Decision-makers often act in response to data-driven predictions, with the goal of achieving favorable outcomes. In such settings, predictions don't passively forecast the future; instead, predictions actively shape the distribution of outcomes they are meant to predict. This performative prediction setting raises new challenges for learning "optimal" decision rules. In particular, existing solution concepts do not address the apparent tension between the goals of forecasting outcomes accurately and steering individuals to achieve desirable outcomes. To contend with this concern, we introduce a new optimality concept -- performative omniprediction -- adapted from the supervised (non-performative) learning setting. A performative omnipredictor is a single predictor that simultaneously encodes the optimal decision rule with respect to many possibly-competing objectives. Our main result demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which we call outcome performativity. On a technical level, our results follow by carefully generalizing the notion of outcome indistinguishability to the outcome performative setting. From an appropriate notion of Performative OI, we recover many consequences known to hold in the supervised setting, such as omniprediction and universal adaptability.
翻译:决策者往往对数据驱动的预测作出反应,目的是实现有利的结果。在这种环境中,预测不会被动地预测未来;相反,预测会积极影响其预期的结果的分布。这种表现良好的预测环境对学习“最佳”决策规则提出了新的挑战。特别是,现有解决方案概念没有解决准确预测结果的目标与引导个人取得理想结果的目标之间的明显矛盾。为了应对这一关切,我们引入了一种新的最佳概念 -- -- 表现性全面性 -- -- 从受监督的(不完善的)学习环境调整为最佳性 -- -- 功能性全面性概念。表现性综合剂是一个单一的预测体,同时将最佳决策规则与许多可能相互兼容的目标结合起来。我们的主要结果显示,在对表现性预测的自然限制下,存在高效的全方位性,我们称之为结果性。在技术层面上,我们的结果是谨慎地将结果的可分化概念与结果的履行性设定相适应。从适当的执行性OI概念中,我们把许多被监督的适应性适应性后果作为公认的普遍性后果。