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Found 8 papers, 3 papers with code
Principal-Agent Reinforcement Learning
We model the delegated task as an MDP, and study a stochastic game between the principal and agent where the principal learns what contracts to use, and the agent learns an MDP policy in response.
Incentivizing Quality Text Generation via Statistical Contracts
While the success of large language models (LLMs) increases demand for machine-generated text, current pay-per-token pricing schemes create a misalignment of incentives known in economics as moral hazard: Text-generating agents have strong incentive to cut costs by preferring a cheaper model over the cutting-edge one, and this can be done "behind the scenes" since the agent performs inference internally.
Strategy-Proof Auctions through Conformal Prediction
The key novelties of our method are: (i) the formulation of a regret prediction model, used to quantify at test time violations of strategy-proofness; and (ii) an auction acceptance rule that leverages the predicted regret to ensure that for a new auction, the data-driven mechanism meets the strategy-proofness requirement with high probability (e. g., 99\%).
MAC Advice for Facility Location Mechanism Design
We study the $k$-facility location mechanism design problem, where the $n$ agents are strategic and might misreport their location.
Contracting with a Learning Agent
We initiate the study of repeated contracts with a learning agent, focusing on agents who achieve no-regret outcomes.
Delegated Classification
When machine learning is outsourced to a rational agent, conflicts of interest might arise and severely impact predictive performance.
Strategic Representation
In our main setting of interest, the system represents attributes of an item to the user, who then decides whether or not to consume.
Strategic Classification in the Dark
Strategic classification studies the interaction between a classification rule and the strategic agents it governs.
