是"准有机体"。Odum兄弟提出的"生态系统能量说"被广泛接受,但也受到质疑,称其为"还原论者的整体论"。基于对上述质疑的回应以及对生态系统整体论的追求,Patten B.C.等提出"生态网络理论",运用"网络‘环境子’分析"方法,试图从物理层面分析解决生物层面的"涌现性"问题。不过,这一理论也受到批判,认为其在探究符号化的现象对生态系统的动态影响时,陷入了还原论困境。Jrgensen S.E.等更进一步,提出"系统论"的生态系统生态学,试图从系统科学的角度研究生态系统的"物质-能量-信息-网络"系统。这一理论受到生态学界高度重视,但是也存在着在具体研究过程中如何平衡能量视角和生物地球化学视角的问题。由上述争论可见,生态系统生态学研究的趋势是从"物质实体"到"能量流动",再到"网络信息",最后到"开放系统"层层递进。目前面临的关键问题是:如何在更好地定义生态系统整体性的基础上,采取相应的能够体现生态系统整体性的方法,去获得更多、更好的生态系统整体性的认识。 相似文献
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The use of the computer metaphor has led to the proposal of mind architecture (Pylyshyn 1984; Newell 1990) as a model of the organization of the mind. The dualist computational model, however, has, since the earliest days of psychological functionalism, required that the concepts mind architecture and brain architecture be remote from each other. The development of both connectionism and neurocomputational science, has sought to dispense with this dualism and provide general models of consciousness – a uniform cognitive architecture –, which is in general reductionist, but which retains the computer metaphor. This paper examines, in the first place, the concepts of mind architecture and brain architecture, in order to evaluate the syntheses which have recently been offered. It then moves on to show how modifications which have been made to classical functionalist mind architectures, with the aim of making them compatible with brain architectures, are unable to resolve some of the most serious problems of functionalism. Some suggestions are given as to why it is not possible to relate mind structures and brain structures by using neurocomputational approaches, and finally the question is raised of the validity of reductionism in a theory which sets out to unite mind and brain architectures. 相似文献
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奥德姆的生态思想是妥协的整体论,有还原论的一面。把生态系统看作是功能性整体、承认生态系统各层次的涌现属性属于整体论,把生态关系简化为能量关系、把生态系统看作是物理系统的分析方法则是还原论的。这种矛盾的生态思想决定了其方法论的先天不足:生态模型的内在逻辑关系没有理顺;较少考虑生态系统的进化;生态研究方法的排它性等。但是,它并不妨碍奥德姆的生态思想在夯实生态学的本体论基础、促进理论生态学和生态工程学的形成、协调生态整体论与还原论分歧、奠定生态系统服务功能研究基础等方面发挥重要作用。要超越生态整体论与还原论,繁荣发展生态复杂性理论也许是最好的选择。 相似文献
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Schloendorn J 《Bioethics》2006,20(4):191-202
In the close to medium future, the life sciences might permit a vast extension of the human life span. I will argue that this is a very desirable development for the individual person. The question whether death is a harm to the dying is irrelevant here. All it takes is that being alive is good for the living person and not being alive is not good for anyone. Thus, living persons who expect to live on happily are rationally required to want to stay alive. Eventual uncertainty whether it will be possible to be happy in the future provides no objection, but rather an incentive to try. This view, however, may be naive in assuming that persons are unchanging entities that exist separately from their psychological information. Objections have been derived from reductionistic views that value our future experiences in a way that declines with time, so that there will be a future point beyond which only negligible value accrues. If we adopt such a view, then we cannot now be concerned to have experiences beyond that point. I argue that these arguments fail to take into account all the reasons we might have to be concerned for the future and all kinds of such concern that come from them. The adoption of a plausible reductionistic account can arguably weaken our concern for the future and certainly change its quality in important ways. But this provides no objection to the desire to live forever, nor to live at all. 相似文献
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Gregory M. Mikkelson 《Biology & philosophy》2001,16(4):533-546
When data are limited, simple models of complex ecological systems tend to wind up closer to the truth than more complex models of the same systems. This greater proximity to the truth, or verisimilitude, leads to greater predictive success. When more data are available, the advantage of simplicity decreases, and more complex models may gain the upper hand. In ecology, holistic models are usually simpler than reductionistic models. Thus, when data are limited, holistic models have an advantage over reductionistic models, with respect to verisimilitude and predictive success. I illustrate these points with models designed to explain and predict the numbers of species on islands. 相似文献
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