English

Pricing in Resource Allocation Games Based on Lagrangean Duality and Convexification

Computer Science and Game Theory 2020-02-24 v3 Optimization and Control

Abstract

We consider a basic resource allocation game, where the players' strategy spaces are subsets of RmR^m and cost/utility functions are parameterized by some common vector uRmu\in R^m and, otherwise, only depend on the own strategy choice. A strategy of a player can be interpreted as a vector of resource consumption and a joint strategy profile naturally leads to an aggregate consumption vector. Resources can be priced, that is, the game is augmented by a price vector λR+m\lambda\in R^m_+ and players have quasi-linear overall costs/utilities meaning that in addition to the original costs/utilities, a player needs to pay the corresponding price per consumed unit. We investigate the following question: for which aggregated consumption vectors uu can we find prices λ\lambda that induce an equilibrium realizing the targeted consumption profile? For answering this question, we revisit a well-known duality-based framework and derive several characterizations of the existence of such uu and λ\lambda using convexification techniques. We show that for finite strategy spaces or certain concave games, the equilibrium existence problem reduces to solving a well-structured LP. We then consider a class of monotone aggregative games having the property that the cost/utility functions of players may depend on the induced load of a strategy profile. For this class, we show a sufficient condition of enforceability based on the previous characterizations. We demonstrate that this framework can help to unify parts of four largely independent streams in the literature: tolls in transportation systems, Walrasian market equilibria, trading networks and congestion control in communication networks.

Keywords

Cite

@article{arxiv.1907.01976,
  title  = {Pricing in Resource Allocation Games Based on Lagrangean Duality and Convexification},
  author = {Tobias Harks},
  journal= {arXiv preprint arXiv:1907.01976},
  year   = {2020}
}

Comments

40 pages, updated and extended version

R2 v1 2026-06-23T10:11:18.204Z