Streamlining Social Decision Making for Improved Internet Standards

当今世界的许多决策都是通过具有不同兴趣和优先级的人员和团队之间复杂的动态交互和沟通过程做出的，即所谓的“分布式决策”（DDM）。例如，许多企业跨多个地理位置分散的办公室和时区工作，其团队专门负责相当不同的领域。每个团队可能有自己的目标和奖励模型，不一定是一致的，并且可能分布在多个组织单位（例如不同的企业或政府）。沟通可以通过几种不同的方式进行，这些方式具有非常不同的时间尺度和性质（例如电子邮件、即时通讯和面对面会议）。不幸的是，尽管许多组织已经开始记录这些过程，甚至提供记录（特别是政府组织，例如https://data.gov.uk/），但我们没有办法自动分析这些记录。如果我们这样做了，我们就可以产生工具来自动总结决策，跟踪谁做出了这些决策，以及为什么和如何做出这些决策（以及为什么其他决策没有做出）。从社会的角度来看，这将有助于使这些过程更加负责任和透明。我们还可以识别合作失败、偏见和其他问题，从而帮助改进未来的决策。这个项目将开发这些迫切需要的方法，使用自然语言处理和社会网络分析的结合。我们将整理、注释并公开发布第一个真实世界分布式决策的多模态数据集。我们将设计技术，采用自然语言和半结构化数据来识别决策中的对话和交互结构，并分析这些结构以生成摘要并评估决策过程的有效性。然后，我们将利用这些产出为能够简化和改进决策的战略干预措施提供信息。我们的方法将具有适当的通用性，以跨越多个领域。然而，该项目将专注于一个特定的全球组织作为其主要用例：互联网工程任务组（IETF）。这是一个国际论坛，负责制定互联网协议标准-正式文件，指定软件和硬件在互联网上“说话”的语言。为了制作这些文件，需要进行广泛的国际合作，包括电子邮件讨论、协作文档编辑、面对面会议和电话会议等多种形式。重要的是，所有这些方式都通过透明的报告记录下来，从公开的电子邮件档案到会议纪要。该项目与IETF合作，帮助建立模型并简化他们的决策过程。我们将借鉴他们的经验，并采用我们的方法来解决决策瓶颈。我们将设计工具，为IETF内的相关方提供建议和建议的干预措施。在许多其他事情中，我们直接使IETF和全球互联网标准社区受益，帮助他们发现偏见并帮助使重要的决策过程负责。

Many decisions in today's world are made through a complex, dynamic process of interaction and communication between people and teams with different interests and priorities - so called "distributed decision-making" (DDM). For example, many businesses work across multiple geographically dispersed offices and timezones, with teams specialising in quite diverse areas. Each team may have its own goals and reward models, which do not necessarily coincide, and may be spread across multiple organisational units (e.g. different businesses or governments). Communication may happen via several different modalities with very different timescales and properties (e.g. email, instant messenger, and face-to-face meetings).Unfortunately, although many organisations have started to document these processes and even make records available (particularly governmental organisations e.g. https://data.gov.uk/), we have no way to automatically analyse these records. If we did, we could produce tools to automatically summarise decisions, trace who made them, and why and how they were made (and why other decisions weren't made). From a societal standpoint this would help make these processes more accountable and transparent. We'd also be able to identify collaborative failures, biases and other problems, and thus help improve decision-making in future.This project will develop these urgently required methods, using a combination of natural language processing and social network analysis. We will collate, annotate and publicly release the first multimodal dataset of real-world distributed decision-making. We will devise techniques to take natural language and semi-structured data to recognise the dialogue and interaction structures in decision making, and analyse those structures to produce summaries and evaluate the efficacy of the decision making process. We will then use the outputs to inform strategic interventions that can streamline and improve decision making. Our methods will be suitably generic to span several domains. However, the project will focus on one particular global organisation as its main use case: the Internet Engineering Task Force (IETF). This is an international forum responsible for producing Internet protocol standards - formal documents which specify the languages by which software and hardware "speak" across the Internet. To produce these documents, extensive international collaboration is performed - this spans several modalities including email discussions, collaborative document editing, face-to-face meetings and teleconferencing. Importantly, all of these modalities are documented via transparency reports ranging from public email archives to minutes from meetings. This project has partnered with the IETF to help model and streamline their decision making process. We will borrow from their experience, and employ our methods to extract decision making bottlenecks. We will devise tooling which will provide advice and proposed interventions to relevant parties within the IETF. Amongst many other things, we directly benefit the IETF, and the global Internet standards community, by helping them to uncover biases and help make important decision processes accountable.