3 resultados para good lives model

em Duke University


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Primates must navigate complex social landscapes in their daily lives: gathering information from and about others, competing with others for food and mates, and cooperating to obtain rewards as well. Gaze-following often provides important clues as to what others see, know, or will do; using information about social attention is thus crucial for primates to be competent social actors. However, the cognitive bases of the gaze-following behaviors that primates exhibit appear to vary widely across species. The ultimate challenge of such analyses will therefore be to understand why such different cognitive mechanisms have evolved across species.

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Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

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Today, the trend towards decentralization is far-reaching. Proponents of decentralization have argued that decentralization promotes responsive and accountable local government by shortening the distance between local representatives and their constituency. However, in this paper, I focus on the countervailing effect of decentralization on the accountability mechanism, arguing that decentralization, which increases the number of actors eligible for policy making and implementation in governance as a whole, may blur lines of responsibility, thus weakening citizens’ ability to sanction government in election. By using the ordinary least squares (OLS) interaction model based on historical panel data for 78 countries in the 2002 – 2010 period, I test the hypothesis that as the number of government tiers increases, there will be a negative interaction between the number of government tiers and decentralization policies. The regression results show empirical evidence that decentralization policies, having a positive impact on governance under a relatively simple form of multilevel governance, have no more statistically significant effects as the complexity of government structure exceeds a certain degree. In particular, this paper found that the presence of intergovernmental meeting with legally binding authority have a negative impact on governance when the complexity of government structure reaches to the highest level.