OPOQ: a Bayesian framework for querying political substance within natural language in support of large scale participatory decision-making

我的研究将通过理论和计算框架的发展来回答这个问题，该框架缩小了民主的三个核心方面：意见、政策和结果之间的差距。创建的框架将使用政治文本作为数据，利用自然语言处理和多种先进的定量方法。它将由a)灵活的Python数据抓取和处理管道组成，b)政策立场与论点分类，c)多维意见极性分析，例如经济左派，右派/环保，中立/社会传统，自由主义，d)按类型细分的颗粒政策，e)端到端民主数据模式，f)从GDP到福祉指标的计量经济模型，g)将这些元素结合在一起的民主决策支持系统。下面将进一步解释这种方法。总的来说，我的研究将采取一种规范的、职业的方法来支持民主项目的推进：展示混合民主治理，包括互补的代表性和直接元素，如何得到先进的定量方法和机器学习技术的支持，这些方法和技术被包裹在一个参与式决策支持系统中。我的方法方法将借鉴数据科学、计量经济学和人机交互最佳实践，由此使用的方法将主要是定量的，但也将与目标用户进行定性的现场测试。该框架将结合这些方法如下：a)在Python中创建灵活的数据处理管道：从多个来源抓取和处理政治文本，例如媒体和社交媒体中的意见，政党宣言，议会辩论和委员会报告，议会投票，并将其转换为“文本作为数据”进行分析。b)政策立场与论点分类：通过“词性”（POS）自然语言处理（NLP），以Lawrence等人（2017）和Burnap和Williams（2015）的工作为基础。这将支持政策和意见的自动分离和关键字标记。c)多维极性分析算法的开发：按轴自动编码政策立场，例如经济上的左-右，社会上的自由-传统，环境友好-中性，具有比仅关注左右分裂更高的语义精度。d)细粒度的自动政策类型分解：超越比较宣言项目和Lowe和Benoit （c.f Lowe et. al., 2011）所应用的方法，以识别和区分政策、立法行动、预算、目标和绩效。e)开发端到端的民主数据模式，将意见、政策和结果与可推广的编码过程联系起来。f)计量经济学结果建模：经济指标，如GDP和收入平等，以及福祉指标，如痛苦和预期寿命，作为a)至d)中开发的数据特征（变量）的函数。g)民主决策支持系统的发展：包括上述内容，显示意见，政策和结果之间的关系，并使用贝叶斯方法提供概率预测。随着时间的推移，该系统将从新数据中进行“机器学习”，包括从参与现场测试的决策者那里获得的数据。虽然这项研究的目标是普遍的民主决策支持，但发展将首先围绕一个实质性主题进行，例如，紧缩。这将确保在早期取得切实的进展，并在项目期间建立起来，因为进一步的主题和调优被迭代地应用。

My research will answer this question via the development of a theoretical and computational framework that closes the gaps between three core aspects of democracy: opinion, policy, and outcomes. The framework created will use political text as data, utilising natural language processing and multiple advanced quantitative methods. It will be comprised of a) a flexible Python data scraping and processing pipeline, b) policy position versus argument classification, c) multi-dimensional opinion polarity analysis e.g. economically left, right / eco-friendly, neutral / socially traditional, liberal, d) granular policy breakdown by type, e) an end to end democratic data schema, f) econometric modelling beyond GDP to wellbeing metrics, and g) a democratic decision support system bringing these elements together. This methodology is explained further below. Overall, my research will take a normative, vocational tack towards supporting the advance of the democratic project: demonstrating how hybrid democratic governance, comprising complementary representative and direct elements, may be supported by an ensemble of advanced quantitative methods and machine learning techniques wrapped into a participatory decision support system. My methodological approach will draw from data science, econometrics and Human Computer Interaction best practice, whereby the methods used will be predominantly quantitative, but also incorporate qualitative field tests with target users. The framework will combine these methods as follows:a) The creation of a flexible data processing pipeline in Python: that scrapes and processes political text from multiple sources, such as opinions in media and social media, party manifestos, parliamentary debates and committee reports, parliamentary votes, and transforms it into 'text as data' for analysis.b) Policy position versus argument classification: via 'parts of speech' (POS) natural language processing (NLP), building upon the work of Lawrence et al. (2017) and Burnap and Williams (2015). This will support the automated separating and keyword tagging of policies and opinion.c) The development of a multi-dimensional polarity analysis algorithm: to automatically code policy positions by axis, for example economic left-right, socially liberal-traditional, environmentally friendly-neutral, with higher semantic precision than achieved by focusing on the left-right cleavage alone.d) Granular automated policy type breakdown: beyond that applied by the Comparative Manifesto Project, and Lowe and Benoit (c.f. Lowe et. al., 2011), to identify and distinguish policy, legislative actions, budgets, goals and performance. e) The development of an end to end democratic data schema connecting opinion, policy and outcomes together with generalisable coding processes.f) Econometric outcome modelling: of economic metrics such as GDP and income equality, and wellbeing metrics such as suffering and life expectancy, as a function of the data features (variables) developed in a) to d). g) The development of a democratic decision support system: comprising the above, that shows relationships between opinions, policy, and outcomes, and uses Bayesian methods to deliver probabilistic predictions. This system will 'machine learn' over time from new data, including from participant decision-makers in field tests.While the objective for this research is generalisable democratic decision support, development will first be carried out around one substantive topic, for example, austerity. This will ensure that tangible advances are made early on, and built upon during the project, as further topics and tuning are iteratively applied.