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AitF: Collaborative Research: Fair and Efficient Societal Decision Making via Collaborative Convex Optimization

AitF：协作研究：通过协作凸优化实现公平高效的社会决策

基本信息

批准号：
1637397
负责人：
Kameshwar Munagala
金额：
$ 33.3万
依托单位：
Duke University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637397&HistoricalAwards=false
关键词：
AitF Collaborative Research Fair Efficient

项目摘要

YouTube competes with Hollywood as an entertainment channel, and also supplements Hollywood by acting as a distribution mechanism. Twitter has a similar relationship to news media, and Coursera to Universities. But there are no online alternatives for making democratic decisions at large scale as a society. As opposed to building consensus and compromise, public discussion boards often devolve into flame wars when dealing with contentious socio-political issues. This project aims to develop algorithms and platforms for collaborative decision making at scale. These platforms will be deployed in real decision-making processes, resulting in substantial broad impact.Much of the work will be informed by participatory budgeting, where a group of users collectively produce a budget. Since budgetary constraints can be modeled as convex constraints, the insights from participatory budgeting will then be applied to more general convex decision spaces.On the algorithmic side, the PIs propose to develop algorithms and mechanisms for consensus that go beyond simple voting. In complex decision spaces, the normal voting methodology of ranking a set of candidates breaks down, and we need new mechanisms. For example, for participatory budgeting, the users might be asked to solve a knapsack problem, providing a complete budget. This leads to exciting directions in incentive compatibility, opinion dynamics, fairness, and convex optimization. Indeed, the PIs believe that this is the natural next step in the evolution of social choice theory, and would represent a substantial intellectual advance in both algorithms and mechanism design.On the experimental and evaluation side, this work will take the deliberative polling methodology developed by Co-PI Fishkin, and design tools for extending it to participatory budgeting. This project will also evaluate how deliberative polling can scale to large online communities. This is a natural next step in the evolution of deliberative democracy.On the deployment side, this project will advance our understanding of how to design interfaces for discussion, collaboration, and voting that lead to genuine deliberation and consensus on complex problems, as opposed to devolving into vitriol like many discussion boards and comment threads.

YouTube作为一个娱乐频道与好莱坞竞争，同时也作为一个分销机制补充好莱坞。Twitter与新闻媒体的关系类似，Coursera与大学的关系也类似。但是，作为一个社会，在大规模做出民主决策方面，没有任何在线替代方案。与建立共识和妥协相反，公共讨论板在处理有争议的社会政治问题时往往演变成口水战。该项目旨在开发大规模协作决策的算法和平台。这些平台将在真实的决策过程中部署，产生巨大的广泛影响，其中大部分工作将通过参与性预算编制提供信息，由一组用户集体编制预算。由于预算约束可以建模为凸约束，因此参与式预算的见解将应用于更一般的凸决策空间。在算法方面，PI建议开发超越简单投票的共识算法和机制。在复杂的决策空间中，对一组候选人进行排名的正常投票方法会崩溃，我们需要新的机制。例如，对于参与式预算，用户可能会被要求解决背包问题，提供一个完整的预算。这导致激励相容性，意见动态，公平性和凸优化令人兴奋的方向。事实上，PI认为，这是自然的下一步，在社会选择理论的发展，并将代表一个实质性的智力进步，在算法和机制设计。在实验和评估方面，这项工作将审议民意调查方法开发的共同PI菲什金，并设计工具，将其扩展到参与式预算编制。该项目还将评估审议投票如何扩展到大型在线社区。这是协商民主进化的自然下一步。在部署方面，这个项目将促进我们对如何设计讨论、协作和投票界面的理解，这些界面将导致对复杂问题的真正审议和共识，而不是像许多讨论板和评论线程那样陷入刻薄。