{"id":"W2802397222","doi":"10.1287/deca.2017.0362","title":"On the Road to Making Science of “Art”: Risk Bias in Market Scoring Rules","year":2018,"lang":"en","type":"article","venue":"Decision Analysis","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Prediction market; Market liquidity; Function (biology); Standard deviation; Bounded function; Scoring rule; Econometrics; Economics; Actuarial science; Computer science; Mathematics; Statistics; Machine learning","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003628248,0.00009696374,0.0003504946,0.001792919,0.0001777009,0.0001036866,0.0004712558,0.00003143189,0.002899011],"category_scores_gemma":[0.001012177,0.00007441486,0.0001671314,0.003831237,0.0001268411,0.0001114797,0.0001115841,0.00009258159,0.0006227482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006224993,"about_ca_system_score_gemma":0.00001621653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003235111,"about_ca_topic_score_gemma":0.0004742507,"domain_scores_codex":[0.9986256,0.00001060417,0.0006208207,0.0003834356,0.0001399759,0.0002196101],"domain_scores_gemma":[0.9986895,0.0001869202,0.0003587794,0.0006294714,0.00008136233,0.00005397812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009458386,0.00009055273,0.8923832,0.000003364877,0.0001685079,0.000003058368,0.0006274636,0.01203682,0.000009722796,0.06497046,0.002620009,0.02699228],"study_design_scores_gemma":[0.0001019436,0.00004665144,0.7174466,0.00003488269,0.00002832364,1.746687e-7,0.00005769727,0.2674713,0.00006227325,0.009218622,0.005409338,0.0001222767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658436,0.00007182743,0.006758306,0.0001582984,0.0001245018,0.00006515133,0.00003132788,0.000004815008,0.02694223],"genre_scores_gemma":[0.9984301,0.0001028562,0.0007722772,0.0002139004,0.000035241,0.000003403375,9.297573e-7,0.000006442458,0.0004348672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2554345,"threshold_uncertainty_score":0.9980125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06304172308103206,"score_gpt":0.2919062619995064,"score_spread":0.2288645389184744,"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."}}