DIMACS Special Focus on Mechanisms and Algorithms to Augment Human Decision Making

通过机器学习和越来越智能（和自主）的设备，人类和机器的融合有望对日常生活产生变革性的影响。在某些任务上，机器可能已经超过了人类。然而，完全的自主仍然是难以捉摸的，预测未来结果的能力将继续受益于人类智慧多年。除了准确的预测之外，决策还需要深入了解他们所影响的人的偏好或效用函数。决策支持工具必须超越观测数据，积极从利益相关者那里获取信息和偏好，适当奖励贡献者，并以最佳模数公平约束和计算能力实际限制的方式将联合收割机组合在一起。“特别重点”（SF）将研究增强个人和组织决策的工具，旨在通过利用人类和机器智能来大大改善决策支持系统。作为该项目的一部分，将探索的工具包括：（1）从人们那里获得复杂概率和偏好的机制，适当地奖励他们;（2）将人类判断和数据驱动预测结合起来的联合收割机算法;以及（3）聚合潜在冲突偏好的算法。SF将举办研讨会，探索正在进行的研究;访问者计划，允许个人深入挖掘出现的问题;以及测试新算法方法的实施挑战。该项目将涉及各种科学界的大量人员，并使他们接触新思想，新问题和新的合作机会。这些活动鼓励意见、经验、想法和专业知识的多样性，同时也推进了让更多妇女和代表性不足的少数群体参与计算的目标。本科生将通过每年夏天的研究经验接触到SF的主题。研讨会将探讨工作的性质以及在智能机器时代将如何改变。演讲还将讨论当前在投票规则、自动驾驶汽车限制以及用于医疗、经济和其他领域决策的算法的公平性等领域具有全球社会重要性的研究。SF将以计算社会选择、众包民主和众包预测（包括预测市场和评分规则）的最新进展为基础。它将研究组织如何通过奖励人们来减少障碍，以增强对机器智能的输入，从而改善预测和决策，它将研究如何引起个人偏好的准确表示和算法，以将引发的投票转化为组织层面的决策，最大限度地提高基本公理和有效计算的目标。它将强调计算机科学理论中的重要主题，如复杂性和机器学习，以及社会责任，算法公平性，决策辅助以及在机器越来越智能和无处不在的数据世界中出现的人机交互问题。SF将探索获取复杂信息的新思路，包括隐私保护启发、基于市场的启发、下注机制和评分规则等方面的新思路。它将解决算法社会选择的新方向，包括策略验证拍卖，激励兼容机器学习，随机社会选择和迭代投票。将讨论获取标签以外信息的机制和市场（例如，功能）来改进机器学习模型。SF将解决偏好聚合的问题，包括值得信赖的偏好聚合，条件偏好聚合和复杂的评级程序。最后，SF将探索从真实的数据中学习的问题，并将考虑在时间有限的情况下进行启发，从关于概率的定性和定量信息中确定参数值，并利用现有知识为启发过程提供信息。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The convergence of humans and machines-through machine learning and increasingly intelligent (and autonomous) devices-promises a transformational impact on daily life. Already machines may outperform humans at certain tasks. Nevertheless, complete autonomy remains elusive, and the ability predict future outcomes will continue to benefit from human wisdom for many more years. Beyond accurate predictions, decisions require insight into the preferences or utility functions of the people that they impact. Decision-support tools must go beyond observational data to actively elicit both information and preferences from stakeholders, to reward contributors appropriately, and to combine the inputs in a way that is optimal modulo fairness constraints and practical limits on computational power. A "special focus" (SF) will study tools to augment decision making in individuals and organizations, aiming to vastly improve decision-support systems by leveraging both human and machine intelligence. Tools to be explored as part of this project include (1) mechanisms to elicit complex probabilities and preferences from people, rewarding them appropriately (2) algorithms to combine human judgments and data-driven predictions, and (3) algorithms to aggregate potentially conflicting preferences. The SF will feature workshops exploring ongoing research; a visitor program allowing individuals to dig deeper into issues that arise; and implementation challenges that test out new algorithmic approaches. The project will involve a large number of people in various scientific communities and expose them to new ideas, new problems, and new opportunities for collaboration. These activities encourage diversity of opinions, experiences, ideas, and expertise, while also advancing the goal of involving more women and under-represented minorities in computing. Undergraduates will be exposed to topics of the SF through research experiences each summer. The workshops will explore the nature of work and how it will change in the age of intelligent machines. Presentations will also address current research that has global societal importance in areas such as voting rules, restrictions on autonomous vehicles, and fairness of algorithms used to make decisions in the medical, economic, and other domains.The SF will build on recent advances in computational social choice, crowd-sourced democracy, and crowd-sourced forecasting, including prediction markets and scoring rules. It will investigate how organizations can reduce barriers by rewarding people to enhance inputs to machine intelligence, thus improving predictions and decisions, and it will study how to elicit accurate representation of individual preferences and algorithms to turn elicited votes into organization-level decisions that maximize an objective subject to fundamental axioms and efficient computation. It will emphasize important themes in computer science theory such as complexity and machine learning, as well as issues of social responsibility, fairness of algorithms, aids to decision making, and human-computer interactions that arise in a world with increasingly intelligent machines and ubiquitous data. The SF will explore new ideas for eliciting complex information, including new ideas in privacy-preserving elicitation, market-based elicitation, wagering mechanisms and scoring rules. It will address new directions in algorithmic social choice involving strategy-proof auctions, incentive compatible machine learning, randomized social choice, and iterative voting. There will be discussion of mechanisms and markets to elicit information beyond labels (e.g., features) to improve machine learning models. The SF will address questions of preference aggregation, including trustworthy preference aggregation, aggregation of conditional preferences, and complex rating procedures. Finally, the SF will explore issues of learning from real data and will consider elicitation when one has limited time, bounding parameter values from qualitative and quantitative information about probabilities, and leveraging existing knowledge to inform the elicitation process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.