Prediction Market Enterprise 2.0?

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Was looking at the prediction market post by Andrew McAfee titled Mobs Rule, which in my opinion misses the point, he describes one of the companies he is involved in Crowdcast i started pondering about the validity of a prediction market as an enterprise 2.0 tool.
Taking on this tool has some distinct usage for businesses that is capable of generating value.
Avoiding the need to rely solely on the Project Management methodology to predict the delivery date of a product or the end of an it upgrade, adding to that the ability to predict the success of the latest campaign, is a extremely efficient tool that is able to generate significant advantages and better profit.
My personal problem with the Prediction Markets is its a self-fulfilling prophecy once the prediction is out, and it tends to direct the out come more then predict it.
The base product of Crowdcast uses an interface, from the demo, looking quite rudimentary and may be a bit too graphic, seems like it is not a poll like and uses adjustments to select a response.
Crowdcast interface
I hope they have more interfaces that enable the users to make a selection though.
Crowdcast promise to bring the market internally and make the organization as the crowd to do the selection.
This is probably good in a very limited type of organizations, one that could have the internal critics, the huge ones. if you read te book the wisdom of crowds you are probably familiar with the two basic assumptions that the crowd needs to bet on the score and needs to be heterogeneous to achieve the best predictions.
So how can we make this a part of the toolset if the enterprise is not as big as it needs and certainly not as heterogeneous in the thought process and loyalty to the company.
I love the google prediction market experiment excerpt

In the last three years, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information.
We document a number of biases in Googles markets, most notably an optimistic bias.
Newly hired employees are on the optimistic side of these markets, and optimistic biases are significantly more pronounced on days when Google stock is appreciating. We find correlated trading among employees who sit within a few feet of one another and employees with social or work relationships.
The results are interesting in light of recent research on the role of optimism in entrepreneurial firms, as well as recent work on the importance of geographic and social proximity in explaining information flows in firms and markets.

So how do we even the score with some pessimism? Look no further! We need to expose this to the external public as a quiz with prizes.
That could be a solution to the issues, but its not a silver bullet due to the type of information exposed to the decision guesser, and i am going back to the book where all the guessers were expose to the entirety of the data even if it was classified in the case of the submarine.
How can we make this a better tool to the enterprise? i say, again, make it a flexible tool that is used in several scenarios and let the organization culture set the pace.
Making a tool that is not just for the intranet or just for the internet, as Predictify is, will enable the organization to add this web 2.0 tool to the Enterprise 2.0 utility belt.

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