{"id":"W206403711","doi":"10.1007/978-1-4419-5904-1_11","title":"Multiple Criteria Approaches to Group Decision and Negotiation","year":2010,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Wilfrid Laurier University","funders":"","keywords":"Negotiation; Multiple-criteria decision analysis; Decision analysis; Management science; Group decision-making; Decision support system; Evidential reasoning approach; Business decision mapping; Computer science; Knowledge management; Operations research; Psychology; Political science; Engineering; Social psychology; Economics; Artificial intelligence; Mathematical economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","sts","open_science","insufficient_payload"],"category_scores_codex":[0.05372393,0.0009008092,0.0008207274,0.02821397,0.004787943,0.01700654,0.01461716,0.0003881751,0.00432239],"category_scores_gemma":[0.01415165,0.0008555932,0.0001896335,0.008566624,0.009379514,0.01265419,0.01536688,0.002537814,0.001194343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003566217,"about_ca_system_score_gemma":0.0006340426,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006016434,"about_ca_topic_score_gemma":0.01933663,"domain_scores_codex":[0.9651355,0.0006487563,0.003199418,0.005103701,0.02370619,0.002206495],"domain_scores_gemma":[0.9890743,0.001559987,0.0002113325,0.003172719,0.005145058,0.0008366235],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005249379,0.0003805637,0.0007298783,0.00003638231,0.00006586654,0.000246075,0.0017063,0.03382092,0.002852383,0.9146951,0.001743356,0.04319818],"study_design_scores_gemma":[0.002661393,0.0004792595,0.03347472,0.001393934,0.00002223742,0.0001103511,0.01013044,0.2434935,0.0007152594,0.1985619,0.5068481,0.002108905],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0532411,0.0001842304,0.02515809,0.04336185,0.01226196,0.01244046,0.00055595,0.000252687,0.8525437],"genre_scores_gemma":[0.553334,0.001863033,0.1636476,0.0003925367,0.00064612,0.001986084,0.0002537881,0.0001452623,0.2777315],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7161332,"threshold_uncertainty_score":0.9997634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3782955830542585,"score_gpt":0.5046880960873662,"score_spread":0.1263925130331077,"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."}}