{"id":"W4285308691","doi":"10.2139/ssrn.4118206","title":"Multi-Agent Reinforcement Mechanism Design for Dynamic Pricing-Based Demand Response in a Smart Charging Network","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Mechanism (biology); Dynamic pricing; Mechanism design; Reinforcement; Reinforcement learning; Demand response; Computer science; Microeconomics; Economics; Engineering; Artificial intelligence; Structural engineering; Electrical engineering","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":[0.001920088,0.000674742,0.00114967,0.0004372494,0.0005411392,0.00118055,0.001761326,0.001135069,0.002506053],"category_scores_gemma":[0.002808776,0.0004656125,0.0004571887,0.0003361675,0.0007241605,0.0008126399,0.0010826,0.0009351105,0.00024347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009420355,"about_ca_system_score_gemma":0.001062044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003875919,"about_ca_topic_score_gemma":0.003063395,"domain_scores_codex":[0.999278,0.0002993096,0.00003253537,0.0001251128,0.0001257092,0.0001393591],"domain_scores_gemma":[0.9986637,0.0006174703,0.0002247642,0.0000606822,0.0003109515,0.0001224896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009918124,0.00006889361,0.0002365282,0.00003960262,0.00003066493,0.00007845066,0.00003559115,0.9823324,0.00170524,0.006116484,0.0003513327,0.008905565],"study_design_scores_gemma":[0.000008754362,0.00002071657,0.00002140839,0.000001060975,0.000004149435,0.000005764337,0.000003017744,0.9992561,0.00008936794,0.0005289724,0.0000588382,0.000001835714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04396335,0.0001366627,0.9511643,0.0002492921,0.00006334536,0.0001152106,0.00002592404,0.0002054105,0.004076398],"genre_scores_gemma":[0.9796948,0.0000496854,0.01872532,0.00003838061,0.00001454989,0.00008242188,0.00001275154,0.00001393755,0.001368138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003875919,"threshold_uncertainty_score":0.01015455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064101681310471,"score_gpt":0.2168912490026526,"score_spread":0.2062502321895479,"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."}}