We introduce the notion of performative power, which measures the ability of a firm
operating an algorithmic system, such as a digital content recommendation platform, to …

Predictions about people, such as their expected educational achievement or their credit
risk, can be performative and shape the outcome that they are designed to predict …

Datasets scraped from the internet have been critical to large-scale machine learning. Yet,
its success puts the utility of future internet-derived datasets at potential risk, as model …

Deployed supervised machine learning models make predictions that interact with and
influence the world. This phenomenon is called performative prediction by Perdomo et …

Learning problems commonly exhibit an interesting feedback mechanism wherein the
population data reacts to competing decision makers' actions. This paper formulates a new …

When reasoning about strategic behavior in a machine learning context it is tempting to
combine standard microfoundations of rational agents with the statistical decision theory …

As predictive models are deployed into the real world, they must increasingly contend with
strategic behavior. A growing body of work on strategic classification treats this problem as a …

In performative prediction, the deployment of a predictive model triggers a shift in the data
distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy …

We consider a scenario where multiple agents are learning a common decision vector from
data which can be influenced by the agents' decisions. This leads to the problem of multi …

We study the multi-agent game within the innovative framework of decision-dependent
games, which establishes a feedback mechanism that population data reacts to agents' …