{"id":"W2531689911","doi":"10.1145/2967934.2968113","title":"Leveraging Asymmetries in Multiplayer Games","year":2016,"lang":"en","type":"article","venue":"","topic":"Digital Games and Media","field":"Social Sciences","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Leverage (statistics); Variety (cybernetics); Appeal; Turns, rounds and time-keeping systems in games; Game mechanics; Game design; Grandparent; Emergent gameplay; Human–computer interaction; Work (physics); Core (optical fiber); Video game design; Psychology; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002184492,0.00003594618,0.00005695587,0.00005642875,0.00002004264,0.00003893234,0.00008338807,0.00002665422,0.0003255109],"category_scores_gemma":[0.0004271644,0.00002140938,0.00002119365,0.00014281,0.00009521411,0.0002487422,0.00001985966,0.00001902991,0.0001379137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003675613,"about_ca_system_score_gemma":0.0000475307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000411854,"about_ca_topic_score_gemma":0.002081662,"domain_scores_codex":[0.9994839,0.00001946186,0.00007060362,0.00009056816,0.0001405655,0.0001948339],"domain_scores_gemma":[0.9996908,0.0001654455,0.0000125232,0.00005176376,0.00001749822,0.00006201557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002940316,0.00001625534,0.05378022,8.953306e-7,0.000001917694,0.000003390649,0.005499958,1.35848e-7,0.0001060586,0.03286667,0.001528464,0.9061931],"study_design_scores_gemma":[0.0002128602,0.00000596589,0.02196978,0.00002028002,5.429609e-7,1.019291e-7,0.003865829,0.000001308662,0.0004774697,0.0009047395,0.9724768,0.00006432939],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2293113,0.00004634575,0.0001938497,0.003061082,0.0001812291,0.00005052547,5.688988e-7,0.00004400239,0.7671111],"genre_scores_gemma":[0.8390769,0.0000633013,0.0001754348,0.0002090534,0.00006162107,0.000002824652,9.185554e-8,0.000002551036,0.1604082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9709483,"threshold_uncertainty_score":0.3564117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02956823713825045,"score_gpt":0.2948252027161769,"score_spread":0.2652569655779264,"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."}}