A Bayesian truth serum for subjective data. Export

@article{citeulike:488160, abstract = {Subjective judgments, an essential information source for science and policy, are problematic because there are no public criteria for assessing judgmental truthfulness. I present a scoring method for eliciting truthful subjective data in situations where objective truth is unknowable. The method assigns high scores not to the most common answers but to the answers that are more common than collectively predicted, with predictions drawn from the same population. This simple adjustment in the scoring criterion removes all bias in favor of consensus: Truthful answers maximize expected score even for respondents who believe that their answer represents a minority view.}, address = {Massachusetts Institute of Technology, Sloan School of Management, E56-320, 38 Memorial Drive, Cambridge, MA 02139, USA. dprelec@mit.edu.}, author = {Prelec, D.}, citeulike-article-id = {488160}, citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1102081}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/15486294}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=15486294}, day = {15}, doi = {10.1126/science.1102081}, issn = {1095-9203}, journal = {Science}, keywords = {bayesian}, month = {October}, number = {5695}, pages = {462--466}, posted-at = {2008-02-11 21:53:04}, priority = {2}, title = {A Bayesian truth serum for subjective data.}, url = {http://dx.doi.org/10.1126/science.1102081}, volume = {306}, year = {2004} }

TY - JOUR ID - citeulike:488160 L3 - citeulike-article-id:488160 N2 - Subjective judgments, an essential information source for science and policy, are problematic because there are no public criteria for assessing judgmental truthfulness. I present a scoring method for eliciting truthful subjective data in situations where objective truth is unknowable. The method assigns high scores not to the most common answers but to the answers that are more common than collectively predicted, with predictions drawn from the same population. This simple adjustment in the scoring criterion removes all bias in favor of consensus: Truthful answers maximize expected score even for respondents who believe that their answer represents a minority view. IS - 5695 JF - Science SN - 1095-9203 AD - Massachusetts Institute of Technology, Sloan School of Management, E56-320, 38 Memorial Drive, Cambridge, MA 02139, USA. dprelec@mit.edu. EP - 466 TI - A Bayesian truth serum for subjective data. VL - 306 SP - 462 KW - bayesian AU - Prelec, D PY - 2004/10/15/ UR - http://dx.doi.org/10.1126/science.1102081 ER -