slot-based mechanism design

To address resource supply-demand imbalances—manifesting as shortages and surpluses—across globally distributed heterogeneous computing clusters, this paper proposes a resource rationing mechanism grounded in real-world market economics. Methodologically, it introduces a periodic simulated-clock auction framework integrating utilization-driven reserve-price setting, long-term resource quota modeling, and supply-demand equilibrium pricing, enabling dynamic price signals to guide users’ autonomous job placement decisions. Its key contribution lies in being the first to systematically embed microeconomic market mechanisms into large-scale distributed resource allocation, replacing static quota or immediate-scheduling paradigms. Evaluated on the Google experimental market, the mechanism significantly incentivizes user migration toward underutilized clusters: resource utilization variance decreases by 32%, and shortage rate drops by 41%. These results empirically validate that price-based incentives can effectively drive system-level behavioral optimization and achieve global resource equilibrium.

This paper studies dynamic pricing and demand learning under resource constraints, unknown demand distributions, and a hard budget on the number of price changes. A decision-maker must sequentially allocate limited inventory to maximize revenue while respecting both inventory and switching budgets. We establish matching upper and lower regret bounds, revealing that the optimal regret exhibits piecewise-constant dependence on the switching budget—thereby characterizing the fundamental statistical impact of joint resource and switching constraints. We propose a robust algorithm integrating online learning, confidence interval analysis, and adaptive switching control. Theoretically, we prove that regret decays nonlinearly with the switching budget. Empirically, the algorithm achieves near-optimal revenue even when the number of price changes is reduced by 90%, significantly enhancing deployability in industrial settings.

This paper addresses the inefficiency of standard mechanisms (e.g., VCG) in multidimensional type environments where agents hold both private preferences and shared, uncertain state information affecting common values. To restore social efficiency, we propose a novel mechanism design framework that integrates posterior behavioral data (e.g., user feedback) into incentive-compatible allocation. Our key innovation is the first incorporation of a state estimator directly within a VCG-style mechanism, yielding a theory of implementation grounded in posterior equilibrium. The framework unifies three canonical settings: full revelation, affine utilities, and consistent estimation—achieving exact social optimality in the first two, and asymptotic optimality in the third, with estimation error decaying at an explicit rate as estimator accuracy improves. Methodologically, we bridge Bayesian mechanism design, state estimation theory, and VCG extensions. We validate the framework through formal models of digital advertising auctions and LLM-based human–AI interaction.

This paper addresses dynamic pricing for reusable resources (e.g., cloud services, leased equipment) under user heterogeneity and non-memoryless usage duration distributions. Method: We propose a computationally tractable inventory-dependent threshold policy, integrating fluid approximation, stochastic process analysis, and local geometric characterization of the revenue function, with explicit inventory-state feedback. Contributions/Results: We establish, for the first time, that only two prices suffice to achieve $o(sqrt{c})$ steady-state performance loss—breaking the $Theta(sqrt{c})$ theoretical barrier of static pricing—and derive a tight $O((log c)^2)$ bound. The policy retains optimal asymptotic performance under extensions to multiple resource types and customer classes. Numerical experiments demonstrate its significant superiority over static pricing even in finite-capacity settings.

Existing undergraduate course allocation systems heavily rely on seat reservation, resulting in inefficiency, inequity, and low student satisfaction. To address these limitations, this paper proposes the first competitive equilibrium–based course allocation mechanism that jointly incorporates student preferences and course priorities. The mechanism guarantees stability, strategy-proofness, and approximate envy-freeness while achieving Pareto efficiency. Methodologically, it integrates stable matching theory, empirically grounded preference estimation, and counterfactual evaluation—thereby departing from conventional reservation-based paradigms. Empirical evaluation demonstrates significant improvements in fairness and overall satisfaction: the number of students envious of lower-priority peers declines by 8% (approximately 500 students). This work provides a theoretically rigorous yet practically implementable framework for optimizing higher education resource allocation.

This study investigates the impact of prior information, Bayesian incentive compatibility (BIC), and resource augmentation on approximation ratios in unrelated machine scheduling. Through randomized algorithm design and resource augmentation analysis, it rigorously proves that randomized BIC mechanisms outperform deterministic ones in two-machine settings, identifies an 8/7 integrality gap, and proposes a collision-parameter-based resource augmentation approximation scheme. The results establish a 4/3 asymptotic lower bound and improve the prior-free randomized approximation ratio to 7/4. Furthermore, by leveraging sampling-level augmentation, the work achieves a (1+ε)-approximation, providing theoretical foundations for efficient scheduling under specific conditions.

研究解决了大规模激励分配问题，通过近似实时维护预算与利润之间的权衡曲线，适用于有基数和拟阵约束的情况。

This study addresses strategic monopolization and resource congestion in railway slot allocation arising from disparities in operator scale. The authors propose a novel multi-agent repeated auction mechanism integrating congestion pricing with asymmetric incentives. By incorporating congestion-sensitive base prices and scale-adjustment rules, the mechanism harmonizes efficiency and fairness under transparent governance. Leveraging an incomplete-information repeated game framework and validated through a real-time web-based multi-agent platform with human participants, this work provides the first empirical evidence—within a genuine human–agent interaction environment—that the mechanism effectively curbs strategic dominance by large operators. Results demonstrate the system’s capacity to dynamically respond to aggregate demand and activate corrective incentives; however, large operators persistently adopt high-request strategies. Qualitative analysis reveals underlying strategic motives, including maintaining market presence and imposing higher costs on competitors.

本文研究带奖励的竞标游戏，通过引入新技巧消除次优玩法，解决无限配置空间问题，并提出一种保证可达性玩家获胜的新策略类型。

This study addresses the online discrete fair allocation problem under generalized budget constraints, where items arrive sequentially and must be irrevocably assigned to agents or a charity, with envy-freeness evaluated only over feasible subsets for each recipient. The work identifies “bounded density extension” as a key structural condition, establishing the first optimal deterministic approximation bounds. It proposes a learning-augmented framework based on joint value–size-type predictions that achieves both consistency and robustness. Theoretical analysis shows the algorithm guarantees feasible envy-free approximations for arbitrary item sizes and attains optimality under homogeneous valuations and small-item regimes. Moreover, resource augmentation significantly strengthens fairness guarantees, and joint prediction strictly outperforms marginal prediction in enhancing allocation quality.