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首页> 外文期刊>International Review of Financial Analysis >Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability
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Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability

机译:谁对Brexit不高兴?一种机器学习,基于代理的财务不稳定研究

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In this paper, we assess the happiness cost of Brexit in the UK and the EU, using data from the Gallup World Poll. We implement a two-stage learning machine, using a naive Bayes classifier to extract happiness preferences of the population and then passing these onto an artificial neural network of attributes to generate dynamic happiness functions for each household, on an agent-based modelling framework. We find that there is a sig-nificant long-run cost in terms of both happiness and unemployment, which primarily affects the most vulnerable portion of the population. In addition, despite the expected instability in City's financial centre, the UK financial sector seems to be well equipped to deal with the repercussions, thus minimising the welfare costs for the country. Our findings extend the discussion of the economic costs of Brexit, by adding the welfare cost of the ensuing financial instability.
机译:在本文中,我们评估了英国和欧盟的Brexit的幸福成本,利用来自盖洛普世界民意调查的数据。我们实施了一个两级学习机,使用天真的贝叶斯分类器来提取人口的幸福偏好,然后将其传递到属性的人工神经网络上,以在基于代理的建模框架上生成每个家庭的动态幸福函数。我们发现,在幸福和失业方面,幸福和失业率都有一个辛特的长期成本,主要影响人口最脆弱的部分。此外,尽管城市的金融中心预期不稳定,但英国金融部门似乎有能力处理困境,从而最大限度地降低了该国的福利成本。我们的调查结果通过增加了随后的金融不稳定的福利成本来讨论了Brexit的经济成本。

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