{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005845356,0.0002024726,0.0003085039,0.0003862131,0.00007007354,0.0001744075,0.0008778074,0.0002386015,0.00004031985],"category_scores_gemma":[0.0001755354,0.0002054352,0.00009990168,0.0005059206,0.00003736396,0.0003208848,0.001120109,0.0004902175,0.00005803015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001156616,"about_ca_system_score_gemma":0.0003076423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007145302,"about_ca_topic_score_gemma":0.00001817685,"domain_scores_codex":[0.9981993,0.0002402911,0.0004166682,0.0006145294,0.000249805,0.000279389],"domain_scores_gemma":[0.9988833,0.00009411949,0.0001442656,0.0006516153,0.0001585372,0.00006814392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001133978,0.0002521242,0.07714509,0.0002632742,0.00005860685,0.00002374711,0.002455018,0.8898717,0.0001230069,0.01547538,0.002462805,0.01175582],"study_design_scores_gemma":[0.003380053,0.00003227043,0.02429771,0.0003690639,0.00001133054,0.000001853156,0.00003051626,0.9665293,0.0006768533,0.001606835,0.002640312,0.0004239537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0606351,0.0002486205,0.9302268,0.001940946,0.0008563263,0.0007268416,0.000005466162,0.0002499518,0.005109967],"genre_scores_gemma":[0.9273262,0.0009749559,0.06416962,0.0007973976,0.0001324526,0.0002797337,0.0001414908,0.0000185683,0.006159568],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8666911,"threshold_uncertainty_score":0.8377404,"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."}}