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Mises and prediction markets: Can markets forecast?

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Abstract

Ludwig von Mises’s methodological position is unique because it combines apriorism with qualitative empirical approach. Nonetheless, Mises’s adherents and detractors continue to characterize his apriorism as rejecting forecasting. This paper argues that a prediction market is a traditional market for forward-looking information; it leverages subjective knowledge and aggregates information via the market process to effectively solve the Hayekian knowledge problem. We argue that Austrian economists should embrace prediction markets as a powerful method of forecasting rooted in human action.

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Notes

  1. Please, see Block (1999, 2003), Caplan (1999, 2001, 2003), and Hülsmann (1999).

  2. See, for example, Hoppe (2007, 1997), Kirzner (1997), O’Driscoll and Rizzo (1996), and Vaughn (1994). When Caplan (2001) discusses the Austrian tradition of empirical analysis, he suggests delineating the term “Misesian” among the Austrian economists. Caplan uses the term “Misesian” in reference to the strand of Austrian economics influenced by both Mises and Rothbard that rejects empirical (a posteriori) analysis of economic laws.

  3. Prediction markets are also known as “information markets” or “event futures” (see Wolfers and Zitzewitz 2004; Hanson 1991; 1990).

  4. See, for example, von Mises’ Human Action (1944[1996]) and Epistemological Problems of Economics (1978 [1933]), as well as Murray Rothbard’s Man, Economy and State ([1962]1993).

  5. See Langlois (1994), Mises (1996; 1978; 1957), Rothbard (1993), and Knight (1921).

  6. Knight (1921: 233) defined measurable uncertainty (i.e., class probability) as risk, and unmeasurable uncertainty (i.e., case probability) as uncertainty He asserted that the probability calculus was applicable to both risk (class probability) and uncertainty (case probability). In the context of case probability, Knight noted that subjective probability required two stages: “the formation of an estimate and the estimation of its value” (ibid: 227). While the first stage is a qualitative judgment, the second stage is a quantitative evaluation.

  7. In the 1970s Lucas’ critique (1976) restated a long-held position of Mises on estimation of economic constants. Neoclassical and Austrian economists agree that human action cannot have economic constants because the dimensions of human actions are unique (Barnett 2003; Mises, 1996; Friedman 1953).

  8. Ludwig von Mises (1996: 109) argued that the frequentist probability was “the only logically satisfactory one” because one could attain it through the deductive aprioristic theory. Crovelli (2010: 2) writes that “for most Austrians who have discussed Ludwig von Mises’ theory of probability (including this author [himself], to some extent) it has been common to label Ludwig von Mises as a proponent of the “frequentist” interpretation of probability advanced by his brother, Richard von Mises.”.

  9. Dice have no memory, so one can induce the odds of rolling two sixes in a row. This is not limited to cases of induction. A die can be weighted so that it comes up on six with more or less than a 1/6 probability, but coins, by the laws of physics, cannot be so weighted (Gelman and Nolan 2002). The probability of flipping heads that is ½ is apodictically certain because it is derived through the deductive aprioristic theory.

  10. We thank an anonymous referee for these comments.

  11. A similar phenomenon occurs regularly during the game show Who Wants to be a Millionaire. When a contestant is stumped by a trivia question, the contestant can choose to ask a friend (an expert) or ask the audience. The expert is correct 65 % of the time; the audience is correct 91 % of the time.

  12. This example shows that insider-trading reveals information in the most efficient way. See, for example: Block and McGee (1992) and McGee and Block (2004).

  13. Some prediction markets use virtual currency like Hollywood Stock Exchange or Foresight Exchange. Does money improve predictions? Servan-Schreiber et al. (2004) conclude that real money does not really matter in forecasting sports events. In contrast, Rosenbloom and Notz (2006) find that real-money markets are significantly more accurate for non-sports events. So far empirical evidence is mixed. It can be a result of two opposing market forces. Profit-seeking self-interest motivates real-money market predictions while minimum transaction costs and intrinsic value motivate play-money market predictions.

  14. IEM is a low-risk market because it limits traders to $500 position. Other prediction markets such as Economic Derivatives that do not limit trading positions turn over hundreds of millions of dollars.

  15. Wolfers and Zitzewitz (2004) define three main types of contracts used in prediction markets: winner-takes-all, index, and spread. Winner-takes-all contract reveals market expectations of probability that event occurs. Index and spread contracts reveal market expectations of mean and median value of outcome.

  16. Can you manipulate prediction markets? In the recent review article Wolfers and Zitzewitz (2004) conclude that none of price manipulations “had much of a discernible effect on prices, except during a short transition phase.”

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  1. Loyola University New Orleans, 6363 St. Charles Ave., Box 15, New Orleans, LA, 70118, USA

    Leonid Krasnozhon & John Levendis

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  1. Leonid Krasnozhon
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  2. John Levendis
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Correspondence to Leonid Krasnozhon.

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We would like to thank William Barnett II, Walter Block, Daniel J. D’Amico, and anonymous referee for helpful comments.

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Krasnozhon, L., Levendis, J. Mises and prediction markets: Can markets forecast?. Rev Austrian Econ 28, 41–52 (2015). https://doi.org/10.1007/s11138-013-0244-6

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