{"id":"W2621047429","doi":"10.1287/msom.2020.0925","title":"Incentivized Actions in Freemium Games","year":2020,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cannibalization; Revenue; Computer science; Software deployment; Incentive; Entertainment; Set (abstract data type); Process (computing); Revenue management; Markov decision process; Variety (cybernetics); Key (lock); Marketing; License; Business; Microeconomics; Economics; Markov process; Computer security","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":[0.00511281,0.002857625,0.003137316,0.001399524,0.001175115,0.004167837,0.003261828,0.005185291,0.01100224],"category_scores_gemma":[0.02887275,0.001410143,0.001552137,0.00108687,0.004208326,0.00513291,0.002861318,0.005410143,0.0009595918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004413102,"about_ca_system_score_gemma":0.004733661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007303075,"about_ca_topic_score_gemma":0.005383756,"domain_scores_codex":[0.9950085,0.002559268,0.0002436166,0.0009078508,0.0004718831,0.000808867],"domain_scores_gemma":[0.9645131,0.03019889,0.002121545,0.0006154915,0.0008323836,0.001718569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003814538,0.0003754339,0.001843489,0.000399553,0.0001177591,0.0004394361,0.0003522596,0.68781,0.001017985,0.2886061,0.006144671,0.01251176],"study_design_scores_gemma":[0.0002378919,0.0001740167,0.0004871436,0.00009974469,0.00004488092,0.0001512244,0.0001973258,0.782832,0.0004797592,0.2113859,0.003853355,0.00005678156],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204039,0.001253413,0.8157346,0.006166443,0.0003449364,0.001829272,0.001653251,0.0006300466,0.05198421],"genre_scores_gemma":[0.8886687,0.001083788,0.08923845,0.001147621,0.0001868878,0.001428673,0.0005143763,0.0001807327,0.01755093],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01100224,"threshold_uncertainty_score":0.03680617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175011864632792,"score_gpt":0.2220965966368035,"score_spread":0.2003464779904756,"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."}}