Conduct a brief literature review of Performative Prediction and Algorithmic Recourse; summarise in the typst document

[MylesBartlett]

typst project: https://typst.app/project/wHE0EGvje8APHT1pdoj-bm

Since we still need to figure out our exact angle of attack, getting a solid grasp of the current state of Performative Prediction and Algorithmic Recourse seems like a necessary preliminary step. Let's start by having a page or so summarising such.

On the AF side, this 2022 survey coauthored by Scholkopf looks quite excellent.

Some PP/SC papers that may be interesting/relevant:

• Multiplayer Performative Prediction: Learning in Decision-Dependent Games -- excellent game-theoretic perspective on the problem that considers not only the performative aspect but also competition between players (which factors into the distribution map and loss function (if non-separable)). The semi-synthetic ride-share dataset/simulation could certainly be relevant to us. The much-abbreviated version of this paper was published at ICML.
• Strategic Classification in the Dark -- addresses one of the deficiencies of the PP setup raised during the meeting, namely classifier being fully revealed (observable) to the agents/clients/players. Abstract excerpt: "...in many real-life scenarios of high-stake classification (e.g., credit scoring), the classifier is not revealed to the agents, which leads agents to attempt to learn the classifier and game it too. In this paper we generalize the strategic classification model to such scenarios. We define the price of opacity as the difference in prediction error between opaque and transparent strategy-robust classifiers, characterize it, and give a sufficient condition for this price to be strictly positive, in which case transparency is the recommended policy....