cost modeling

This work addresses the problem of chore allocation under a restricted additive cost model—such as assigning paper reviewing tasks—where each item incurs either zero or a fixed cost to an agent. The authors propose a polynomial-time algorithm that simultaneously achieves two strong fairness guarantees, EFX (envy-freeness up to any good) and MMS (maximin share), while attaining a 2-approximation to the optimal social cost. They further establish that this 2-approximation ratio is theoretically tight. When relaxing the fairness requirements slightly, the algorithm yields weaker yet more tractable fairness guarantees, still computable in polynomial time. By bridging combinatorial optimization with fair division theory, this study introduces a novel paradigm for achieving both efficiency and fairness in resource allocation under constrained cost structures.

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.

In first-price auction (FPA) markets, budget constraints induce cross-group interference in A/B testing, undermining causal inference validity. Method: We formally characterize the market interference mechanism under the First-Price Pure Equilibrium (FPPE) model; propose a parallel budget-control testing framework based on submarket partitioning to avoid throughput loss from conventional budget splitting; and construct a first-order bias-corrected, unbiased proxy estimator with established asymptotic normality. Our end-to-end causal inference pipeline integrates FPPE modeling, sensitivity analysis, and plug-in estimation. Results: Experiments on large-scale online market data and semi-synthetic benchmarks demonstrate that the framework significantly reduces estimation bias, ensures nominal coverage of confidence intervals, and improves statistical power—particularly under tight budget constraints.

This paper addresses the challenge of ensuring machine learning fairness under resource constraints, where conventional fairness algorithms—ignoring such constraints—often fail in deployment. We propose the first “resource-constrained fairness” framework, formally defining and quantifying the resource cost required to achieve fairness. We reveal that resource availability fundamentally governs fairness cost and establish a unified evaluation and optimization paradigm jointly incorporating resource budgets and group fairness. Methodologically, we model resource consumption via classifier threshold tuning, and jointly optimize fairness metrics (e.g., equalized odds) subject to explicit resource constraints. Our approach integrates theoretical analysis—including derivation of tight theoretical bounds on achievable fairness under adjustable thresholds—with empirical validation. Results demonstrate that resource scarcity substantially increases fairness cost; the derived theoretical boundaries provide actionable design guidelines for fair model deployment in real-world, resource-limited settings.

This paper studies budget allocation under approval voting, aiming to design simple, consistent, and approximately fair allocation rules. We introduce the *sequential payment* framework: voters dynamically and sequentially pay their budget shares to approved candidates. Within this framework, we propose two novel rules—MP and 1/3-MP—which achieve 2-approximation and optimal 1.5-approximation, respectively, to the average fair share. MP is the unique sequential payment rule (up to degenerate cases) satisfying monotonicity. All sequential payment rules satisfy strong population consistency; moreover, 1/3-MP simultaneously satisfies monotonicity and achieves the optimal constant approximation ratio. Our work integrates game-theoretic modeling, axiomatic fairness analysis, and dynamic mechanism design, yielding a new paradigm for approval-based budgeting that offers both theoretical guarantees and practical implementability.

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 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 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 high cost of small-scale pilot experiments required to fit scaling laws in large-scale model training. Framing the problem as a budget-aware sequential experimental design task, the authors propose an uncertainty-aware active selection strategy that dynamically chooses the most informative experiments from a heterogeneous-cost pool for extrapolation to the target regime. By integrating sequential experimental design, uncertainty quantification, and active learning, the method achieves fitting accuracy comparable to that of exhaustive experimentation using only approximately 10% of the total training budget across diverse scaling law tasks, substantially outperforming conventional experimental design baselines.

This study addresses the risk of discriminatory outcomes in data-driven pricing arising from group disparities in demand models. It investigates mechanisms for incorporating fairness constraints within a two-stage pricing pipeline—comprising demand estimation and price optimization—and systematically compares enforcing group parity or Rawlsian fairness at either the price or demand level. Theoretical analysis reveals that directly imposing fairness on training loss can yield suboptimal outcomes. To address this, the authors propose a novel strategy that applies price fairness during estimation and demand fairness during optimization, deriving conditions under which social welfare is maximized. Empirical evaluations using linear and nonlinear demand models, along with real-world vaccine pricing data, demonstrate that when market structures are similar, price fairness in the estimation stage benefits consumers more, whereas demand fairness in the optimization stage performs better; under Rawlsian fairness, both strategies are equivalent and effective.