{"id":"W2884971059","doi":"10.1109/twc.2018.2885747","title":"A Stackelberg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowdsensing","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":186,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Stackelberg competition; Computer science; Incentive; Service provider; Revenue; Backward induction; Service (business); Mobile telephony; Mobile computing; Computer network; Game theory; Complete information; Bayesian game; Computer security; Business; Microeconomics; Mobile radio; Sequential game; 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","sts"],"consensus_categories":[],"category_scores_codex":[0.0004430562,0.0003170928,0.0003542617,0.0002510377,0.001728508,0.0003544441,0.001883951,0.000196849,0.00001055462],"category_scores_gemma":[0.000009727233,0.0003448107,0.0002746291,0.0007422114,0.0004601222,0.0004562975,0.00004006469,0.0004551384,0.00005263714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002165432,"about_ca_system_score_gemma":0.0002789386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009248922,"about_ca_topic_score_gemma":0.0001329389,"domain_scores_codex":[0.9977551,0.000278981,0.0004675638,0.0006314419,0.0003261975,0.0005407374],"domain_scores_gemma":[0.9958658,0.0004820961,0.0002026063,0.002711434,0.0005705764,0.0001674472],"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":[0.0002267488,0.004908582,0.000008643516,0.0003071854,0.000847429,0.0000064535,0.05551916,0.0162576,0.0529996,0.1917135,0.001653123,0.675552],"study_design_scores_gemma":[0.001300058,0.0005627102,0.00001955652,0.0001815071,0.0001138274,0.0000328455,0.002448077,0.8938708,0.0890816,0.007137649,0.00433347,0.0009179012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005358399,0.00004897859,0.9907355,0.0008386899,0.0006096432,0.0009094973,0.00005681241,0.0005728732,0.0008696206],"genre_scores_gemma":[0.8462516,0.00006438867,0.1525748,0.000286584,0.00007165862,0.0004228744,0.00001469182,0.00004626264,0.0002670882],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8776132,"threshold_uncertainty_score":0.9999004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03813459668068552,"score_gpt":0.2866466521383979,"score_spread":0.2485120554577123,"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."}}