{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101046"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101046","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics","abstract":"In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I also compare these two intransitive decision heuristics with transitive linear order models and two simple transitive heuristics. For each decision theory, I use two types of probabilistic specifications: distance-based models (which assume deterministic preferences and probabilistic response processes), and mixture models (which assume probabilistic preferences and deterministic response processes). I test 26 such probabilistic models on datasets from three different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all stimulus sets. The frequentist analysis generates little evidence against transitivity. Model selection using Bayes factors suggests extensive heterogeneity across participants and stimulus sets.","abstract_html":"In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I also compare these two intransitive decision heuristics with transitive linear order models and two simple transitive heuristics. For each decision theory, I use two types of probabilistic specifications: distance-based models (which assume deterministic preferences and probabilistic response processes), and mixture models (which assume probabilistic preferences and deterministic response processes). I test 26 such probabilistic models on datasets from three different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all stimulus sets. The frequentist analysis generates little evidence against transitivity. Model selection using Bayes factors suggests extensive heterogeneity across participants and stimulus sets.","abstract_has_math":false,"creators":["Guo, Ying"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Regenwetter, Michel","Köhn, Hans-Friedrich"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:27:24Z","date_published":"2018-09-04T20:27:24Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Transitivity of preference, Probabilistic model, Order-Constrained inference, lexicographic semiorder, similarity model, linear order"],"languages":["en"],"rights":["Copyright 2018 Ying Guo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101046","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Regenwetter, Michel","Köhn, Hans-Friedrich"]},{"key":"dc:creator","label":"Author","values":["Guo, Ying"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:27:24Z","2018-04-23","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Transitivity of preference, Probabilistic model, Order-Constrained inference, lexicographic semiorder, similarity model, linear order"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Ying Guo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101046"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I also compare these two intransitive decision heuristics with transitive linear order models and two simple transitive heuristics. For each decision theory, I use two types of probabilistic specifications: distance-based models (which assume deterministic preferences and probabilistic response processes), and mixture models (which assume probabilistic preferences and deterministic response processes). I test 26 such probabilistic models on datasets from three different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all stimulus sets. The frequentist analysis generates little evidence against transitivity. Model selection using Bayes factors suggests extensive heterogeneity across participants and stimulus sets.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Ying Guo, accepted the attached license on 2018-04-23 at 11:31.","The student, Ying Guo, submitted this Thesis for approval on 2018-04-23 at 11:39.","This Thesis was approved for publication on 2018-04-23 at 16:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12425 on 2018-08-31 at 17:14:18","Made available in DSpace on 2018-09-04T20:27:24Z (GMT). No. of bitstreams: 3 GUO-THESIS-2018.pdf: 600780 bytes, checksum: e5f1ee72f10946d5fd7264cb6b526a11 (MD5) LICENSE.txt: 4205 bytes, checksum: cafc0838ffac844a3bbddebb4b198a16 (MD5) RightsLink Printable License.pdf: 83295 bytes, checksum: b733fb4d83a8d2f461d7973b3a7ce3e3 (MD5) Previous issue date: 2018-04-23"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics"]}]}],"canonical_facts":{"dc:contributor":["Regenwetter, Michel","Köhn, Hans-Friedrich"],"dc:creator":["Guo, Ying"],"dc:date":["2018-09-04T20:27:24Z","2018-04-23","2018-05"],"dc:description":["In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I also compare these two intransitive decision heuristics with transitive linear order models and two simple transitive heuristics. For each decision theory, I use two types of probabilistic specifications: distance-based models (which assume deterministic preferences and probabilistic response processes), and mixture models (which assume probabilistic preferences and deterministic response processes). I test 26 such probabilistic models on datasets from three different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all stimulus sets. The frequentist analysis generates little evidence against transitivity. Model selection using Bayes factors suggests extensive heterogeneity across participants and stimulus sets.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Ying Guo, accepted the attached license on 2018-04-23 at 11:31.","The student, Ying Guo, submitted this Thesis for approval on 2018-04-23 at 11:39.","This Thesis was approved for publication on 2018-04-23 at 16:46.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12425 on 2018-08-31 at 17:14:18","Made available in DSpace on 2018-09-04T20:27:24Z (GMT). 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