{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007328777,0.001282753,0.0004466401,0.001235498,0.00225798,0.004478163,0.0016498,0.0008791475,0.002837295],"category_scores_gemma":[0.02244662,0.0006019635,0.0005042266,0.0004103094,0.003359641,0.007180576,0.01005998,0.001754183,0.0004757185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279523,"about_ca_system_score_gemma":0.000967115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006199515,"about_ca_topic_score_gemma":0.001156166,"domain_scores_codex":[0.9860747,0.01003293,0.0006037219,0.0006776584,0.001804827,0.0008060392],"domain_scores_gemma":[0.9906325,0.005629473,0.001169712,0.001195522,0.0005995567,0.0007731784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001458804,0.001123344,0.03016516,0.001772743,0.0002189172,0.001901469,0.2195766,0.01159274,0.05674028,0.3409447,0.004278219,0.330227],"study_design_scores_gemma":[0.0005562765,0.002984528,0.03651489,0.002060319,0.0005619327,0.004399935,0.1113324,0.09171893,0.04278419,0.411282,0.2952567,0.0005479206],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5915456,0.0004550697,0.3256401,0.002295804,0.0001290321,0.0008187018,0.00006700928,0.0005537379,0.07849488],"genre_scores_gemma":[0.9627269,0.0001093391,0.03415558,0.0001826229,0.00002110673,0.0003208878,0.00003326707,0.0000575358,0.002392838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007328777,"threshold_uncertainty_score":0.03875875,"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."}}