{"title":"合作沟通的形态","authors":"Jordan M. Martel, Edward Dickersin Van Wesep","doi":"10.2139/ssrn.2464406","DOIUrl":null,"url":null,"abstract":"It is common for one who has information to share it cooperatively with one who needs it. Perhaps surprisingly, this information is often not communicated in the simplest possible way. For example, Standard and Poor's assigns ratings of at least \"B-\" to 97% of corporate issues, and segments these issues into 16 categories (AAA, AA, AA-, etc.). A full 7 of these 16 categories are devoted to issues with nearly identical default probabilities, leaving only 9 categories to cover the wide variety of default probabilities found in speculative corporate debt. Equally concerning, Yelp restaurant reviews are predominantly positive, with an average of 3.8 stars out of 5. This limits the site's usefulness in distinguishing the highest quality fare. I show that the purpose of a reviewer generates the optimal distribution of reviews. If it is most important to separate great from good, then reviews will tend to be harsh, in the sense that most reviews will be below average. If it is most important to separate bad from worst, then reviews will tend to be polite, in the sense that most reviews will be above average. Importantly, politeness and harshness are emergent properties of the optimal messaging rule. Results are consistent with casual observation, and provide testable implications across a variety of settings, including credit reports, analyst ratings, credit ratings, wine ratings, referee reports, customer reviews, grade inflation, and letters of recommendation.","PeriodicalId":112243,"journal":{"name":"Vanderbilt University - Owen Graduate School of Management Research Paper Series","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Shape of Cooperative Communication\",\"authors\":\"Jordan M. Martel, Edward Dickersin Van Wesep\",\"doi\":\"10.2139/ssrn.2464406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is common for one who has information to share it cooperatively with one who needs it. Perhaps surprisingly, this information is often not communicated in the simplest possible way. For example, Standard and Poor's assigns ratings of at least \\\"B-\\\" to 97% of corporate issues, and segments these issues into 16 categories (AAA, AA, AA-, etc.). A full 7 of these 16 categories are devoted to issues with nearly identical default probabilities, leaving only 9 categories to cover the wide variety of default probabilities found in speculative corporate debt. Equally concerning, Yelp restaurant reviews are predominantly positive, with an average of 3.8 stars out of 5. This limits the site's usefulness in distinguishing the highest quality fare. I show that the purpose of a reviewer generates the optimal distribution of reviews. If it is most important to separate great from good, then reviews will tend to be harsh, in the sense that most reviews will be below average. If it is most important to separate bad from worst, then reviews will tend to be polite, in the sense that most reviews will be above average. Importantly, politeness and harshness are emergent properties of the optimal messaging rule. Results are consistent with casual observation, and provide testable implications across a variety of settings, including credit reports, analyst ratings, credit ratings, wine ratings, referee reports, customer reviews, grade inflation, and letters of recommendation.\",\"PeriodicalId\":112243,\"journal\":{\"name\":\"Vanderbilt University - Owen Graduate School of Management Research Paper Series\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vanderbilt University - Owen Graduate School of Management Research Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2464406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vanderbilt University - Owen Graduate School of Management Research Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2464406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
拥有信息的人与需要信息的人合作分享信息是很常见的。也许令人惊讶的是,这些信息往往不是以最简单的方式传达的。例如,标准普尔(Standard and Poor's)对97%的公司债券给予至少“B-”的评级,并将这些债券分为16类(AAA、AA、AA-等)。在这16个类别中,整整7个类别都是针对违约概率几乎相同的问题,只剩下9个类别涵盖了投机性公司债务中各种各样的违约概率。同样令人担忧的是,Yelp上的餐厅评论大多是正面的,平均评分为3.8分(满分5分)。这限制了该网站在区分最高质量票价方面的实用性。我展示了审稿人的目的产生了审稿人的最佳分布。如果将优秀与优秀区分开来是最重要的,那么评论就会变得很苛刻,也就是说大多数评论都会低于平均水平。如果区分差和差是最重要的,那么评论就会趋于礼貌,从某种意义上说,大多数评论都会高于平均水平。重要的是,礼貌和苛刻是最优消息传递规则的紧急属性。结果与随机观察一致,并在各种设置中提供可测试的含义,包括信用报告、分析师评级、信用评级、葡萄酒评级、推荐人报告、客户评论、分数膨胀和推荐信。
It is common for one who has information to share it cooperatively with one who needs it. Perhaps surprisingly, this information is often not communicated in the simplest possible way. For example, Standard and Poor's assigns ratings of at least "B-" to 97% of corporate issues, and segments these issues into 16 categories (AAA, AA, AA-, etc.). A full 7 of these 16 categories are devoted to issues with nearly identical default probabilities, leaving only 9 categories to cover the wide variety of default probabilities found in speculative corporate debt. Equally concerning, Yelp restaurant reviews are predominantly positive, with an average of 3.8 stars out of 5. This limits the site's usefulness in distinguishing the highest quality fare. I show that the purpose of a reviewer generates the optimal distribution of reviews. If it is most important to separate great from good, then reviews will tend to be harsh, in the sense that most reviews will be below average. If it is most important to separate bad from worst, then reviews will tend to be polite, in the sense that most reviews will be above average. Importantly, politeness and harshness are emergent properties of the optimal messaging rule. Results are consistent with casual observation, and provide testable implications across a variety of settings, including credit reports, analyst ratings, credit ratings, wine ratings, referee reports, customer reviews, grade inflation, and letters of recommendation.