{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/69312"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/69312","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Incentive Control Strategies for Decision Problems With Parametric Uncertainties (Large Scale Systems, Game Theory, Stochastic, Coordination, Sensitivity Analysis)","abstract":"The central theme of this thesis is the design of incentive control policies in large scale systems with hierarchical decision structures, under the stipulation that the objective functionals of the agents at the lower level of the hierarchy are uncertain to the top-level controller (the leader). These uncertainties are modeled as a finite-dimensional parameter vector whose exact value constitutes private information to the relevant agent at the lower level. The approach we have adopted is to design incentive policies for the leader such that the dependence of the decision of the agents on the uncertain parameter is minimized. We have identified several classes of problems for which this approach is feasible. In particular, we have constructed policies whose performance is arbitrarily close to the solution of a version of the same problem that does not involve uncertainties. We have also shown that for a certain class of problem wherein the leader observes a linear combination of the agents' decisions, the leader can achieve the performance he would obtain if he had observed each decision separately.","abstract_html":"The central theme of this thesis is the design of incentive control policies in large scale systems with hierarchical decision structures, under the stipulation that the objective functionals of the agents at the lower level of the hierarchy are uncertain to the top-level controller (the leader). These uncertainties are modeled as a finite-dimensional parameter vector whose exact value constitutes private information to the relevant agent at the lower level. The approach we have adopted is to design incentive policies for the leader such that the dependence of the decision of the agents on the uncertain parameter is minimized. We have identified several classes of problems for which this approach is feasible. In particular, we have constructed policies whose performance is arbitrarily close to the solution of a version of the same problem that does not involve uncertainties. We have also shown that for a certain class of problem wherein the leader observes a linear combination of the agents' decisions, the leader can achieve the performance he would obtain if he had observed each decision separately.","abstract_has_math":false,"creators":["Cansever, Derya H."],"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":"2014-12-15T19:04:58Z","date_published":"2014-12-15T19:04:58Z","updated_at":"2026-07-22T22:26:00Z","subjects":["Physics, Electricity and Magnetism"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(UMI)AAI8600138"],"render_values":[{"text":"(UMI)AAI8600138","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/69312","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Cansever, Derya H."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-12-15T19:04:58Z","10000-01-01","1985"]},{"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":["Physics, Electricity and Magnetism"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/69312","(UMI)AAI8600138"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The central theme of this thesis is the design of incentive control policies in large scale systems with hierarchical decision structures, under the stipulation that the objective functionals of the agents at the lower level of the hierarchy are uncertain to the top-level controller (the leader). These uncertainties are modeled as a finite-dimensional parameter vector whose exact value constitutes private information to the relevant agent at the lower level. The approach we have adopted is to design incentive policies for the leader such that the dependence of the decision of the agents on the uncertain parameter is minimized. We have identified several classes of problems for which this approach is feasible. In particular, we have constructed policies whose performance is arbitrarily close to the solution of a version of the same problem that does not involve uncertainties. We have also shown that for a certain class of problem wherein the leader observes a linear combination of the agents' decisions, the leader can achieve the performance he would obtain if he had observed each decision separately.","Made available in DSpace on 2014-12-15T19:04:58Z (GMT). No. of bitstreams: 1 8600138.pdf: 4647425 bytes, checksum: 1183608e409cb8ac39dc04093f1a7df8 (MD5) Previous issue date: 1985","Embargo set by: Seth Robbins for item 69478 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","149 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1985."]},{"key":"dc:title","label":"Title","values":["Incentive Control Strategies for Decision Problems With Parametric Uncertainties (Large Scale Systems, Game Theory, Stochastic, Coordination, Sensitivity Analysis)"]}]}],"canonical_facts":{"dc:creator":["Cansever, Derya H."],"dc:date":["2014-12-15T19:04:58Z","10000-01-01","1985"],"dc:description":["The central theme of this thesis is the design of incentive control policies in large scale systems with hierarchical decision structures, under the stipulation that the objective functionals of the agents at the lower level of the hierarchy are uncertain to the top-level controller (the leader). These uncertainties are modeled as a finite-dimensional parameter vector whose exact value constitutes private information to the relevant agent at the lower level. The approach we have adopted is to design incentive policies for the leader such that the dependence of the decision of the agents on the uncertain parameter is minimized. We have identified several classes of problems for which this approach is feasible. In particular, we have constructed policies whose performance is arbitrarily close to the solution of a version of the same problem that does not involve uncertainties. We have also shown that for a certain class of problem wherein the leader observes a linear combination of the agents' decisions, the leader can achieve the performance he would obtain if he had observed each decision separately.","Made available in DSpace on 2014-12-15T19:04:58Z (GMT). No. of bitstreams: 1 8600138.pdf: 4647425 bytes, checksum: 1183608e409cb8ac39dc04093f1a7df8 (MD5) Previous issue date: 1985","Embargo set by: Seth Robbins for item 69478 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","149 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1985."],"dc:identifier":["http://hdl.handle.net/2142/69312","(UMI)AAI8600138"],"dc:subject":["Physics, Electricity and Magnetism"],"dc:title":["Incentive Control Strategies for Decision Problems With Parametric Uncertainties (Large Scale Systems, Game Theory, Stochastic, Coordination, Sensitivity Analysis)"],"dc:type":["text"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:00Z"}