{"id":"W4285200946","doi":"10.1007/978-3-031-08965-7_7","title":"Application and Comparison of CC-Integrals in Business Group Decision Making","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Agencia Estatal de Investigación; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministerio de Ciencia y Tecnología; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Flexibility (engineering); Choquet integral; Preference; Ideal solution; Computer science; Multiple-criteria decision analysis; Group (periodic table); Certainty; TOPSIS; Similarity (geometry); Modular design; Ideal (ethics); Group decision-making; Management science; Mathematics; Artificial intelligence; Mathematical optimization; Operations research; Psychology; Statistics; Engineering; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01698656,0.0008273213,0.001383187,0.003698422,0.00112476,0.003202159,0.001502587,0.00118325,0.003431166],"category_scores_gemma":[0.05928722,0.0002175791,0.0008073986,0.005428506,0.001407777,0.003184249,0.001699907,0.001448302,0.0003140368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002434915,"about_ca_system_score_gemma":0.002432381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005843173,"about_ca_topic_score_gemma":0.003980354,"domain_scores_codex":[0.992148,0.004791232,0.0003180006,0.0003617258,0.002119164,0.0002618232],"domain_scores_gemma":[0.936589,0.05582685,0.0006580604,0.001964184,0.004450431,0.0005114623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001381773,0.0003207108,0.004722214,0.0005922855,0.000162266,0.0001539145,0.0007694784,0.2069885,0.00269833,0.3472987,0.001971237,0.4329405],"study_design_scores_gemma":[0.00004554474,0.0003721114,0.002148398,0.000107131,0.0001069918,0.0001349255,0.0002564438,0.9070415,0.003484077,0.08277521,0.003475737,0.00005196821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1475171,0.004530285,0.8199751,0.0003142027,0.0003510125,0.0002731777,0.0001366292,0.0003963988,0.02650601],"genre_scores_gemma":[0.6556258,0.001275312,0.3399003,0.00005383816,0.0001091515,0.0001927448,0.0001413411,0.0001521878,0.002549206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01698656,"threshold_uncertainty_score":0.08983463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0673348112312594,"score_gpt":0.3843921618588343,"score_spread":0.3170573506275749,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}