Learning the optimum as a Nash equilibrium
| dc.citation.epage | 499 | en_US |
| dc.citation.issueNumber | 4 | en_US |
| dc.citation.spage | 483 | en_US |
| dc.citation.volumeNumber | 24 | en_US |
| dc.contributor.author | Özyıldırım, S. | en_US |
| dc.contributor.author | Alemdar, N. M. | en_US |
| dc.date.accessioned | 2016-02-08T10:38:37Z | |
| dc.date.available | 2016-02-08T10:38:37Z | |
| dc.date.issued | 2000 | en_US |
| dc.department | Department of Management | en_US |
| dc.department | Department of Economics | en_US |
| dc.description.abstract | 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. © 2000 Elsevier Science B.V. | en_US |
| dc.description.provenance | Made available in DSpace on 2016-02-08T10:38:37Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2000 | en |
| dc.identifier.doi | 10.1016/S0165-1889(99)00012-3 | en_US |
| dc.identifier.issn | 0165-1889 | |
| dc.identifier.uri | http://hdl.handle.net/11693/25064 | |
| dc.language.iso | English | en_US |
| dc.publisher | Elsevier BV | en_US |
| dc.relation.isversionof | https://doi.org/10.1016/S0165-1889(99)00012-3 | en_US |
| dc.source.title | Journal of Economic Dynamics and Control | en_US |
| dc.subject | Learning | en_US |
| dc.subject | Nash equilibrium | en_US |
| dc.subject | Optimal control | en_US |
| dc.subject | Parallel genetic algorithms | en_US |
| dc.title | Learning the optimum as a Nash equilibrium | en_US |
| dc.type | Article | en_US |
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