{"id":"W4285090456","doi":"10.1145/3490486.3538343","title":"Peer Effects from Friends and Strangers: Evidence from Random Matchmaking in an Online Game","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 23rd ACM Conference on Economics and Computation","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Peer effects; Product (mathematics); Peer-to-peer; Computer science; Peer influence; Psychology; Internet privacy; Business; Social psychology; World Wide Web; Mathematics; Artificial intelligence","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":[0.0003318773,0.0001062876,0.0002206849,0.00005297306,0.0002650246,0.0001642048,0.0003092868,0.00003538791,0.00001598949],"category_scores_gemma":[0.00007926622,0.0001054297,0.00002777881,0.00005892871,0.0001540568,0.0004072336,0.0002937543,0.0001323965,4.174256e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001221067,"about_ca_system_score_gemma":0.00003957894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003392411,"about_ca_topic_score_gemma":0.0009770242,"domain_scores_codex":[0.999213,0.00003104501,0.0002213686,0.0002940442,0.0001084576,0.0001320764],"domain_scores_gemma":[0.9993893,0.0002091954,0.0002240161,0.000062642,0.00006939335,0.00004543435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001295077,0.0006388531,0.5180631,0.00006203473,0.0001350914,0.000001781281,0.2947201,0.00210584,0.0252158,0.04996345,0.00009443076,0.1077045],"study_design_scores_gemma":[0.005426643,0.0009037666,0.427545,0.0005058312,0.0001344876,0.000001410375,0.1655848,0.07977515,0.005016129,0.3138589,0.0002720396,0.0009759614],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964975,0.0001719668,0.000004191659,0.00252993,0.0001629209,0.0002675636,0.00005544847,0.00001271521,0.0002977641],"genre_scores_gemma":[0.9988424,0.0003003118,0.0006495483,0.00009956062,0.00004492325,0.00002997801,0.000009615245,0.000007800296,0.00001581994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2638954,"threshold_uncertainty_score":0.5128334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08598168647289438,"score_gpt":0.335289807474686,"score_spread":0.2493081210017916,"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."}}