{"id":"W2097916930","doi":"","title":"Estimation of Dynamic Discrete Games Using the Nested Pseudo Likelihood Algorithm: Code and Application","year":2009,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Code (set theory); Computer science; Estimation; Maximum likelihood; Algorithm; Sequential game; Game theory; Mathematics; Programming language; Statistics; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006278593,0.0004165853,0.0006055921,0.0004520731,0.00050829,0.00008932342,0.0009950132,0.0002039589,0.00004728586],"category_scores_gemma":[0.00006997374,0.0004283159,0.0002912941,0.0003350953,0.0006057001,0.0003974078,0.001786711,0.0006970703,0.000003447992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008858257,"about_ca_system_score_gemma":0.0001395238,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008836596,"about_ca_topic_score_gemma":0.002352957,"domain_scores_codex":[0.997983,0.000137147,0.0004268684,0.0006355603,0.0004699009,0.0003475542],"domain_scores_gemma":[0.9977203,0.0001954283,0.0009158392,0.0008918311,0.0002334893,0.00004312789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004039616,0.0002602364,0.006450342,0.001311757,0.0003115929,0.00003149782,0.005250401,0.002040575,0.002215683,0.0006885131,0.0001561361,0.9808793],"study_design_scores_gemma":[0.0007066822,0.0000252377,0.07502863,0.0006459203,0.0008694844,0.00001353646,0.003995304,0.9124842,0.00001188085,0.004315295,0.001421755,0.0004820777],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9229487,0.0008422444,0.06941438,0.00113762,0.0001580491,0.001309937,0.0003225005,0.0001400209,0.00372653],"genre_scores_gemma":[0.9788082,0.0005936836,0.01978245,0.00008352097,0.00006739276,0.000002023664,0.0004751183,0.00004282188,0.0001447841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9803972,"threshold_uncertainty_score":0.9998168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335657042893489,"score_gpt":0.2335187863878823,"score_spread":0.2201622159589474,"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."}}