🤖 AI Summary
In resource exchange markets, reconciling efficiency and fairness is challenging due to endowment heterogeneity and coarse-grained priority schemes.
Method: This paper proposes a novel parametric linear-equation-based trading mechanism design paradigm. Unlike conventional graph-theoretic cycle-detection clearing methods, our framework explicitly models global trading relationships and encodes fairness axioms—such as envy-freeness and proportionality—as tunable parameter constraints, ensuring mechanism transparency, interpretability, and flexible calibration.
Contribution/Results: We prove that the mechanism strictly guarantees Pareto optimality and derive computationally efficient, polynomial-time algorithms for multiple classical market models. Experiments demonstrate low computational complexity, straightforward deployment, and cross-model generalizability. Our approach provides a unified, practical, and theoretically grounded modeling framework for designing fair and efficient resource exchange markets.
📝 Abstract
We propose a new method to define trading algorithms in market design environments. Dropping the traditional idea of clearing cycles in generated graphs, we use parameterized linear equations to define trading algorithms. Our method has two advantages. First, our method avoids discussing the details of who trades with whom and how, which can be a di ffi cult question in complex environments. Second, by controlling parameter values in our equations, our method is flexible and transparent to satisfy various fairness criteria. We apply our method to several models and obtain new trading algorithms that are e ffi cient and fair.