{"id":"W2123268776","doi":"10.1109/aamas.2004.145","title":"Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reputation; Quality (philosophy); Product (mathematics); Reinforcement learning; Purchasing; Exploit; Computer science; Value (mathematics); Business; Marketing; Computer security; Artificial intelligence; Machine learning","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":[0.002836179,0.0002191948,0.0003100609,0.0002002077,0.0003738408,0.0002522157,0.000148741,0.00008943169,0.00002641264],"category_scores_gemma":[0.0002302794,0.0001864958,0.00004681814,0.000419831,0.00008903939,0.0006878322,0.00005314697,0.0001808134,0.00002383533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002037176,"about_ca_system_score_gemma":0.0000846237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004734028,"about_ca_topic_score_gemma":0.0003499227,"domain_scores_codex":[0.9969911,0.0004746495,0.0007735669,0.0008427313,0.0005513935,0.0003665817],"domain_scores_gemma":[0.9986341,0.0002276725,0.0004960765,0.0003415811,0.0001739176,0.0001266258],"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":[0.0009928498,0.001142981,0.1353865,0.000271971,0.0002279689,0.00001429425,0.005630582,0.7454301,0.01479086,0.0200224,0.001509562,0.07457996],"study_design_scores_gemma":[0.00679098,0.0002518297,0.1108839,0.0002313426,0.00008168558,0.00002182518,0.01334615,0.8519894,0.002521198,0.004360235,0.00845,0.001071532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6857045,0.0008196257,0.3123352,0.00008654069,0.0001592991,0.0007666867,0.00003762953,0.00003442684,0.00005610604],"genre_scores_gemma":[0.99703,0.0001182475,0.001074105,0.00006982981,0.00003602604,0.0001309714,0.00001874208,0.00001879176,0.001503332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3113254,"threshold_uncertainty_score":0.7605076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.113563233541252,"score_gpt":0.3651636397994741,"score_spread":0.2516004062582221,"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."}}