Paradoxes of Game Theoretic Equilibria and Price of Anarchy
For decades, static solution concepts (Nash, Correlated, and Coarse Correlated Equilibria) and the Price of Anarchy (PoA) have formed the bedrock of algorithmic game theory, with no-regret learning proving fast convergence to such game-theoretic equilibria. We show that reducing multi-agent learning to static equilibrium and black-box regret analysis obscures underlying dynamic disequilibrium and game theoretic bounds. First, interior Nash equilibria lack $C^1$ vector field information, meaning agents cannot distinguish aligned from strictly opposing incentives. Inheriting this geometry, the worst-case pure Nash equilibria dictating robust PoA bounds manifest as topologically unstable str...
| Authors | Georgios Piliouras, Ian Gemp, Siqi Liu et al. |
|---|---|
| Primary category | cs.GT |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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