Next Practices / Technologies Tools and Methodologies: Prediction markets


 The value of analytics can be realized by converting corporate data and information from hindsight to insights and then, ideally generate, valuable and actionable foresights. There are two most common approaches to generating foresights. They are, the use of statistics and mathematical models, and the use of machine learning tools such as neural nets and genetic algorithms software to mine existing data and generate predictions. The following paragraph depicts the features of an emerging new approach in generating predictions. These predictions or actionable foresights implement social analytics through prediction markets. Over time, social analytics or social intelligence will become an integral—and eventually indistinguishable—element of the enterprise’s ability to sense, interpret, and recommend actions based on signals from the market. Organizations can use prediction markets as part of their portfolio of social analytics tools.

Application Areas

  •  Prediction markets reduce the level of uncertainty surrounding predictions of future consumer behavior and provide quick, cost-effective results. Therefore, the use of prediction markets will serve a niche but critical role during the innovation process.
  •  Deployed as part of a culture change program by the Human Resources group
  •  Hewlett-Packard pioneered applications in sales forecasting and uses prediction markets in several business units. It is working towards a commercial launch of the implementation as a product, BRAIN (Behaviorally Robust Aggregation of Information Networks).
  •  Assess new ideas to improve processes and products.
  •  More recently, it has executed markets based on the 2003 California gubernatorial election, the 2004 presidential election, the 2004 democratic presidential nomination and how the Federal Reserve will alter the federal funds interest rate. Universities in other countries have also started event markets about their own elections, like the Austrian electronic market run by the Vienna University of Technology or the university of British Columbia election stock market that focuses on Canadian elections.
  •  Some prominent examples include Tradesports and Betfair and pseudo markets (in which participants trade virtual currency) such lumenogic and Ideosphere.


 Rank-ordering uncertainties that need to be resolved, based on the value of resolutions, identifies a priority list of potential prediction market applications. Online prediction markets are built on the principle that markets serve to aggregate the beliefs of multiple traders to generate a forecast. Similar to the stock market, which serves to assign a price to the future estimated earnings of a stock, prediction markets assign a value to a belief about the future (a prediction).

Prediction markets are the aggregation mechanism for collective intelligence. Prediction markets, like commodity markets, channel inputs from all traders into a single dynamic stock price. Instead of determining the value of a particular good, a prediction market is used to determine the probability of a particular event occurring. The following paragraphs present and discuss features of prediction markets that urge a collective toward optimal solutions. Through the combination of these features, prediction markets lend themselves to the systematic study of the promising phenomenon of collective intelligence.

From the success of prediction markets, Hanson (1999) was one of the first to propose the use of such markets for making decisions. He proposed decision markets created especially for evaluating various policy alternatives.


CFOs can use prediction markets to reduce uncertainty. A prediction market is a sophisticated aggregation tool. A market is an ideal aggregation mechanism for the generation of collective intelligence because it is decentralized to handle complex problems. Prediction markets combine two prime examples of decentralization — free markets and social dynamics — into a system that is ripe for the generation of collective intelligence. Organizations may use these prediction markets to generate forecasts of variables that can potentially be utilized in other forecasting models

Prediction markets derive their predictive powers from three sources:

The first source is cognitive diversity. Concisely, when dealing with complex issues involving many variables or moving parts, no one can claim to have a complete model or theory from which to make fail-safe predictions. More likely, everyone has a partial understanding of the situation, further clouded by his own biases. However, when all these partial, biased models are put together, a wonderful thing happens, knowledge accumulates, gaps filled, while the various biases cancel each other. The group’s collective model is better and more complete than any individual model. Prediction markets attract diversity like powerful magnets, because anyone with a model that disagrees with the current consensus has a profit motive to participate in the market.

The second source of market accuracy is that the process encourages the voicing of informed contrarian opinions rather than calculated conformity or respectful consensus. Indeed, the only possibility for profit lies in disagreeing with the consensus, publicly. This makes sure that all informed points of views are included and aggregated. Importantly, the possibility of financial loss also discourages and penalizes the voicing of non-informed opinions.

The third source of market power is that framing predictions as wagers makes people think differently. Brain imaging studies show that when it contemplates a gamble, the brain becomes more risk averse and tunes out the emotional signals that might interfere with cognitive performance. In short, the thinking becomes more objective and  judgments less clouded by  passions and preferences.

Prediction Markets

Prediction Markets


  1.    i.     Independent decisions

An essential component to this maximization is that participants maintain their individuality by making independent decisions. Participants must be free to express their beliefs without feeling influence from others. Prediction markets accomplish this by encouraging competition between participants, not consensus. Because of competition, participants are unlikely to share their privately held information and thus influence others or feel social pressure to alter their decisions.

ii.      Reasonably intelligent crowd

Collective intelligence in prediction markets is founded on the belief that people are not flawless decision makers. Collection of people will have more knowledge than any single person even the most expert.

iii.    Diversity

Diversity is the fundamental mechanism behind the emergence of collective intelligence. Diversity provides the basis for an explanation of why collective effort by a group can often outperform an individual, by virtue of being different; individuals can improve upon each other’s solutions to a problem.

iv.    Complexity

Leaders are particularly interested in prediction questions, as they are, by their very nature, complex problems because they depend on a constellation of factors. Prediction markets will contribute solutions to those inscrutable problems that will not yield to the diligent efforts of brilliant problem-solver.

v.     Decentralization

Individuals, teams, and organizations are not the only way to solve problems. Distributed intelligence in a decentralized system is an important way to solve problems and increase our knowledge because it produces answers to questions that are too complex for an individual or groups to grasp. Prediction markets make decentralization feasible, profitable, and competitive. Decentralization refers to a property of a system where decisions are made by individuals based on their own local and specific knowledge rather than by an omniscient or farseeing planner.

Types of Prediction

Prediction markets are simply markets in which payoffs are tied to unknown future events. Naturally there exist many ways to tie future events to financial payoffs, and careful design can be used to elicit the market’s expectations of a range of different parameters.

Barriers and drivers for adoption

Prediction markets are an effective way to eliminate a bias in information through tapping diverse minds. The trouble with normal human beings is that even when they are smart, they have access to imperfect information and follow the groupthink of peers. Because they often disagree with other groups, they band together and end up agreeing too much with own teams. No single leader can overcome such biases and data gaps to predict with certainty whether an action will succeed or fail. However, prediction markets can do just that.

Arguably, the most important issue with these markets is their performance as predictive tools. In the political domain, Berg, Forsythe, Nelson and Reitz (2001) summarized the evidence from the Iowa Electronic Markets, documenting that the market has both yielded very accurate predictions and outperformed large-scale polling organizations.

Prediction markets are emerging as a valuable forecasting tool in diverse application areas from sales forecasts to project success. This social analytics strategy could potentially help resolve a number of business uncertainties, especially where prior data may be sparse or the situation is so unique that other forecasting tools are less useful.

The success of prediction markets, like any market, can depend on their design and implementation. Some of the key design issues include how buyers are matched to sellers, the specification of the contract whether real money is used, and whether a diversity of information exists in a way that provides a basis for trading.

The value of this prediction markets trading is that it creates valuable signals for management that include the probability of different outcomes. It can alert management to take action to remedy potential problems such as a project potentially going off track from a timing and budget perspective.

 Prediction markets now have a record of accomplishment in several fields, which has helped convince people that they can be useful in real-world situations. Technology has made a big difference, since corporate can now use intranets to cheaply and efficiently aggregate information across a big organization. In addition, although this is rarely mentioned, the prediction markets trend is really part of a broader Web 2.0 bottom-up movement. Large groups of people can solve problems together and come up with interesting answers and that do not necessarily need formal hierarchies to accomplish this.

Business Value

Prediction markets offer a unique ability to incorporate information-aggregation and the predictive power of markets within traditional corporate structures. A prediction market is established within the company to generate predictions on issues of interest in a manner that directly addresses the foundational communication constraints. Incorporating this type of approach in a geographically distributed and virtually managed organization can provide significant benefits by promoting a smart forum with a goal to drive final analysis of influence and intelligence in a particular area. Additionally, a collaborative approach can increase employee engagement, raise energy levels, and offer a unified platform for employees to be heard, while leveraging the ability for leaders to receive input from all minds in the company.

Prediction markets provide the most business value when executives consistently take action on the market results. Executives note the importance of developing a comparison between current baselines and market outcomes, to gain confidence in market accuracy. Executives find the market to be on average as accurate as or more accurate than traditional methods. In addition, prediction markets consistently provide valuable insights on how to structure or adjust forecasting processes, the most appropriate new product and service channels, and real-time status of critical initiatives.

A set of properly designed markets can be a good information aggregation mechanism. The deployment of such an information aggregation mechanism inside Hewlett-Packard Corporation for making sales forecasts. Results show that information aggregation mechanism performed better than traditional methods employed inside Hewlett-Packard.

Future evolution

Although predictive markets have spread beyond early-adopting companies in the technology industry, they have still not become mainstream management tools. A couple of challenges are raised. Google has created the largest corporate prediction market in the world.

Google also uses prediction markets to forecast product launch dates. In the past months, Google has run more than 130 markets on events such as launch dates and new office openings. The company even executes markets on how often particular products are likely to be used.

  To uncover the number of 7-day active Gmail users. In all, more than 1,000 employees participate in Google’s prediction markets, competing for T-shirts, gift certificates, and cash prizes.

Smartly applied, prediction markets can help management listen to voices, throughout the company, that otherwise go unheard.


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