incentive mechanism design

This study addresses the challenge of equilibrium nonexistence in multi-principal, multi-team settings, where strategic externalities induce interdependence among incentive-compatible mechanisms and potential discontinuities in the mechanism correspondence. To overcome this limitation of classical models, the authors develop a novel framework that jointly characterizes the outcome distribution along honest obedience paths and the feasible sets attainable through unilateral deviations, integrating mechanism design theory, game theory, and set-valued analysis. Within this framework, they establish rigorous conditions for equilibrium existence in environments featuring team production and agency problems, thereby significantly extending the applicability of Myerson’s classic model to more complex, realistic multi-principal contexts.

This paper addresses incentive-compatible mechanism design under concurrent hidden productivity types and hidden flexible actions. To capture the coupling effect between these two forms of private information, we develop— for the first time—a unified framework of joint incentive constraints. Our approach integrates mechanism design theory with sequential optimal control and envelope analysis to derive explicit necessary and sufficient conditions for incentive compatibility. The resulting framework fully characterizes the feasible mechanism set and is operationally tractable: it significantly improves mechanism construction efficiency and enhances analytical tractability and economic interpretability in canonical principal–agent settings—including multitask and dynamic agency models. By providing a general theoretical toolkit, our work advances contract design under complex, multidimensional private information.

This paper investigates finite-horizon continuous-time principal–agent problems where the agent employs measure-valued controls and the principal’s utility is characterized by a martingale-driven backward stochastic differential equation (BSDE). Under moral hazard, conventional PDE-based approaches fail to establish existence of optimal contracts due to degeneracy in regularity. To overcome this, we pioneer the integration of measure-valued controls with the BSDE framework, complemented by compactification techniques, to rigorously prove existence of optimal contracts under general constraint conditions. Our result breaks through long-standing limitations in dynamic contract theory regarding existence proofs, providing a solid foundation for principal–agent models featuring complex control structures and nonstandard information architectures. The analysis accommodates irregular control spaces and path-dependent incentives while preserving analytical tractability within the BSDE paradigm.

This paper studies optimal contract design in a principal–agent framework where the agent sequentially and adaptively performs multi-step actions: selecting an action, observing its outcome, and dynamically deciding whether to continue or stop, ultimately submitting a realized outcome to trigger payment. It innovatively integrates sequential decision-making and the Pandora’s Box model into contract theory to capture adaptive, information-dependent behavior with multiple attempts. Methodologically, it models the agent’s exploration as an optimal stopping problem under uncertainty, with outcomes drawn from known distributions. Theoretical contributions include: (1) proving that linear contracts are efficiently optimal under outcome independence, yielding a polynomial-time algorithm; (2) providing a polynomial-time optimal algorithm for general contracts when the number of possible outcomes is fixed; and (3) establishing computational hardness by showing that constant-factor approximation is impossible under outcome correlation, thereby characterizing the complexity boundary of the problem.

This paper studies optimal contract design in sequential exploration: an agent sequentially opens $n$ costly boxes—each containing a prize—and selects one, while the principal commits upfront to a nonnegative payment contract to incentivize the agent and maximize expected utility (prize value minus payment). It bridges contract theory and the Pandora’s Box problem. Methodologically, it integrates game-theoretic modeling, dynamic programming, optimal stopping theory, and probabilistic analysis. The contributions are threefold: (i) the first polynomial-time algorithm for computing the exact optimal linear contract; (ii) a closed-form characterization of the optimal general (nonlinear) contract under single-prize and i.i.d. box assumptions; and (iii) theoretical guarantees of both principal-optimal utility and incentive compatibility. The framework yields a computationally tractable and interpretable mechanism design paradigm for sequential decision-making under uncertainty.

This study investigates how firms design internal incentive contracts in the presence of demand and production risks when they can hedge exposure using forward markets. Within a continuous-time CARA framework, the authors jointly solve for optimal production, compensation, and static hedging strategies under both in-house production and delegated agency, integrating a moral hazard principal–agent model with competitive forward market equilibrium. The analysis reveals an interplay between external risk transfer and internal incentives: hedging and delegation act as substitutes in risk sharing, and delegation can enhance firm value even when the agent is more risk-averse and equally productive. Hedging reduces the need for incentive-driven risk exposure, leading to lower optimal hedge ratios and higher equilibrium forward prices under delegation. Numerical experiments confirm the robustness of these findings under demand uncertainty.

本文针对AI代理的偏好与能力未知的问题，通过设计一种单边模仿结构机制来激励其诚实与服从，并应用于多个具体场景。

This study addresses the challenge in continuous-time principal–agent problems where existing methods fail to construct optimal contracts under joint control of drift and volatility, due to the breakdown of key structural assumptions. To overcome this limitation, the paper introduces a general incentive contract framework parameterized by a function ψ, which simultaneously satisfies revelation and principal-losslessness properties. Within this framework, two classes of contracts are constructed: the first employs backward stochastic differential equations (BSDEs) to implement an enforcement mechanism that ensures controllability of the agent’s actions; the second leverages second-order BSDEs (2BSDEs) to correct the duality gap and recover optimality without relying on the original restrictive assumptions. This work thus transcends the dependence of prior theory on specific structural conditions and establishes a novel pathway for optimal contract design in general settings.

This study addresses revenue maximization in auction mechanism design when bidders are constrained to underreport their valuations unilaterally. The work proposes that enforcing only one-sided incentive compatibility—sufficient to prevent underbidding—is adequate to achieve the same maximal revenue as full incentive compatibility. By employing linear programming duality in a discrete valuation model, the authors theoretically establish the sufficiency of one-sided incentive compatibility for revenue optimality, thereby substantially simplifying the characterization of feasible allocation rules in multi-agent settings. This result uncovers a tractable pathway for mechanism design under specific bias constraints and offers rigorous theoretical support for practical auction systems where strategic misreporting is limited to downward deviations.

This study addresses the breakdown of multi-unit auction mechanisms under constraints such as the inability to incur interim debt. The authors develop a unified theoretical framework to analyze strategic behavior by constrained bidders and distinguish between two objectives: revenue maximization and consumer surplus maximization. Employing tools from measure theory, they establish that Myerson-style auctions remain optimal for revenue maximization, whereas for consumer surplus maximization, deterministic mechanisms that restrict bidder strategies can strictly outperform classic incentive-compatible designs. The work not only uncovers a fundamental divergence between these two objectives in constrained environments but also provides concrete constructions of optimal mechanisms applicable across a broad range of settings.