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This chapter focuses on political prediction markets, that is markets that allow the public to speculate on election and policy outcomes. Prediction markets temper three of the largest problems of politics. First, they offer a mechanism for overcoming what has been called the “basic public action problem” of collective decision making—the Difficulty of persuading individual citizens to provide information that is useful to the whole community. Second, the markets help mix expert and dispersed o,pinion. Finally, prediction markets are able to draw together information into a single place and numerical form, economizing on the attention of citizens. This latter capacity is particularly important today, because in an age of accelerating technology, people are likely to be distracted from the business of public life by the more compelling entertainment of private life.

This chapter explores ways in which public corporations might harness the benefits of prediction markets. A major challenge addressed by corporate law scholarship is that agency costs sometimes might lead managers to make decisions that advance their own interests rather than those of the shareholders. A prediction market can be seen as a mechanism that can help counter those agency costs, and it may be more effective than some alternative mechanisms, such as shareholder voting. This chapter explores a range of potential applications of prediction markets. It argues that a more powerful Hollywood Stock Exchange might not merely predict movie success but might also be used to determine which movies would be made. The chapter explains that prediction markets might promote the flow of information, both to managers and to actual and potential shareholders, and can serve as a weak form of insider trading.

This chapter emphasizes that institutions evolve slowly and that although they may evolve too slowly, this does not justify radical change. The chapter considers two new prediction market designs: first, the market web, which could integrate and relate various prediction markets to one another; and second, self-resolving prediction markets, which forecast only their own predictions. The chapter considers John Maynard Keynes's famous comparison of markets to a beauty pageant to explain why self-resolving prediction markets could produce reasonable decisions. The chapter makes a relatively modest proposal, use of prediction markets for collaborative social science modeling; before considering the possibility of predictocracy. After cautioning that the case for self-government may argue against predictocracy, it notes that prediction markets could be used not only for democratic purposes but also for dictatorial ones. The chapter also explains how predictocracy could accommodate regional preferences and facilitate international cooperation.

This chapter suggests that prediction markets, if carefully designed, can serve as an alternative to committees. Once created, prediction markets might allow for more streamlined decision-making than committees might, avoiding the necessity of meetings and direct deliberation. At the same time, the profit incentive that prediction markets provide can help overcome some of the pathologies of group decision-making. Just as important, it is possible to design a “deliberative prediction market” that gives individual traders incentives to share information with one another. The chapter reveals that other approaches to encouraging information revelation and reflection already exist, but the decentralized nature of prediction markets might provide for cheaper and even more effective decision-making. It argues that because a prediction market can be designed to forecast an expert assessment, prediction markets can be used to make decisions on virtually any topic that committees might address.

This chapter elaborates on the argument that prediction markets can help aggregate information, noting that at times, what prediction markets aggregate might more accurately be understood as individual assessments of information. The prediction markets' aggregative powers will be greater if markets are subsidized, and this chapter explains how the market mechanisms described might be subsidized. It provides a partial description of the Policy Analysis Market, which was to have been a governmentally subsidized set of prediction markets. The collapse of that program should warn those who are optimistic about the short-term possibilities of prediction markets. The chapter also describes current regulatory obstacles and ways they might be avoided. It discusses an existing application of prediction markets, the Foresight Exchange, which avoids those obstacles by using play money and then imagines two potential government applications of prediction markets.

This chapter introduces some possible applications of prediction markets for businesses. One problem that prediction markets might help businesses overcome is “optimism bias,” a tendency that people have to overestimate their chances of success on a particular project. Businesses currently have informal ways of overcoming such bias, but prediction markets might provide a new, decentralized technique. The chapter discusses a new means of structuring and subsidizing prediction markets that will be particularly useful where only a very small number of traders might have expertise in a particular area, a context that may be common in business settings, especially if businesses offer a wide range of prediction markets. The chapter discusses the experiences of some companies with prediction markets, including the danger that they could encourage sabotage. The chapter also presents a variety of potential applications, including prediction markets to assess the success of particular products and to provide information to consumers.

This chapter suggests that prediction markets can provide objective gauges of expert consensus for the media to pass along to the public. Experience suggests that prediction markets are generally fairly accurate, and, at least, they provide incentives for those who could beat the market to push predictions in a sensible direction. The chapter describes a relatively simple structure for a prediction market that can be used to estimate the probability that a particular event will occur. After recounting the experiences of perhaps the most famous prediction market, the nonprofit Iowa Electronic Markets (IEM), the chapter elaborates a more general type of numeric prediction market that the IEM also uses. The chapter also argues that prediction markets offer considerable advantages and also present unique challenges. The chapter suggests how the media might use prediction markets to provide better information to readers and viewers.

This chapter argues that the application of prediction markets to administrative agency decision-making offers the possibility of a distinct benefit. It has been observed that prediction markets can be used to aggregate evidence and assessments and to aggregate preferences or at least to aggregate expected preferences. The chapter states that normative prediction markets are promising because they potentially can provide objective guides to policy. The chapter assesses the possibility that the demographics of market participants might have some effects on the results. It argues that prediction markets might be useful in specific contexts in which administrative agencies might otherwise be excessively politicized, such as in assessing governmental spending decisions or in which “capture” by private interests is a significant danger.

This chapter considers the possibility of market-based legislatures and attempts to develop the idea of text-authoring prediction markets that might be used to produce consensus “texts” of various types for a variety of purposes. After describing how such a market could work, the chapter discusses “chain novels”—those that actually exist and those that are mere metaphors for legal philosophers. The chapter considers whether prediction markets might facilitate improvements on Wikipedia, the decentralized encyclopedia, or help organize open-source software projects. It considers the more adventurous application of a market-based legislature, a model that also could be easily applied to tasks such as market-based drafting of administrative regulations. The chapter also considers what some of the limitations might be of a text-authoring market as a reflection of collective choice and the possibility of modifying the market to take into account intensity of preference.

Crowdsourcing is an increasingly important phenomenon that is fundamentally changing how companies create and capture value. There are still important questions with respect to how crowdsourcing works and can be applied in practice, especially in business practice. In this chapter, we focus on prediction markets as a mechanism and tool to tap into a crowd in the early stages of an innovation process. The act of opening up to external knowledge sources is also in line with the growing interest in open innovation. One example of a prediction market, a virtual stock market, is applied to open innovation through an online platform. We show that use of mechanisms of internal crowdsourcing with prediction markets can outperform use of external crowds.