incentive compatibility

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 study addresses the challenge of achieving incentive compatibility in environments with dispersed information, where traditional direct mechanisms are precluded by the Hurwicz impossibility theorem. The authors propose a class of non-revelation-equivalent mechanisms that circumvent this limitation by constructing a framework of parallel, unlinkable strategic interactions, thereby enabling indirect inference of agents’ preferences. This approach demonstrates that incentive compatibility can be attained without requiring centralized information or direct preference disclosure, thus overcoming a fundamental constraint in classical mechanism design. By establishing the feasibility of such mechanisms, the work expands the theoretical boundaries of mechanism design and offers a novel pathway for designing incentives in distributed information settings.

This study addresses the challenge of ensuring unique equilibrium predictions in strategic environments where decisions can be delegated to intermediaries—such as platforms or cartels. It models two settings: one where intermediaries lack enforcement power, leading to coarse correlated equilibria (CCE), and another where they possess punitive capabilities, yielding individually rational correlated distributions (IRCP). The work characterizes the game structures that guarantee uniqueness in both solution concepts and uncovers a structural link between CCE and IRCP. Building on this connection, it establishes novel conditions for the uniqueness of Nash and correlated equilibria that do not rely on dominant strategies. The resulting equilibria are shown to be robust to informational perturbations, communication protocols, selection mechanisms, and learning dynamics. The paper provides necessary and sufficient conditions for equilibrium uniqueness, offering a theoretical foundation for collusion-resistant mechanism design and significantly enhancing the stability of equilibrium predictions in complex real-world settings.

This work addresses the efficiency–fairness trade-off in multi-agent systems arising from information asymmetry and misaligned incentives. It proposes a learnable linear contract mechanism that models the repeated interaction between a sender with private information and a receiver who relies on that information for decision-making. The mechanism enables the sender to optimize its utility through strategic information pricing, while revealing how communication strategies are sensitive to incentive misalignment and environmental observability. Experimental results demonstrate that the sender can effectively learn optimal communication and pricing policies, substantially increasing its own payoff at the expense of significantly reduced receiver surplus. This quantifies the impact of information pricing on system fairness and highlights the inherent tension between efficiency and equity in strategic information exchange.

This paper addresses governance and safety challenges arising from the deep societal embedding of AI by introducing the “Incentive-Compatible Socio-Technical Alignment Problem” (ICSAP), which bridges the gap between technical AI alignment and socio-institutional contexts. Moving beyond dominant purely technical approaches, it is the first to systematically integrate incentive compatibility (IC) from game theory, establishing a unified framework grounded in mechanism design, contract theory, and Bayesian persuasion. The paper formally defines ICSAP, analyzes the applicability boundaries of each IC-based approach, and outlines preliminary implementation strategies. By embedding socio-technical considerations into AI alignment research, this work advances interdisciplinary AI governance, enabling dynamic, context-sensitive, and human-consensus-driven AI systems. It thus extends the methodological scope of AI alignment beyond algorithmic optimization toward institutional and behavioral coherence.

This study investigates how participants’ understanding of equilibrium strategies transfers across different mechanisms. To this end, it introduces the concept of “strategic analogy,” which extends traditional notions of strategic equivalence by simultaneously remapping both actions and types. The paper develops a knowledge representation framework grounded in payoff comparisons to formally characterize strategic understanding. Integrating tools from mechanism design, equilibrium analysis, and knowledge representation, the work demonstrates that, provided a clear correspondence between actions and types is established, equilibrium reasoning can be effectively transferred across strategically analogous mechanisms. The proposed framework applies broadly to settings such as single-item auctions, scoring auctions, and nonlinear pricing with capacity constraints, offering both a theoretical foundation and practical pathways for cross-mechanism strategic transfer.

This study investigates whether standard matching mechanisms remain effective in markets populated by large language model (LLM) agents. By comparing decentralized free negotiation with centralized mechanism-based markets in a one-to-one matching setting, the authors systematically evaluate the stability and efficiency of LLM agents as decision-makers. Using controlled simulations with canonical mechanisms—Deferred Acceptance (DA), Efficient DA (EADA), and Top Trading Cycles (TTC)—the experiments reveal that mechanism-driven markets substantially outperform free negotiation. LLM agents report their true preferences significantly more often than humans under DA and EADA, supporting the applicability of matching theory to AI-agent markets. However, TTC does not consistently elicit higher truthfulness, highlighting a partial misalignment between the formal properties of mechanisms and the strategic behavior of LLMs.

This study addresses a fundamental challenge in the formal verification of multi-agent systems: determining whether equilibrium strategies exist in multi-player graph games that satisfy given payoff constraints. The work provides a systematic investigation of the constrained existence problem under five distinct equilibrium concepts, integrating computational complexity theory, formal methods, and game theory to deliver a complete characterization of the associated complexity classes. In contrast to classical two-player zero-sum games, this research substantially extends the analytical framework by precisely delineating the computational boundaries of constrained equilibrium existence across different solution concepts, thereby establishing a rigorous theoretical foundation for verifying robustness in multi-agent systems.

This work addresses the loss of incentive compatibility in conventional correlated equilibria under uncertainty in agents’ cost parameters, which can lead to coordination failure. To overcome this limitation, the paper proposes a chance-constrained correlated equilibrium framework that enforces incentive compatibility with a prescribed confidence level, thereby enabling robust non-cooperative coordination. Leveraging stochastic optimization and duality theory, the authors develop a sensitivity analysis to quantify the value of information and characterize the trade-off between confidence levels and system efficiency. Both theoretical analysis and numerical experiments demonstrate that the proposed framework effectively preserves coordination performance under uncertainty, validating strategic prioritization of information acquisition and revealing an intrinsic balance between robustness and efficiency.

This paper studies institutional admission decisions in decentralized many-to-one matching markets under persistent preference uncertainty: applicants gradually reveal their preferences only upon receiving offers, rendering classical stable matching mechanisms inapplicable. To address this, the paper proposes a probabilistic offer mechanism—where institutions send stochastic admission offers based on prior beliefs, and applicants subsequently update their preferences and respond. Combining randomized assignment design, game-theoretic analysis, and expected utility modeling, the paper establishes that this mechanism achieves ex ante market clearing and stability. It further identifies, for the first time, the counterintuitive phenomenon that increased information disclosure may reduce aggregate applicant welfare. The core contribution is the first decentralized matching framework for dynamically revealed preferences that simultaneously guarantees stability, implementability, and analyzable welfare properties.