{"id":"W2094521969","doi":"10.1080/00036846.2014.889801","title":"Inducing risk preferences in multi-stage multi-agent laboratory experiments","year":2014,"lang":"en","type":"article","venue":"Applied Economics","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Context (archaeology); Computer science; Binary number; Implementation; External validity; Outcome (game theory); Proof of concept; Range (aeronautics); Management science; Economics; Risk analysis (engineering); Microeconomics; Social psychology; Psychology; Mathematics; Software engineering; Business","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":[0.04169657,0.001346785,0.001714045,0.0006787837,0.0007344518,0.003154492,0.003799513,0.002459281,0.004176308],"category_scores_gemma":[0.09456506,0.001314451,0.00111395,0.0006447254,0.0041652,0.004252773,0.003020638,0.003603577,0.0005080877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001509774,"about_ca_system_score_gemma":0.001545877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003462558,"about_ca_topic_score_gemma":0.000273622,"domain_scores_codex":[0.9683895,0.02522985,0.001077606,0.00224624,0.002317143,0.0007396343],"domain_scores_gemma":[0.9076664,0.06810311,0.01187172,0.009111858,0.001694907,0.001551969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01189791,0.009228338,0.008133085,0.002615682,0.0007635355,0.0002894728,0.002411663,0.1453688,0.05301258,0.6492498,0.001618276,0.1154109],"study_design_scores_gemma":[0.005053558,0.01724407,0.00593196,0.0004491178,0.000356966,0.0001311455,0.0003202576,0.2789028,0.0294254,0.6553958,0.006474303,0.000314631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3216544,0.0003981374,0.6611435,0.001370588,0.0002154551,0.003065429,0.0001856298,0.0003949406,0.01157188],"genre_scores_gemma":[0.75903,0.0002786342,0.2340623,0.0005712,0.00004973241,0.004102985,0.00006018822,0.00002999376,0.00181496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04169657,"threshold_uncertainty_score":0.2205151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2498274179656936,"score_gpt":0.4041737291836797,"score_spread":0.154346311217986,"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."}}