CAREER: A New Theory of Social Choice for More than Two Alternatives: Combining Economics, Statistics, and Computation

该提案旨在产生计算机制，使个人能够为作出更好的集体决策(例如，新闻排名)做出贡献，包括可以聚合在线噪音答案的众包。该提案结合了经济学、统计学和计算的思想，以扩展社会选择机制的能力，以处理大量替代选择，从聚集的偏好中提取基本事实，并解决单个代理可能无法比较某些替代选择的问题。与传统的社会选择理论仅限于在两个选项之间进行选择不同，该项目提出了一种严格的计算选择模型研究，该模型将足够健壮，可以在数千甚至数百万个备选方案中进行区分。该建议可能会对我们构建多代理系统、搜索引擎和推荐系统的方式产生深远影响。所提出的努力可以作为日益增长的计算社会选择领域的催化剂，包括：(1)排名聚合已在许多领域使用，涉及一些影响较大的应用，如新闻排名。然而，这个问题远不能用传统的计算社会选择方法来解决，因为它们要么只适用于两个备选方案，需要完整的排名，要么在揭示基本事实方面做得很差，要么很难计算。这项拟议的研究将开发新的方法，通过为丰富的偏好设计客观、稳健和可计算的社会选择机制来克服这些不足。(2)众包，即将在线工作者的嘈杂答案汇总在一起，以产生对某个问题更好的整体答案。这不能通过现有的计算社会选择技术来解决，因为在线工作者的答案往往是偏序的，工作者可能通过提供虚假答案来操纵结果，而聚合的目的是揭示真实答案。拟议的研究将通过设计直接适用于现有系统的新机制来直接应对这些挑战。

The proposal aims at generating computational mechanisms that will enable individuals to contribute towards making better collective decisions (e.g., news ranking) including crowdsourcing where aggregation of online noise answers can occur. The proposal brings together ideas from economics, statistics, and computation to expand the capabilities of social choice mechanisms to handle large numbers of alternative choices, to extract ground truth from aggregated preferences, and to address problems where individual agents might not be able to compare some alternatives. In contrast to classical social choice theory, which is limited to the selection between two alternatives, the project proposes a rigorous study of a model for computational choice that will be robust enough for discerning between thousands or even millions of alternatives.The proposal could have a profound impact in the way we build multi-agent systems, search engines and recommender systems. The proposed effort can serve as a catalyst in the growing area of computational social choice, including: (1) Rank aggregation has been used in many fields, involving some high impact applications like ranking of news. However, this problem is far from solved using traditional computational social choice methods because they either only work for two alternatives, require full rankings, does poorly in revealing the ground truth, or are hard to compute. The proposed research will develop new methodologies to overcome these deficiencies by designing objective, robust, and computable social choice mechanisms for rich preferences. (2) Crowdsourcing, whereby online workers' noisy answers are aggregated to produce a better overall answer to some question. This cannot be solved by existing computational social choice techniques as the online workers' answers are often partial orders, workers may manipulate the outcome by providing false answers, and the objective of aggregation is to reveal the true answer. The proposed research will directly tackle these challenges by designing new mechanisms, which are directly applicable to existing systems.