Making Decisions under Outcome Performativity

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

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Cost Effective Speculation with the Omnipredictor

International audienceModern superscalar processors heavily rely on out-of-order and speculative execution to achieve high performance. The conditional branch predictor, the indirect branch predictor and the memory dependency predictor are among the key structures that enable efficient speculative out-of-order execution. Therefore, processors implement these three predictors as distinct hardware components. In this paper, we propose the omnipredictor that predicts conditional branches, memory dependencies and indirect branches at state-of-the-art accuracies without paying the hardware cost of the memory dependency predictor and the indirect jump predictor. We first show that the TAGE prediction scheme based on global branch history can be used to concurrently predict both branch directions and memory dependencies. Thus, we unify these two predictors within a regular TAGE conditional branch predictor whose prediction is interpreted according to the type of the instruction accessing the predictor. Memory dependency prediction is provided at almost no hardware overhead. We further show that the TAGE conditional predic-tor can be used to accurately predict indirect branches through using TAGE entries as pointers to Branch Target Buffer entries. Indirect target prediction can be blended into the conditional predictor along with memory dependency prediction, forming the omnipredictor