{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/67171"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/67171","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Understanding the Development and Implementation of Heuristics and Biases in Design","abstract":"Heuristics are rules of thumb used by designers to save time and resources in exchange for satisfactory, but not necessarily optimal, solutions. However, there is a large knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team's training processes, and aiding others on their path to design expertise. Similarly, awareness of heuristics used by other team members could aid a designer's understanding of decisions outside of their own expertise and the collective vision for the team’s final design. Ultimately, describing how heuristics are used may lead to a more normative approach to heuristics, through determining how one heuristic may add more value to the design process over another. This justification should lead to more effective decision making in design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This dissertation presents four exploratory case studies aimed at identifying improvements to heuristic extraction methodology, with participants ranging from space mission concept design, advanced manufacturing, and graduate student design teams. A framework for documenting and updating heuristic knowledge over time is formed based on statistically significant correlations of heuristic attributes, specifically in regards to how often a heuristic is used, how the reliable the heuristic is perceived, and how often the heuristic evolves. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed.","abstract_html":"Heuristics are rules of thumb used by designers to save time and resources in exchange for satisfactory, but not necessarily optimal, solutions. However, there is a large knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team's training processes, and aiding others on their path to design expertise. Similarly, awareness of heuristics used by other team members could aid a designer's understanding of decisions outside of their own expertise and the collective vision for the team’s final design. Ultimately, describing how heuristics are used may lead to a more normative approach to heuristics, through determining how one heuristic may add more value to the design process over another. This justification should lead to more effective decision making in design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This dissertation presents four exploratory case studies aimed at identifying improvements to heuristic extraction methodology, with participants ranging from space mission concept design, advanced manufacturing, and graduate student design teams. A framework for documenting and updating heuristic knowledge over time is formed based on statistically significant correlations of heuristic attributes, specifically in regards to how often a heuristic is used, how the reliable the heuristic is perceived, and how often the heuristic evolves. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed.","abstract_has_math":false,"creators":["Fillingim, Kenton Blane"],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":"Doctoral","degree_discipline":null,"degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":["Fu, Katherine"],"committee_chairs":[],"committee_members":["Paredis, Chris","Linsey, Julie","Saldana, Chris","Kurfess, Tom"],"year":2021,"date_issued":"2021-08-04","date_published":"2021-08-04","updated_at":"2026-07-27T19:49:09Z","subjects":["Heuristics","Design"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1853/67171","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fu, Katherine"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Paredis, Chris","Linsey, Julie","Saldana, Chris","Kurfess, Tom"]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Fillingim, Kenton Blane"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-08-25T13:30:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-08-25T13:30:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-08-04"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Heuristics","Design"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1853/67171"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Heuristics are rules of thumb used by designers to save time and resources in exchange for satisfactory, but not necessarily optimal, solutions. 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This dissertation presents four exploratory case studies aimed at identifying improvements to heuristic extraction methodology, with participants ranging from space mission concept design, advanced manufacturing, and graduate student design teams. A framework for documenting and updating heuristic knowledge over time is formed based on statistically significant correlations of heuristic attributes, specifically in regards to how often a heuristic is used, how the reliable the heuristic is perceived, and how often the heuristic evolves. 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