Learning the optimum as a Nash equilibrium
Alemdar, N. M.
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/10983
Journal of Economic Dynamics and Control
- Department of Economics 
This paper shows the computational benefits of a game theoretic approach to optimization of high dimensional control problems. A dynamic noncooperative game framework is adopted to partition the control space and to search the optimum as the equilibrium of a k-person dynamic game played by k-parallel genetic algorithms. When there are multiple inputs, we delegate control authority over a set of control variables exclusively to one player so that k artificially intelligent players explore and communicate to learn the global optimum as the Nash equilibrium. In the case of a single input, each player's decision authority becomes active on exclusive sets of dates-so that k GAs construct the optimal control trajectory as the equilibrium of evolving best-to-date responses. Sample problems are provided to demonstrate the gains in computational speed and accuracy. (C) 2000 Elsevier Science B.V. All rights reserved.
Özyıldırım, S., & Alemdar, N. M. (2000). Learning the optimum as a Nash equilibrium. Journal of Economic Dynamics and Control, 24(4), 483-499. ISO 690