# Performative Prediction in a Stateful World

@article{Brown2020PerformativePI, title={Performative Prediction in a Stateful World}, author={Gavin Brown and Shlomi Hod and Iden Kalemaj}, journal={ArXiv}, year={2020}, volume={abs/2011.03885} }

Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called "performative prediction" by Perdomo et al. (2020), who investigated it in a stateless setting. We generalize their results to the case where the response of the population to the deployed classifier depends both on the classifier and the previous distribution of the population. We also demonstrate such a setting empirically, for the scenario of strategic…

## 40 Citations

### Performative Prediction with Neural Networks

- Computer Science, MathematicsArXiv
- 2023

This work assumes that the data distribution is Lipschitz continuous with respect to the model's predictions, a more natural assumption for performative systems, and significantly relax the assumptions on the loss function.

### How to Learn when Data Gradually Reacts to Your Model

- Computer ScienceAISTATS
- 2022

This work proposes a new algorithm, Stateful Performative Gradient Descent (Stateful PerfGD), for minimizing the performative loss even in the presence of these effects, and provides theoretical guarantees for the convergence of Stateful perfGD.

### State Dependent Performative Prediction with Stochastic Approximation

- Computer ScienceAISTATS
- 2022

This paper studies the performative prediction problem which optimizes a stochastic loss function with data distribution that depends on the decision variable by considering a setting where the agent(s) provides samples adapted to the learner's and agent's previous states.

### Performative Prediction with Bandit Feedback: Learning through Reparameterization

- Computer ScienceArXiv
- 2023

A two-level zeroth-order optimization algorithm is developed, where one level aims to compute the distribution map, and the other level reparameterizes the performative prediction objective as a function of the induced data distribution, which allows for provable regret guarantees.

### Performative Reinforcement Learning

- Computer ScienceArXiv
- 2022

This work considers a regularized version of the reinforcement learning problem and shows that repeatedly optimizing this objective converges to a performatively stable policy under reasonable assumptions on the transition dynamics.

### How to Learn when Data Reacts to Your Model: Performative Gradient Descent

- Computer ScienceICML
- 2021

This work introduces performative gradient descent (PerfGD), which is the first algorithm which provably converges to the performatively optimal point and is simple to use.

### Approximate Regions of Attraction in Learning with Decision-Dependent Distributions

- Computer Science
- 2021

This work considers the case where there may be multiple local minimizers of performative risk, motivated by situations where the initial conditions may have significant impact on the long-term behavior of the system.

### Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions

- Computer ScienceArXiv
- 2021

This work considers a company whose current employee demographics affect the applicant pool they interview: the initial demographics of the company can affect the long-term hiring policies of theCompany, and introduces the notion of performative alignment, which provides a geometric condition on the convergence of repeated risk minimization to performative risk minimizers.

### Data Feedback Loops: Model-driven Amplification of Dataset Biases

- Computer ScienceArXiv
- 2022

This work formalizes a system where interactions with one model are recorded as history and scraped as training data in the future, and proposes an intervention to help calibrate and stabilize unstable feedback systems.

### Making Decisions under Outcome Performativity

- Computer ScienceITCS
- 2023

This work demonstrates that efficient performative omnipredictors exist, under a natural restriction of performative prediction, which is called outcome performativity, and introduces a new optimality concept -- Performative omniprediction -- adapted from the supervised (non-performative) learning setting.

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It is proved non-asymptotic rates of convergence for both greedily deploying models after each stochastic update as well as for taking several updates before redeploying, illustrating how depending on the strength of performative effects, there exists a regime where either approach outperforms the other.

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This work proposes a new algorithm, Stateful Performative Gradient Descent (Stateful PerfGD), for minimizing the performative loss even in the presence of these effects, and provides theoretical guarantees for the convergence of Stateful perfGD.

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- Computer ScienceAISTATS
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This paper studies the performative prediction problem which optimizes a stochastic loss function with data distribution that depends on the decision variable by considering a setting where the agent(s) provides samples adapted to the learner's and agent's previous states.

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This work introduces performative gradient descent (PerfGD), which is the first algorithm which provably converges to the performatively optimal point and is simple to use.

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This work considers a company whose current employee demographics affect the applicant pool they interview: the initial demographics of the company can affect the long-term hiring policies of theCompany, and introduces the notion of performative alignment, which provides a geometric condition on the convergence of repeated risk minimization to performative risk minimizers.

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