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Streamlining Social Decision Making for Improved Internet Standards

Streamlining Social Decision Making for Improved Internet Standards
简化社会决策以改进互联网标准
批准号:
EP/S033564/1
负责人:
Matthew Purver
金额:
$96.49万
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --

项目摘要

项目成果

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中文摘要
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英文摘要
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.
期刊论文(10)
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科研奖励(0)
会议论文
SemEval-2020 Task 3: Graded Word Similarity in Context
SemEval-2020 任务 3:上下文中的单词相似度分级
DOI: --
发表时间: 2020
期刊:
影响因子: --
作者: [Armendariz C]
通讯作者: Armendariz C
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
CoSimLex:用于评估上下文中分级单词相似度的资源
DOI: --
发表时间: 2020
期刊:
影响因子: --
作者: [Armendariz C]
通讯作者: Armendariz C
DOI: --
发表时间: 2021
期刊: ReInAct 2021 - Proceedings of the Conference on Reasoning and Interaction
影响因子: --
作者: [Del-Bosque-Trevino J.]
通讯作者: Del-Bosque-Trevino J.
Leveraging Data Science To Combat COVID-19: A Comprehensive Review
利用数据科学对抗 COVID-19:全面回顾
DOI: 10.36227/techrxiv.12212516.v1
发表时间: 2020
期刊:
影响因子: --
作者: [Crowcroft J]
通讯作者: Crowcroft J
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