{"id":"W2307344796","doi":"10.1109/tsmcc.2006.875423","title":"Understanding representational sensitivity in the iterated prisoner's dilemma with fingerprints","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Prisoner's dilemma; Computer science; Representation (politics); Artificial intelligence; Markov chain; Theoretical computer science; Dilemma; Iterated function; Machine learning; Finite-state machine; Algorithm; Mathematics","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":[0.0009561214,0.0001041424,0.0001466412,0.00006126351,0.0007694168,0.0001473972,0.00005450685,0.00005791408,0.00001406875],"category_scores_gemma":[0.000002819779,0.00007397445,0.00002521522,0.0003215509,0.0002271988,0.0001153261,9.585988e-7,0.0001297935,0.00001137829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005486142,"about_ca_system_score_gemma":0.00003364575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006225379,"about_ca_topic_score_gemma":0.003171053,"domain_scores_codex":[0.9987077,0.0004856306,0.000258801,0.0002282449,0.0001814189,0.0001382467],"domain_scores_gemma":[0.9994936,0.0001721956,0.00008421738,0.0001591128,0.00004873643,0.00004211523],"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":[0.00005629877,0.0004845037,0.002989051,0.0001320629,0.00003569937,0.000007724691,0.00474937,0.008393584,0.000203788,0.9750786,0.000949281,0.006920033],"study_design_scores_gemma":[0.002065882,0.0003014648,0.01910449,0.00129076,0.0003233532,0.0002612008,0.01599208,0.01012373,0.0001462683,0.0137441,0.9352939,0.001352739],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1411929,0.002908183,0.8290781,0.002757344,0.0001952949,0.003912699,0.00005183666,0.00009293799,0.01981066],"genre_scores_gemma":[0.9961688,0.002030163,0.00008439877,0.00007195614,0.00009341773,0.0003889208,0.00001021595,0.000006146698,0.001145987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9613345,"threshold_uncertainty_score":0.591781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06409947645518549,"score_gpt":0.288453556724364,"score_spread":0.2243540802691785,"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."}}