{"id":"W4415100966","doi":"10.48550/arxiv.2503.08833","title":"Randomization in Optimal Execution Games","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Nash equilibrium; Uniqueness; Simple (philosophy); Kernel (algebra); Backward induction; Best response; Regular polygon; Sample (material)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002059089,0.0008353406,0.001060568,0.0004724273,0.0005257154,0.001594123,0.001108799,0.001333004,0.003457468],"category_scores_gemma":[0.01201482,0.0004302623,0.0006155217,0.0005422754,0.002248229,0.003174052,0.001300381,0.0016008,0.0002891022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001439172,"about_ca_system_score_gemma":0.001156615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001665489,"about_ca_topic_score_gemma":0.001042966,"domain_scores_codex":[0.9982326,0.0009011431,0.00008281985,0.0002794943,0.0002514395,0.0002524484],"domain_scores_gemma":[0.994743,0.003628439,0.0008345481,0.0003024597,0.0002089965,0.0002826239],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002813031,0.0001552082,0.00107676,0.0001080197,0.00006066781,0.0002298328,0.00009963283,0.4731202,0.003028695,0.5105431,0.0008564403,0.0104402],"study_design_scores_gemma":[0.000054344,0.00008559513,0.0002151389,0.00001069708,0.0000117023,0.00003511547,0.00002583759,0.8049836,0.0006021884,0.1933159,0.0006452137,0.0000147246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2571869,0.0006274412,0.7237335,0.001401699,0.00007482569,0.0001614585,0.0001346267,0.0002762846,0.01640328],"genre_scores_gemma":[0.9654192,0.0002967255,0.02907933,0.0001356598,0.0000499948,0.0001231328,0.00006231098,0.00004899537,0.004784568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003457468,"threshold_uncertainty_score":0.01156634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03625296461344273,"score_gpt":0.2939332410177845,"score_spread":0.2576802764043417,"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."}}