T. A. Montgomery, Paul M. Stieg, Michael J. Cavaretta, P. Moraal
{"title":"主持企业预测市场的经验:超出预测的好处","authors":"T. A. Montgomery, Paul M. Stieg, Michael J. Cavaretta, P. Moraal","doi":"10.1145/2487575.2488212","DOIUrl":null,"url":null,"abstract":"Prediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described. Over the last few years, Ford Motor Company obtained practical experience by deploying one of the largest corporate prediction markets known. Business partners in the US, Europe, and South America provided questions on new vehicle features, sales volumes, take rates, pricing, and macroeconomic trends. We describe our experience, including both the strong and weak correlations found between predictions and real world results. Evaluating this methodology goes beyond prediction accuracy, however, since there are many side benefits. In addition to the predictions, we discuss the value of comments, stock price changes over time, the ability to overcome bureaucratic limits, and flexibly filling holes in corporate knowledge, enabling better decision making. We conclude with advice on running prediction markets, including writing good questions, market duration, motivating traders and protecting confidential information.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"21 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Experience from hosting a corporate prediction market: benefits beyond the forecasts\",\"authors\":\"T. A. Montgomery, Paul M. Stieg, Michael J. Cavaretta, P. Moraal\",\"doi\":\"10.1145/2487575.2488212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described. Over the last few years, Ford Motor Company obtained practical experience by deploying one of the largest corporate prediction markets known. Business partners in the US, Europe, and South America provided questions on new vehicle features, sales volumes, take rates, pricing, and macroeconomic trends. We describe our experience, including both the strong and weak correlations found between predictions and real world results. Evaluating this methodology goes beyond prediction accuracy, however, since there are many side benefits. In addition to the predictions, we discuss the value of comments, stock price changes over time, the ability to overcome bureaucratic limits, and flexibly filling holes in corporate knowledge, enabling better decision making. We conclude with advice on running prediction markets, including writing good questions, market duration, motivating traders and protecting confidential information.\",\"PeriodicalId\":20472,\"journal\":{\"name\":\"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2487575.2488212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2487575.2488212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Experience from hosting a corporate prediction market: benefits beyond the forecasts
Prediction markets are virtual stock markets used to gain insight and forecast events by leveraging the wisdom of crowds. Popularly applied in the public to cultural questions (election results, box-office returns), they have recently been applied by corporations to leverage employee knowledge and forecast answers to business questions (sales volumes, products and features, release timing). Determining whether to run a prediction market requires practical experience that is rarely described. Over the last few years, Ford Motor Company obtained practical experience by deploying one of the largest corporate prediction markets known. Business partners in the US, Europe, and South America provided questions on new vehicle features, sales volumes, take rates, pricing, and macroeconomic trends. We describe our experience, including both the strong and weak correlations found between predictions and real world results. Evaluating this methodology goes beyond prediction accuracy, however, since there are many side benefits. In addition to the predictions, we discuss the value of comments, stock price changes over time, the ability to overcome bureaucratic limits, and flexibly filling holes in corporate knowledge, enabling better decision making. We conclude with advice on running prediction markets, including writing good questions, market duration, motivating traders and protecting confidential information.