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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Bicretieria Optimization in Routing Games
Costas Busch, Rajgopal Kannan · 2008-02-01 · via cs.DS updates on arXiv.org

Two important metrics for measuring the quality of routing paths are the maximum edge congestion $C$ and maximum path length $D$. Here, we study bicriteria in routing games where each player $i$ selfishly selects a path that simultaneously minimizes its maximum edge congestion $C_i$ and path length $D_i$. We study the stability and price of anarchy of two bicriteria games: - {\em Max games}, where the social cost is $\max(C,D)$ and the player cost is $\max(C_i, D_i)$. We prove that max games are stable and convergent under best-response dynamics, and that the price of anarchy is bounded above by the maximum path length in the players' strategy sets. We also show that this bound is tight in worst-case scenarios. - {\em Sum games}, where the social cost is $C+D$ and the player cost is $C_i+D_i$. For sum games, we first show the negative result that there are game instances that have no Nash-equilibria. Therefore, we examine an approximate game called the {\em sum-bucket game} that is always convergent (and therefore stable). We show that the price of anarchy in sum-bucket games is bounded above by $C^* \cdot D^* / (C^* + D^*)$ (with a poly-log factor), where $C^*$ and $D^*$ are the optimal coordinated congestion and path length. Thus, the sum-bucket game has typically superior price of anarchy bounds than the max game. In fact, when either $C^*$ or $D^*$ is small (e.g. constant) the social cost of the Nash-equilibria is very close to the coordinated optimal $C^* + D^*$ (within a poly-log factor). We also show that the price of anarchy bound is tight for cases where both $C^*$ and $D^*$ are large.