Prediction markets

One problem with democracy is that many people lack access to independent information and knowledge about the consequences of different choices. If decisions are made on the basis of false premises, the consequences can be disastrous. So what can be done to make better, more informed decisions in an organisation? One solution we have implemented in our digital democracy tool is the possibility of using a prediction market.


In a prediction market, all participants get to bet on what will happen in the future. They can bet with points, money, or other assets. Those who make correct predictions are rewarded and those who fail lose their investment. One of the first modern electronic prediction markets was organised by the University of Iowa in 1988 during the presidential election [1].


On our democracy forum Flowback it is possible to post important decisions for your organisation for analysis on a prediction market. This means that you enlist the help of competent people who like to compete in predictions to get an independent assessment of the consequences of the options you are about to vote on. It has several advantages:

  1. Prediction markets with many participants have been shown to often make much better predictions than experts and averages over time, often getting the result right over 70% of the time[2] [3] [4] [5] [6]. Prediction markets have been shown to work so well that they are even used by some companies for their environmental analyses [7].
  2. Having a prediction market together with delegation leads to the ability to evaluate expertise in real time and use information about how good predictions someone makes in a particular subject area as a basis for whether to delegate your vote to them.
  3. A final advantage of prediction market analysis is that it is not "controlled from above" with the risk of biased information, but "generated from below". This means that the analyses have the potential to gain high legitimacy, even among irrational conspiracy theorists and others who do not trust the mainstream media.


One problem with prediction markets can be that experts' predictions are drowned out by those who don't have the skills. But that problem has two solutions. First, in the long run, those who are bad at predicting tend to get bored as they lose all the time and have fewer resources to invest, and some might learn over time. Only committed and dedicated experts tend to stay. Second, the weight of the prediction votes can be increased for those who tend to vote right, and vice versa for those who vote wrong, per area. This leads to the convergence of the mean vote towards better predictions over time.

Learn more about prediction markets

Prediction markets are a form of crowdsourcing where participants compete to make good predictions. A prediction market aggregates the knowledge of participants, thus harnessing the decentralised wisdom of the crowd to make better predictions. Alternative similar versions such as prediction polls exist, which according to some researchers work even better [8].

Theoretical foundations of the idea of prediction markets can be found, for example, in the 1945 book "The Use of Knowledge in Society" by Nobel laureate Friedrich Hayek. He argues against central planning because information is scattered among many actors in society, and argues that decentralised thinking leads to better outcomes [9], which Ludwig von Mises also believed and wrote about as early as 1920 [10]. One of Wikipedia's founders has also cited Hayek's work as central to the development of Wikipedia.

Another central book is "The Wisdom of Crowds" 2014 by James Surowiecki which argues that thinking, coordination, and collaboration are something that happens better in decentralized form. The author argues that centralization, homogeneity, closed-mindedness, repetition of what others think and do, and collective emotional responses lead to irrational group decisions where experts can be overruled by participants who do not have expertise in certain areas [11].

Theoretically related ideas can also be found in Nobel Prize winner Joseph Stiglitz who has written about the importance of collecting distributed information in a modern information economy through some sort of mechanism [12]. Marxist economists such as Paul Cockshott have also argued for the importance of collecting decentralized information in order to plan effectively [13]. 


[1] Stanley W. Angrist (28 August 1995). "Iowa Market Takes Stock of Presidential Candidates (Reprinted with Permission of THE WALL STREET JOURNAL)". The University of Iowa, Henry B. Tippie College of Business. Archived from the original on 30 November 2012. Retrieved 7 November 2012.

[2] Steven Gjerstad. ""Risk Aversion, Beliefs, and Prediction Market Equilibrium""(PDF). Archived from the original (PDF) on 12 April 2014. Retrieved 20 August 2016.

[3] Justin Wolfers; Eric Zitzewitz. ""Interpreting Prediction Market Prices as Probabilities"" (PDF). Archived from the original (PDF)on 12 November 2012. Retrieved 20 August 2016.

[4] Pennock, David M.; Lawrence, Steve; Giles, C. Lee; Årup Nielsen, Finn (2001). "The real power of artificial markets". Science. 291 (5506): 987–988. CiteSeerX doi:10.1126/science.291.5506.987. PMID 11232583. S2CID 35108036.

[5] Page, Lionel; Clemen, Robert T. (2013). "Do Prediction Markets Produce Well‐Calibrated Probability Forecasts?" (PDF). The Economic Journal. 123 (568): 491–513. doi:10.1111/j.1468-0297.2012.02561.x. S2CID 152567648.

[6] "How Accurate are Prediction Markets? : Networks Course blog for INFO 2040/CS 2850/Econ 2040/SOC 2090". Retrieved 12 February 2019.

[7] Lohr, Steve (9 April 2008). "Betting to Improve the Odds". The New York Times. ISSN 0362-4331. Retrieved 3 February 2017.

[8] Atanasov, Pavel; Rescober, Phillip; Stone, Eric; Swift, Samuel A.; Servan-Schreiber, Emile; Tetlock, Philip; Ungar, Lyle; Mellers, Barbara (22 April 2016). "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls". Management Science. 63 (3): 691–706. doi:10.1287/mnsc.2015.2374. ISSN 0025-1909.

[9] Friedrich Hayek (September 1945). "The Use of Knowledge in Society" (PDF). The American Economic Review. 35 (4): 519–530. JSTOR 1809376.

[10] "Economic Calculation in the Socialist Commonwealth". Mises Institute. Retrieved 27 April 2010.

[11] Schiff, Stacy (July 31, 2006). "Know It All". The New Yorker. Retrieved October 31, 2008.

[12] Stiglitz, Joseph (1996). Whither Socialism?. The MIT Press. ISBN 978-0262691826.

[13] Cockshott, Paul; Cottrell, Allin (1993). Towards A New Socialism (PDF). Russell Press. p. 86. ISBN 0851245455.

For further information, see the journal of prediction markets.

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The Digital Democracy Association is a non-profit association that is religiously and politically independent. It aims to support and promote effective democratic organisation of people at all levels: from small networks and organisations at grassroots level to large companies, political parties and organisations at national and international level.

Its ultimate goal is a world in which everyone's influence and participation is maximised in a way that is compatible with high flexibility, efficiency and power to act in human co-operation and organisations. The Association's sub-goals are to (i) develop innovative open-source tools for digital democratic organisation (ii) disseminate knowledge and stimulate interest in effective democratic decision-making and organisational practices (iii) support organisations and companies to improve their internal democracy.