981 resultados para Computational thinking


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Latin had no word for "strategy", but the East Romans, whom we call the Byzantines, did. This book tracks the evolution of the concept of warfare being subjected to higher political aims from Antiquity to the Present, using Greek, Latin, French, Spanish, Italian, English and German sources. It tracks the rise, fall, and resurrection of the belief in the Roman and later the medieval and early modern world that warfare was only legitimate if it pursued the higher goal of a just peace, which in the 19th century gave way to a blinkered concentration on military victory as only war aim. It explains why one school of thought, from Antiquity to the present, emphasised eternal principles of warfare, while others emphasised, in Clausewitz's term, the "changing character of war". It tracks ideas from land warfare to naval warfare to air power and nuclear thinking, but it also stresses great leaps and discontinuities in thinking about strategy. It covers asymmetric wars both from the point of view of the weaker power seeking to overthrow a stronger power, and from the stronger power dealing with insurgents and other numerically inferior forces. It concludes with a commentary of the long-known problems of bureaucratic politics, non-centralised command and inter-service rivalry, which since the 16th century or earlier has created obstacles to coherent strategy making.

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It is easy to read Hobbes's moral thinking as a deviant contribution to 'modern' natural law, especially if Leviathan (1651) is read through a lens provided by De Cive (1642). But The Elements of Law (1640) encourages the view that Hobbes's argument is 'physicalist', that is, that it requires no premises beyond those required by his physics of matter in motion. The Elements included a draft De Homine and its argument is intimately connected with De Cive's; it shows how such concepts as 'reason', 'right', 'natural law' and 'obligation' can be understood in physicalist terms. But Hobbes's decision to print the latter work in isolation has led to serious misunderstandings

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Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

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