Strategic Apple Tasting

TL;DR

This paper introduces algorithms for online decision-making in strategic settings with apple-tasting feedback, achieving sublinear regret in stochastic and adversarial scenarios, applicable to bandit and classification problems.

Contribution

It formalizes the strategic decision-making problem with apple-tasting feedback and provides algorithms with provable regret bounds for stochastic and adversarial agent sequences.

Findings

Achieves $O(\sqrt{T})$ regret with stochastic agents.

Handles adversarial agents with $O(T^{(d+1)/(d+2)})$ regret.

Algorithms adapt to bandit feedback and strategic classification settings.

Abstract

Algorithmic decision-making in high-stakes domains often involves assigning decisions to agents with incentives to strategically modify their input to the algorithm. In addition to dealing with incentives, in many domains of interest (e.g. lending and hiring) the decision-maker only observes feedback regarding their policy for rounds in which they assign a positive decision to the agent; this type of feedback is often referred to as apple tasting (or one-sided) feedback. We formalize this setting as an online learning problem with apple-tasting feedback where a principal makes decisions about a sequence of $T$ agents, each of which is represented by a context that may be strategically modified. Our goal is to achieve sublinear strategic regret, which compares the performance of the principal to that of the best fixed policy in hindsight, if the agents were truthful when revealing their contexts. Our main result is a learning algorithm which incurs $O (\sqrt{T})$ strategic regret when the sequence of agents is chosen stochastically. We also give an algorithm capable of handling adversarially-chosen agents, albeit at the cost of $O(T^{(d+1)/(d+2)})$ strategic regret (where $d$ is the dimension of the context). Our algorithms can be easily adapted to the setting where the principal receives bandit feedback -- this setting generalizes both the linear contextual bandit problem (by considering agents with incentives) and the strategic classification problem (by allowing for partial feedback).