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

• 批准号: 1637418
• 负责人: Ashish Goel
• 金额: $ 47.5万
• 依托单位: Stanford 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637418&HistoricalAwards=false
• 关键词: AitF; Collaborative; Research; Fair; Efficient

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作为娱乐频道与好莱坞竞争，同时也作为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，在网上没有其他选择来进行大规模的民主决策。与建立共识和妥协相反，在处理有争议的社会政治问题时，公共讨论板经常演变成激烈的战争。该项目旨在开发大规模协作决策的算法和平台。这些平台将部署在实际的决策过程中，产生广泛的影响。大部分工作将采用参与式预算编制，由一组用户共同编制预算。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随后应用于更一般的凸决策空间。在算法方面，PIs提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常规投票方法失效了，我们需要新的机制。例如，对于参与式预算，可能要求用户解决一个背包问题，提供一个完整的预算。这导致了激励兼容性、意见动态、公平性和凸优化等令人兴奋的方向。事实上，pi相信这是社会选择理论进化的自然下一步，并且将代表算法和机制设计方面的实质性智力进步。在实验和评估方面，这项工作将采用Co-PI Fishkin开发的审议性民意调查方法，并设计将其扩展到参与式预算的工具。该项目还将评估如何将审议性投票扩展到大型在线社区。这是协商民主发展的自然下一步。在部署方面，该项目将促进我们对如何设计用于讨论、协作和投票的界面的理解，从而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沦为刻薄的东西。

项目成果

Relating Metric Distortion and Fairness of Social Choice Rules

将度量扭曲与社会选择规则的公平性联系起来

• DOI: 10.1145/3230654.3230658
• 发表时间: 2018
• 期刊: Systems and Computation
• 作者: Goel, Ashish;Hulett, Reyna;Krishnaswamy, Anilesh K.
• 通讯作者: Krishnaswamy, Anilesh K.

Markets for Public Decision-Making

公共决策市场

• 发表时间: 2018
• 期刊: Web and Internet Economics - 14th International Conference
• 作者: Garg, Nikhil;Goel, Ashish;Plaut, Benjamin
• 通讯作者: Plaut, Benjamin

Implementing the Lexicographic Maxmin Bargaining Solution

• 发表时间: 2018-10
• 期刊: ArXiv
• 作者: Ashish Goel;A. Krishnaswamy

Iterative Local Voting for Collective Decision-making in Continuous Spaces

连续空间集体决策的迭代局部投票

• DOI: 10.1613/jair.1.11358
• 发表时间: 2019
• 期刊: Journal of Artificial Intelligence Research
• 影响因子: 5
• 作者: Garg, Nikhil;Kamble, Vijay;Goel, Ashish;Marn, David;Munagala, Kamesh
• 通讯作者: Munagala, Kamesh

Knapsack Voting for Participatory Budgeting

• DOI: 10.1145/3340230
• 发表时间: 2019-08-01
• 期刊: ACM TRANSACTIONS ON ECONOMICS AND COMPUTATION
• 影响因子: 1.2
• 作者: Goel, Ashish;Krishnaswamy, Anilesh K.;Aitamurto, Tanja
• 通讯作者: Aitamurto, Tanja