{"id":127,"date":"2018-06-14T19:04:35","date_gmt":"2018-06-14T19:04:35","guid":{"rendered":"https:\/\/pressbooks.ccconline.org\/bus3060\/chapter\/ch07-7\/"},"modified":"2026-02-15T01:30:47","modified_gmt":"2026-02-15T01:30:47","slug":"ch07-7","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ccconline.org\/bus3060\/chapter\/ch07-7\/","title":{"raw":"7.7 Prediction Markets and the Wisdom of Crowds","rendered":"7.7 Prediction Markets and the Wisdom of Crowds"},"content":{"raw":"
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Learning Objectives<\/span><\/p>\r\n\r\n<\/header>\r\n

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After studying this section you should be able to do the following:<\/p>\r\n\r\n

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  1. Understand the concept of the wisdom of crowds as it applies to social networking.<\/li>\r\n \t
  2. List the criteria necessary for a crowd to be smart.<\/li>\r\n<\/ol>\r\n<\/div>\r\n<\/div>\r\n \r\n\r\n<\/div>\r\n

    Many social software efforts leverage what has come to be known as the wisdom of crowds. In this concept, a group of individuals (the crowd often consists mostly of untrained amateurs), collectively has more insight than a single or small group of trained professionals. Made popular by author James Surowiecki (whose best-selling book was named after the phenomenon), the idea of crowd wisdom is at the heart of wikis, folksonomy tagging systems, and many other online efforts. An article in the journal Nature<\/em> positively comparing Wikipedia to Encyclopedia Britannica<\/em> lent credence to social software\u2019s use in harnessing and distilling crowd wisdom (Giles, 2005).<\/p>\r\n

    The crowd isn\u2019t always right, but in many cases where topics are complex, problems are large, and outcomes are uncertain, a large, diverse group may bring collective insight to problem solving that one smart guy or a professional committee lacks. One technique for leveraging the wisdom of crowds is a prediction market, where a diverse crowd is polled and opinions aggregated to form a forecast of an eventual outcome. The concept is not new. The stock market is arguably a prediction market, with a stock price representing collective assessment of the discounted value of a firm\u2019s future earnings. But Internet technologies are allowing companies to set up prediction markets for exploring all sorts of problems.<\/p>\r\n

    Consider Best Buy, where employees are encouraged to leverage the firm\u2019s TagTrade prediction market to make forecasts, and are offered small gifts as incentives for participation. The idea behind this incentive program is simple: the \u201cblue shirts\u201d (Best Buy employees) are closest to customers. They see traffic patterns and buying cycles, can witness customer reactions first hand, and often have a degree of field insight not available to senior managers at the company\u2019s Minneapolis headquarters. Harness this collective input and you\u2019ve got a group brain where, as wisdom of crowds proponents often put it, \u201cthe we is greater than the me.\u201d When Best Buy asked its employees to predict gift card sales, the \u201ccrowd\u2019s\u201d collective average answer was 99.5 percent accurate; experts paid to make the prediction were off by 5 percent. Another experiment predicting holiday sales was off by only 1\/10 of 1 percent. The experts? Off by 7 percent (Dvorak, 2008; Dye, 2008)!<\/p>\r\n

    In an article in the McKinsey Quarterly<\/em>, Surowiecki outlined several criteria necessary for a crowd to be \u201csmart\u201d (Dye, 2008). The crowd must<\/p>\r\n\r\n