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 work addresses the modeling of incentives in agent decision-making, systematically distinguishing *response incentives* (how environmental variables affect the optimal policy), *instrumental control incentives* (whether the agent actively manipulates environment variables, e.g., user preferences), and *influence incentives* (variables the agent alters—intentionally or unintentionally). We propose the Structural Causal Influence Model (SCIM), the first framework unifying influence diagrams with structural causal models. Building on causal reasoning, graph theory, and formal modeling, we derive the first decidable graphical criteria for identifying and classifying all three incentive types in single-decision settings. Our approach enables precise, theoretically grounded incentive attribution, significantly enhancing the interpretability and controllability of agent behavior. Empirically, it improves predictive accuracy of behavioral tendencies in fairness-critical and AI safety–sensitive applications, supporting more robust and transparent autonomous decision-making.

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 paper addresses the ambiguous boundary between “strategic” and “non-strategic” behavior in behavioral game theory—particularly the lack of a rigorous, formal definition of non-strategic behavior under bounded rationality. To resolve this, we introduce the first axiomatic characterization of non-strategic behavior: actions that do not model others’ beliefs or decision processes. Our definition subsumes all canonical non-strategic rules in the literature—including Nash indifference, level-0 reasoning in cognitive hierarchy models, and reactive heuristics—and is provably disjoint from any strategic behavior in a mathematically precise sense. Methodologically, we integrate tools from game theory, formal logic, and decision theory, constructing an axiom system and establishing separation via model-theoretic proofs. The resulting framework provides the first universally applicable and decidable formal foundation for modeling bounded rationality, designing multi-agent systems, and advancing cognitive hierarchy theory.

Existing incentive mechanisms fail to adapt to learning agents whose strategies evolve continuously over time. Method: We propose a two-timescale adaptive incentive mechanism driven by individual externality—the difference between an agent’s marginal cost and the operator’s marginal cost—and updated at a rate slower than the agents’ learning dynamics. Leveraging two-timescale stochastic approximation and differential game theory, we develop a technical framework comprising externality modeling, fixed-point analysis, and convergence proof. Contribution/Results: We establish the first general incentive framework decoupled from agents’ learning dynamics; rigorously guarantee that the Nash equilibrium coincides with the socially optimal solution; and unify treatment across atomic aggregative games and nonatomic routing games. We prove that every fixed point corresponds to an optimal incentive and derive sufficient conditions for global convergence. Numerical validation confirms these conditions hold and convergence is rapid in both canonical game settings.

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 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 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.

Existing agent programming models struggle to coordinate internal decision-making with external interaction behaviors and lack effective abstractions for protocol adherence. This work proposes Kiko, a protocol-based agent programming model that introduces a decider abstraction, enabling agents to select actions and generate protocol-compliant messages exclusively from the set of legally permissible moves, thereby strictly adhering to interaction protocols in decentralized environments. Kiko fully encapsulates low-level communication details, supports flexible decision-making strategies, and integrates formal operational semantics to guarantee correct execution of arbitrary protocols. By doing so, it allows developers to focus on business logic while ensuring correctness and reliability in protocol enforcement within multi-agent systems.

Determining whether multiple simple agents inadvertently form a collective agent with emergent goals and capabilities poses a critical challenge for the safety of advanced AI systems. This work proposes a formal, behavior-based framework: a group is deemed a collective agent when its joint behavior can be effectively predicted by a rational, goal-directed model. Innovatively integrating causal game theory and causal abstraction, the paper establishes—for the first time—a fidelity-preserving mapping between high- and low-level models, providing a rigorous foundation for identifying and quantifying collective agency. The approach successfully resolves the multi-agent incentive paradox in Actor-Critic architectures and enables quantitative assessment of the degree of collective intelligence under diverse voting mechanisms.