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101. Equilibrium selection via approximation and penalization
Invited abstract in session TD-40: Tools and algorithms for equilibrium detection, stream Interfaces Between Optimization, Hierarchical Problems and Equilibrium Detection with Applications.
Tuesday, 14:30-16:00Room: 96 (building: 306)
Authors (first author is the speaker)
| 1. | Giancarlo Bigi
|
| Dipartimento di Informatica, Universita' di Pisa |
Abstract
The selection of equilibria is a central issue in the management of multi-agent systems that can be partially controlled. Once the system has been modelled, the selection can be performed through an hierarchical program whose lower-level describes the equilibria of the system and the upper-level explicitly addresses the selection criterion through a suitable objective function. These hierarchical programs are simpler than more general bilevel structures as the lower-level problems are non-parametric with respect to the upper level variables. In order to tackle them, suitable approximated versions are introduced. On the one hand, the approximation does not perturb the original [exact] program too much and allows for some additional flexibility in the choice. On the other hand, it allows relying on suitable exact penalty schemes by recovering those regularity conditions that the original problems do not satisfy. These penalization approaches are addressed in detail and their convergence properties are established.
Keywords
- Game Theory
- Non-smooth Optimization
- Agent Systems
Status: accepted
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