Price of Anarchy

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9 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Formalizing Cooperation and Competition: An AI-Assisted Mathematical Addendum to a 2018 Planning Thesis
Oct 21, 2026Haitham Masaud AmarPrice of Anarchy Cooperative Game Theory
Price of Anarchy of First-Price Auctions in Agentic Markets
Oct 14, 2026Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal +3Price of Anarchy Mechanism Design
Google DeepMind · Google Research
Agent-based Modeling: Equilibrium, Echo Chambers, and Efficiency in Hybrid Coevolutionary Opinion Games
Sep 23, 2026Ming-Zhi Jiang, An-Tzu Teng, Jun-En Liu +2Opinion Dynamics Game Theory
Department of Information Management & Finance, National Yang Ming Chiao Tung University, Taiwan. · Institute of Information Management, National Yang Ming Chiao Tung University, Taiwan.
Deriving the Pure Price of Anarchy for Networked Resource Allocation Games
Sep 10, 2026Vartika Singh, Philip N. BrownMechanism Design Game Theory
University of Colorado Colorado Springs, Colorado Springs, CO, 80918 USA · Politecnico di Torino, Italy
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search
Jul 20, 2026Yohei NakajimaNash Equilibrium Game Theory
Untapped Capital
Paradoxes of Game Theoretic Equilibria and Price of Anarchy
Jul 13, 2026Georgios Piliouras, Ian Gemp, Siqi Liu +1Nash Equilibrium Game Theory
Google Deepmind, UK
The Price of Anarchy in Disaggregated Inference
Jun 11, 2026Athos GeorgiouLLM Inference Efficiency Game Theory
NCA
When Individually Calibrated Models Become Collectively Miscalibrated
May 14, 2026Zhaohui WangProbability Calibration Game Theory
USC Viterbi School of Engineering, University of Southern California
Efficiency of Proportional Mechanisms in Online Auto-Bidding Advertising
Apr 14, 2026Nguyen Kim ThangPrice of Anarchy Mechanism Design
LIG, Grenoble INP, University Grenoble-Alpes, France

Oct 21, 2026J/K move · Enter open · S save

Formalizing Cooperation and Competition: An AI-Assisted Mathematical Addendum to a 2018 Planning Thesis

Haitham Masaud Amar

Price of Anarchy Cooperative Game Theory Game Theory Multi-Agent Planning Multi-Agent Negotiation

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We present an AI-assisted mathematical addendum to Strategic Trip Planning: Striking a Balance Between Competition and Cooperation (Amar, 2018). We reconstruct selected relationships between strategic choice, cooperative stability, participant approval and enforcement. The thesis's Nash-to-core proposition is under-specified until outsider opportunities are stated. We verify two-person implications under fixed outsiders and explicit efficiency conditions. Under isolation, the original example has an empty TU core; its funded counterpart uses an explicit external subsidy. For a monotone resource model we characterize the exact financing gap. A finite negotiation procedure implemented in Lean is proved to terminate with participant approval, joint-cost non-worsening and local optimality against permitted approved moves. Supporting formalizations address bargaining geometry and regret-based efficiency. A resource-game counterinstance shows why monotonicity alone provides no uniform price-of-anarchy bound. Reproducible computations compare a documented heuristic reconstruction with the verified procedure on 44 source-based instances and 96 starting cases from four additional synthetic network families. Acceptance-matched heuristic and randomized pairwise-search controls contextualize the optimization results. The mathematical ingredients are established results; the contribution is a traceable reconstruction linking source statements, explicit assumptions, checked proofs, executable code and retained counterinstances. The study illustrates how AI-assisted investigation can turn engineering intuitions from legacy research into precise mathematical claims whose conditions and computational consequences can be inspected and reproduced.