resource allocation

This study addresses the “tragedy of the commons” arising from conflicts between individual incentives and collective efficiency in common-pool resource governance. It systematically traces the evolution of modeling approaches—from mid-20th-century deterministic bioeconomic models to contemporary coupled human–environment complex systems frameworks. Integrating institutional realism, behavioral irrationality assumptions, and complex systems perspectives, the research advances modeling paradigms from static optimization toward dynamic resilience by synthesizing classical and evolutionary game theory, stochastic differential equations, agent-based computational modeling, and behavioral economics. The work elucidates how institutional elements such as monitoring, communication, and graduated sanctions foster cooperation, identifies early-warning signals of systemic collapse, and examines the role of spatial heterogeneity, thereby offering theoretical foundations and policy insights for sustainable governance.

This study addresses the allocation of multiple heterogeneous resource types in hierarchical organizations, where certain resources can be transformed into one another. The problem is formulated as a market equilibrium model incorporating structural constraints inherent to the hierarchy. To solve it efficiently, the authors propose a novel two-stage approximation algorithm: first solving a tractable instance that respects the hierarchical structure, then iteratively refining the solution to handle general cases. This work introduces, for the first time, a two-stage approximation framework to hierarchical resource allocation with conversion capabilities, establishing both the guaranteed existence of feasible equilibria and computational efficiency. Experiments on real-world Google TPU/GPU allocation datasets demonstrate the algorithm’s effectiveness and rapid convergence.

This study addresses the joint optimization of personnel assignment and performance evaluation rules in networked organizations where employee output depends on both individual effort and exogenous, uncontrollable advantages. The authors develop a dual-network framework: a competition network that shapes effort decisions and a spillover network that propagates advantage effects. By integrating game-theoretic equilibrium analysis, Katz–Bonacich centrality, and mechanism design theory, they demonstrate that optimal evaluation rules generally deviate from true output—employing negative matching with low weights when effort dominates, and positive matching with high weights when advantages dominate. The work also provides the first quantification of efficiency loss due to pairwise stability constraints and characterizes the intrinsic relationship between equilibrium effort, network position, and effective advantage, offering tailored optimal strategies for diverse production environments.

This work addresses the challenge of utility loss under strict resource constraints in machine learning–based decision-making, where conventional fairness criteria can incur unbounded utility degradation and exacerbate inter-group resource disparities. To mitigate this, the authors propose an enhanced proportional fairness criterion alongside a novel variant of equality of opportunity, both of which guarantee bounded fairness-induced utility costs. Building upon algorithmic fairness theory and resource allocation models, they formulate a constrained optimization framework that systematically quantifies utility loss across different fairness definitions. Theoretical analysis and empirical evaluations demonstrate that the proposed approaches significantly improve robustness while preserving fairness, thereby achieving an effective trade-off between accuracy and fairness under resource limitations.

This study addresses how an agent should dynamically allocate limited effort between novel and established solution approaches to maximize the probability of success when problem difficulty is unknown. By integrating Bayesian learning, dynamic optimization, and mechanism design theory, the authors formulate a principal–agent model that captures both the exploration–exploitation trade-off and moral hazard. The analysis reveals that the optimal policy alternates between trying new and existing methods, and that learning effects lead to front-loaded incentive schemes. These findings offer novel theoretical foundations for designing innovation strategies and dynamic incentives in creative endeavors such as scientific research and product development.

This study addresses a critical gap in algorithmic fairness research, which has predominantly focused on trade-offs between performance and fairness in prediction space while overlooking the real-world utilities of multiple stakeholders and welfare distribution across groups. The authors propose a novel multi-stakeholder framework grounded in welfare economics and distributive justice, formalizing fairness as the social planner’s utility and employing posterior multi-objective optimization to identify optimal trade-offs between decision-maker utility and societal fairness. For the first time, they characterize the fairness–performance Pareto frontier in utility space under both deterministic and randomized policies, theoretically demonstrating that randomization can yield strictly superior trade-offs under certain conditions. Empirical results confirm that simple randomized mechanisms leverage outcome uncertainty to enhance fairness–performance balance, offering a more transparent and equitable design paradigm for algorithmic decision systems.

This work addresses the challenge of effectively translating social preferences into resource allocation objectives within multi-agent control systems to fulfill ethical and socially responsible missions. By aggregating individual preferences into a welfare-oriented control objective, the study unifies this approach across three major control paradigms: online feedback optimization, Markov decision process control, and model predictive control. It presents the first systematic framework that embeds social welfare principles directly into the control design pipeline, integrating preference aggregation with formal verification mechanisms to yield a certifiably compliant control architecture. This framework offers a novel pathway for automated resource allocation systems that simultaneously ensures fairness, efficiency, and interpretability.

This study addresses the problem of dynamic fair allocation of indivisible goods (or tasks) in an online setting, where items arrive sequentially and must be allocated immediately upon arrival. Under a broad range of models—including normalized and non-normalized utilities as well as identical and general additive utility functions—the work designs constructive online algorithms within the competitive analysis framework, targeting multiple fairness criteria such as EF1 and PROP1. For most settings considered, the paper not only presents algorithms achieving optimal competitive ratios but also establishes matching theoretical upper bounds, thereby substantially expanding the theoretical foundations of online fair division.

This work addresses the dynamic task allocation problem under stochastic task arrivals and an average-effort fairness constraint by proposing a novel dynamic pseudomarket mechanism. For the first time, individual preferences are explicitly incorporated into the equilibrium task assignment framework, achieving both asymptotic balance and Pareto efficiency. The theoretical analysis integrates stochastic matching models with asymptotic equilibrium theory and yields a closed-form expression for productivity gains that can be estimated using only aggregate data. Simulation results demonstrate that the proposed mechanism substantially improves average productivity compared to conventional rotation schemes, and its allocations are Pareto superior to those of current practical approaches.

This work addresses the challenge of maximizing end-to-end success probability in structured agent workflows under hard constraints on budget and deadline. The authors propose Monte Carlo Combinatorial Planning (MCPP), a lightweight closed-loop planner that dynamically replans during execution in response to observations. MCPP employs a finite-horizon stochastic online allocation model with parallel sampling and leverages Monte Carlo simulation to estimate, in real time, the probability of successful task completion under the given constraints. Experimental results demonstrate that MCPP significantly outperforms strong baseline methods on the CodeFlow and ProofFlow benchmarks, consistently achieving higher task completion rates across diverse budget–deadline configurations. These findings validate MCPP’s effectiveness and robustness in resource-constrained scenarios.