{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005619668,0.0001486467,0.0001244466,0.0001483014,0.00008353905,0.00008462829,0.0001674839,0.0000383761,0.0003478075],"category_scores_gemma":[0.000001846207,0.0001691142,0.0000331277,0.0004572033,0.000009539719,0.0002862548,0.00002890566,0.000160663,0.0001757668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000723816,"about_ca_system_score_gemma":0.000007788739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008706986,"about_ca_topic_score_gemma":0.001906025,"domain_scores_codex":[0.9991332,0.00001297329,0.000312817,0.0002129004,0.0001331332,0.0001950348],"domain_scores_gemma":[0.9996688,0.000009715378,0.00001233699,0.0002171289,0.00002480501,0.00006720218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007951449,0.00008743349,0.0003861619,0.0003029938,0.00009518176,0.00001177214,0.002786588,0.9835569,0.001414325,0.00431198,0.0009504054,0.006088326],"study_design_scores_gemma":[0.0037137,0.00004503664,0.2895547,0.0001459221,0.0001663895,0.000002372182,0.006408285,0.3280082,0.03842376,0.0002892557,0.3319212,0.001321167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9331197,0.00003400808,0.0369322,0.01010648,0.0003473443,0.0009074126,0.00003823583,0.0009406474,0.01757402],"genre_scores_gemma":[0.9943663,0.00004759387,0.00312749,0.001929631,0.00004128679,0.0001773815,0.0001287435,0.00002398189,0.0001575575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6555486,"threshold_uncertainty_score":0.6896276,"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."}}