{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132617"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132617","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Strategic interactions in modern elections and markets","abstract":"This thesis examines strategic interactions in democratic and economic institutions against the backdrop of technological advancements which fuel new strategic capabilities. I investigate four interconnected domains: manipulation of redistricting fairness through strategic campaigning (termed here as 'Votemandering'), computational complexity and optimal strategies in Ranked Choice Voting (RCV), large-scale empirical validation of RCV's mechanism design, and AI-driven strategic behavior in oligopolistic markets. My overarching aim is to identify strategic gaps and guide institutional design in technology-mediated environments, which I do by developing theoretical and algorithmic frameworks through computational modeling, optimization techniques, as well as comprehensive empirical analysis. The findings contribute toward strengthening mechanism design in both electoral and market contexts. In redistricting, I demonstrate that fairness measures can exhibit vulnerability to strategic voter-data manipulation, though efficient regulatory frameworks and non-partisan constraints can enhance robustness. For RCV, I develop efficient algorithms that circumvent computational complexity barriers to uncover its strategic incentives. A large-scale analysis of over 100 diverse real-world elections shows that despite theoretical vulnerabilities, actual strategic dynamics are straightforward and transparent, and allow significant improvements in democratic benefits over prior plurality elections. In market contexts, I find that large language models demonstrate sophisticated strategic capabilities but exhibit autonomous, tacit collusion, sustaining prices up to 200\\% of competitive levels. However, targeted regulation of major firms can effectively restore competitive pricing. Together, these contributions provide computational frameworks for understanding technology-mediated strategic behavior while offering practical tools for preserving institutional integrity in democratic governance and competitive markets.","abstract_html":"This thesis examines strategic interactions in democratic and economic institutions against the backdrop of technological advancements which fuel new strategic capabilities. I investigate four interconnected domains: manipulation of redistricting fairness through strategic campaigning (termed here as 'Votemandering'), computational complexity and optimal strategies in Ranked Choice Voting (RCV), large-scale empirical validation of RCV's mechanism design, and AI-driven strategic behavior in oligopolistic markets. My overarching aim is to identify strategic gaps and guide institutional design in technology-mediated environments, which I do by developing theoretical and algorithmic frameworks through computational modeling, optimization techniques, as well as comprehensive empirical analysis. The findings contribute toward strengthening mechanism design in both electoral and market contexts. In redistricting, I demonstrate that fairness measures can exhibit vulnerability to strategic voter-data manipulation, though efficient regulatory frameworks and non-partisan constraints can enhance robustness. For RCV, I develop efficient algorithms that circumvent computational complexity barriers to uncover its strategic incentives. A large-scale analysis of over 100 diverse real-world elections shows that despite theoretical vulnerabilities, actual strategic dynamics are straightforward and transparent, and allow significant improvements in democratic benefits over prior plurality elections. In market contexts, I find that large language models demonstrate sophisticated strategic capabilities but exhibit autonomous, tacit collusion, sustaining prices up to 200\\% of competitive levels. However, targeted regulation of major firms can effectively restore competitive pricing. Together, these contributions provide computational frameworks for understanding technology-mediated strategic behavior while offering practical tools for preserving institutional integrity in democratic governance and competitive markets.","abstract_has_math":false,"creators":["Deshpande, Sanyukta"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Jacobson, Sheldon H.","Sreenivas, Ramavarapu S.","Wang, Qiong","Garg, Nikhil"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Strategic Behavior","Mechanism Design","Fairness","Rank Choice Voting","Game Theory","Electoral Institutions"],"languages":["en"],"rights":["Copyright 2025 Sanyukta Deshpande"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132617","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jacobson, Sheldon H.","Sreenivas, Ramavarapu S.","Wang, Qiong","Garg, Nikhil"]},{"key":"dc:creator","label":"Author","values":["Deshpande, Sanyukta"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-09-02"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial 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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Strategic Behavior","Mechanism Design","Fairness","Rank Choice Voting","Game Theory","Electoral Institutions"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Sanyukta Deshpande"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132617"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis examines strategic interactions in democratic and economic institutions against the backdrop of technological advancements which fuel new strategic capabilities. I investigate four interconnected domains: manipulation of redistricting fairness through strategic campaigning (termed here as 'Votemandering'), computational complexity and optimal strategies in Ranked Choice Voting (RCV), large-scale empirical validation of RCV's mechanism design, and AI-driven strategic behavior in oligopolistic markets. My overarching aim is to identify strategic gaps and guide institutional design in technology-mediated environments, which I do by developing theoretical and algorithmic frameworks through computational modeling, optimization techniques, as well as comprehensive empirical analysis. The findings contribute toward strengthening mechanism design in both electoral and market contexts. In redistricting, I demonstrate that fairness measures can exhibit vulnerability to strategic voter-data manipulation, though efficient regulatory frameworks and non-partisan constraints can enhance robustness. For RCV, I develop efficient algorithms that circumvent computational complexity barriers to uncover its strategic incentives. A large-scale analysis of over 100 diverse real-world elections shows that despite theoretical vulnerabilities, actual strategic dynamics are straightforward and transparent, and allow significant improvements in democratic benefits over prior plurality elections. In market contexts, I find that large language models demonstrate sophisticated strategic capabilities but exhibit autonomous, tacit collusion, sustaining prices up to 200\\% of competitive levels. However, targeted regulation of major firms can effectively restore competitive pricing. Together, these contributions provide computational frameworks for understanding technology-mediated strategic behavior while offering practical tools for preserving institutional integrity in democratic governance and competitive markets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Sanyukta Deshpande, accepted the attached license on 2025-08-19 at 12:10.","The student, Sanyukta Deshpande, submitted this Dissertation for approval on 2025-08-19 at 12:24.","This Dissertation was approved for publication on 2025-09-02 at 16:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22766 on 2026-02-19 at 18:45:20"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Strategic interactions in modern elections and markets"]}]}],"canonical_facts":{"dc:contributor":["Jacobson, Sheldon H.","Sreenivas, Ramavarapu S.","Wang, Qiong","Garg, Nikhil"],"dc:creator":["Deshpande, Sanyukta"],"dc:date":["2025-12","2025-09-02"],"dc:description":["This thesis examines strategic interactions in democratic and economic institutions against the backdrop of technological advancements which fuel new strategic capabilities. I investigate four interconnected domains: manipulation of redistricting fairness through strategic campaigning (termed here as 'Votemandering'), computational complexity and optimal strategies in Ranked Choice Voting (RCV), large-scale empirical validation of RCV's mechanism design, and AI-driven strategic behavior in oligopolistic markets. My overarching aim is to identify strategic gaps and guide institutional design in technology-mediated environments, which I do by developing theoretical and algorithmic frameworks through computational modeling, optimization techniques, as well as comprehensive empirical analysis. The findings contribute toward strengthening mechanism design in both electoral and market contexts. In redistricting, I demonstrate that fairness measures can exhibit vulnerability to strategic voter-data manipulation, though efficient regulatory frameworks and non-partisan constraints can enhance robustness. For RCV, I develop efficient algorithms that circumvent computational complexity barriers to uncover its strategic incentives. A large-scale analysis of over 100 diverse real-world elections shows that despite theoretical vulnerabilities, actual strategic dynamics are straightforward and transparent, and allow significant improvements in democratic benefits over prior plurality elections. In market contexts, I find that large language models demonstrate sophisticated strategic capabilities but exhibit autonomous, tacit collusion, sustaining prices up to 200\\% of competitive levels. However, targeted regulation of major firms can effectively restore competitive pricing. Together, these contributions provide computational frameworks for understanding technology-mediated strategic behavior while offering practical tools for preserving institutional integrity in democratic governance and competitive markets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Sanyukta Deshpande, accepted the attached license on 2025-08-19 at 12:10.","The student, Sanyukta Deshpande, submitted this Dissertation for approval on 2025-08-19 at 12:24.","This Dissertation was approved for publication on 2025-09-02 at 16:52.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22766 on 2026-02-19 at 18:45:20"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132617"],"dc:language":["en"],"dc:rights":["Copyright 2025 Sanyukta Deshpande"],"dc:subject":["Strategic Behavior","Mechanism Design","Fairness","Rank Choice Voting","Game Theory","Electoral Institutions"],"dc:title":["Strategic interactions in modern elections and markets"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}