GTJun 12

An Entropy Potential for Type-Composition Games

arXiv:2606.14428v15.1
Predicted impact top 70% in GT · last 90 daysOriginality Highly original
AI Analysis

For researchers in algorithmic game theory, it provides a new tool that simplifies equilibrium analysis and yields constructive algorithms for games where only non-constructive or involved proofs existed before.

The paper introduces a novel class of entropy-inspired log-multinomial potential functions for type-composition games, enabling simple equilibrium existence proofs and efficient algorithms for constructing equilibria in general models, resolving several open problems.

Potential functions are a key tool in theoretical computer science with applications ranging from the runtime analysis of algorithms and data structures, through the analysis of the expected behavior of random processes and search heuristics, to proving the existence of equilibrium states in strategic games. Typically, proofs that employ potential functions are short, elegant, and easy to verify, yet very powerful. Moreover, potential functions are essential ingredients for constructive proofs, in particular in algorithmic game theory. There, a key question is the existence of equilibrium states, but the most powerful theorem in the field -- Nash's theorem -- is unfortunately non-constructive. For many strategic games, potential functions come to the rescue by enabling constructive proofs that sometimes even yield efficient algorithms for finding equilibria. We add to this by providing a novel class of entropy-inspired log-multinomial potential functions for natural game-theoretic settings where rational agents of different types strategically choose actions to maximize their utility. In particular, we consider utility functions that are based on the fraction of same- and other-type agents taking the same action. We demonstrate the versatility of the new potential function class by presenting simple equilibrium existence proofs for two recent game-theoretic models, for which only involved technical proofs were previously known. Even better, the new potential function class yields efficient algorithms for constructing equilibria for much more general models. Thereby, we positively resolve several open problems.

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