{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69244"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69244","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multimodel Design of Large Scale Systems With Multiple Decision Makers","abstract":"The central theme of this thesis is multimodeling. It is concerned with modeling and control strategy interaction in a multimodel context. Realistic situations are studied, which allow the decision makers to use different simplified models of the system. Three different approaches to multimodeling are examined. Firstly, within the framework of multiparameter singular perturbations, we demonstrate the well-posedness of an a-priori selected multimodeling scheme, for a class of Nash and team problems. This establishes, in some sense, the "robustness" of this multimodeling scheme to a class of solution concepts and information patterns. Secondly, for a class of weakly-coupled Markov chains, we use a perturbational approach to develop an efficient algorithm for computing near-optimal incentive policies, which allows for multimodeling on the part of the decision makers. Finally, for a class of linear-quadratic problems, we use an input-output approach to restructure the problem, and choose appropriate admissible strategies which induce multimodel solutions.","abstract_html":"The central theme of this thesis is multimodeling. It is concerned with modeling and control strategy interaction in a multimodel context. Realistic situations are studied, which allow the decision makers to use different simplified models of the system. Three different approaches to multimodeling are examined. Firstly, within the framework of multiparameter singular perturbations, we demonstrate the well-posedness of an a-priori selected multimodeling scheme, for a class of Nash and team problems. This establishes, in some sense, the "robustness" of this multimodeling scheme to a class of solution concepts and information patterns. Secondly, for a class of weakly-coupled Markov chains, we use a perturbational approach to develop an efficient algorithm for computing near-optimal incentive policies, which allows for multimodeling on the part of the decision makers. Finally, for a class of linear-quadratic problems, we use an input-output approach to restructure the problem, and choose appropriate admissible strategies which induce multimodel solutions.","abstract_has_math":false,"creators":["Saksena, Vikram Raj"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-12-15T19:04:24Z","date_published":"2014-12-15T19:04:24Z","updated_at":"2026-07-22T22:26:00Z","subjects":["Engineering, Electronics and Electrical"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8302979"],"render_values":[{"text":"(UMI)AAI8302979","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69244","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Saksena, Vikram Raj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:04:24Z","10000-01-01","1982"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69244","(UMI)AAI8302979"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The central theme of this thesis is multimodeling. It is concerned with modeling and control strategy interaction in a multimodel context. Realistic situations are studied, which allow the decision makers to use different simplified models of the system. Three different approaches to multimodeling are examined. Firstly, within the framework of multiparameter singular perturbations, we demonstrate the well-posedness of an a-priori selected multimodeling scheme, for a class of Nash and team problems. This establishes, in some sense, the "robustness" of this multimodeling scheme to a class of solution concepts and information patterns. Secondly, for a class of weakly-coupled Markov chains, we use a perturbational approach to develop an efficient algorithm for computing near-optimal incentive policies, which allows for multimodeling on the part of the decision makers. 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Three different approaches to multimodeling are examined. Firstly, within the framework of multiparameter singular perturbations, we demonstrate the well-posedness of an a-priori selected multimodeling scheme, for a class of Nash and team problems. This establishes, in some sense, the "robustness" of this multimodeling scheme to a class of solution concepts and information patterns. Secondly, for a class of weakly-coupled Markov chains, we use a perturbational approach to develop an efficient algorithm for computing near-optimal incentive policies, which allows for multimodeling on the part of the decision makers. Finally, for a class of linear-quadratic problems, we use an input-output approach to restructure the problem, and choose appropriate admissible strategies which induce multimodel solutions.","Made available in DSpace on 2014-12-15T19:04:24Z (GMT). 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