Truth and Truthfulness: Experimental Tests of the Bayesian Truth Serum

该提案的目标是从实验上测试并在理论上扩展“贝叶斯真理血清”，这是一种在意见研究或专家判断环境中从受访者那里获得高质量信息的新方法。该方法依赖于一种数学评分算法，该算法鼓励受访者对问题提供诚实和经过仔细考虑的答案，即使只有受访者知道哪一个答案与他们真实的意见最匹配。这种方法可以用来收集对遥远未来事件的真实专家预测(例如，人类是否能活过2100年)，真实表达个人品味或意图(例如，投票的意图)，以及真实的个人行为和特征自我报告(例如，危险的性行为)。评分算法背后的主要思想是给比预测更常见的答案打高分，由回答问题的相同受访者生成预测。根据贝叶斯推理，说真话可以最大限度地提高预期得分，即使受访者确信自己的真实观点代表了少数人的观点。此外，在一个问题(即涉及客观知识的问题)只有一个客观正确答案的情况下，评分算法应该能够识别客观正确的答案，即使大多数受访者支持不同的不正确答案。

The proposal objective is to experimentally test and theoretically extend the "Bayesian Truth Serum", a new method for obtaining superior quality information from respondents in opinion research or expert judgment settings. The method rests on a mathematical scoring algorithm that gives respondents an incentive to provide truthful and carefully considered answers to questions, even though only the respondent knows which answer best matches to their truthful opinion. The method could be used to collect truthful expert forecasts of events in the far future (e.g., whether mankind will survive past year 2100), truthful expression of personal tastes or intentions (e.g., intentions to vote), and truthful self-reports of personal behaviors and characteristics (e.g., risky sexual practices).The main idea behind the scoring algorithm is to give high scores to answers that are more common than predicted, with predictions generated by the same respondents who answer the questions. It follows from Bayesian reasoning that truth-telling maximizes expected score even for a respondent who is sure that their truthful opinion represents a minority view. Furthermore, in situations where there is a single objectively correct answer to a question (i.e., a question dealing with objective knowledge), the scoring algorithm should be able to identify the objectively correct answer even if the majority of respondents endorse a different, incorrect answer.