论文标题
使用贝叶斯网络对“公平且可持续的福祉”建模（BES）：意大利地区的案例研究
Modeling "Equitable and Sustainable Well-being" (BES) using Bayesian Networks: A Case Study of the Italian regions
论文作者
论文摘要
自上世纪末以来，对幸福感的衡量一直是一个高度争议的话题。尽管某些特定方面仍然是空旷的问题，但现在将多维方法以及共享和根深蒂固的指标系统的构建被接受为衡量这种复杂现象的主要途径。在这个方向上，一项有意义的努力是意大利“公平和可持续的福祉”（BES）指标体系，该系统由意大利国家统计研究所（ISTAT）和国家经济与劳工委员会（CNEN）开发。 BES框架包括在区域层面每年测量的许多原子指标，并反映了福祉的不同领域（例如，健康，教育，工作\＆Life Balance，Enviration，Enviration，...）。在这项工作中，我们旨在处理指标BES系统的多维性，并尝试回答三个主要的研究问题：i）BES原子指标之间关系的结构是什么； ii）BES域之间关系的结构是什么？ iii）关系的结构在多大程度上反映了当前的BES理论框架。我们通过实施贝叶斯网络（BNS）来解决这些问题，这是一种广泛接受的多元统计模型，特别适合以不确定性处理推理。 BN的实施会导致一组节点和一组条件独立语句，这些声明为探索变量系统中的关联提供了有效的工具。在这项工作中，我们还提出了两种策略，以编码BN估计算法中的先验知识，以便可以将BES理论框架表示为网络。
Measurement of well-being has been a highly debated topic since the end of the last century. While some specific aspects are still open issues, a multidimensional approach as well as the construction of shared and well-rooted systems of indicators are now accepted as the main route to measure this complex phenomenon. A meaningful effort, in this direction, is that of the Italian "Equitable and Sustainable Well-being" (BES) system of indicators, developed by the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour (CNEL). The BES framework comprises a number of atomic indicators measured yearly at the regional level and reflecting the different domains of well-being (e.g. Health, Education, Work \& Life Balance, Environment,...). In this work we aim at dealing with the multidimensionality of the BES system of indicators and try to answer three main research questions: I) What is the structure of the relationships among the BES atomic indicators; II) What is the structure of the relationships among the BES domains; III) To what extent the structure of the relationships reflects the current BES theoretical framework. We address these questions by implementing Bayesian Networks (BNs), a widely accepted class of multivariate statistical models, particularly suitable for handling reasoning with uncertainty. Implementation of a BN results in a set of nodes and a set of conditional independence statements that provide an effective tool to explore associations in a system of variables. In this work, we also suggest two strategies for encoding prior knowledge in the BN estimating algorithm so that the BES theoretical framework can be represented into the network.
