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Prediction Markets

OpenBI Forum

The U.S. presidential election campaign is now in the home stretch. Yet I remain confused by the welter of polls and their many interpretations. I read a quote in a recent Newseek magazine: “Another day, another 37 new statewide polls.”1 Do the polls predictably measure voting behavior? Do we really care about national polls when national elections are resolved state by state? What “adjustments” must be made to polling figures to remove their bias? Can landline telephone polling reach the critical Millennium Generation, many of whom use only cell phones? It’s a bit overwhelming for me. I think I’ll take the maverick path: buy a six-pack, watch a hockey game - and look to the Iowa Election Markets (IEM) for guidance.2

Citing the excellent book The Wisdom of Crowds, by James Surowiecki, the OpenBI Forum mentioned the IEM as an illustration of prediction markets in Part 1 of the BI Ensemble series.3 The IEM, established by the Tippie School of Business at the University of Iowa, allows paying participants to buy and sell future contracts on candidates to either predict the winners of elections or the vote share per candidate - and has proven remarkably prescient in forecasting national elections. Alas, the current IEM presidential market does not bode well for Senator McCain. As of October 22, Senator Obama leads on both the election winner and vote-share markets, and the disparity has increased post Republican convention. Indeed, the bid prices on the winner take all presidential market are $0.868 for Obama and $0.127 for McCain, which translate to confidence in Obama’s prospects in excess of 6 to 1.

The insightful article “Discovering and Managing New Product Blockbusters: The Magic and Science of Prediction Markets” from the Haas Business School at the University of California, Berkeley, sheds light on the workings of prediction markets. The authors, one a Haas professor and the other an HP analyst, address the problem of failed product launches, noting the inadequacies of traditional approaches to demand forecast, such as target customer surveys, pooled expert opinion and company meetings. As an inexpensive but reliable replacement, they propose prediction markets to harness the wisdom of crowds.4 Key to the success of such markets is a learning platform that compensates for individual prediction limitations by aggregating information from the active participation of large numbers of public, diverse and independent thinkers. The authors detail their prediction market framework, cleverly named I4C, which rewards solely information using price as an indicator that aggregates opinions of all participants. The market encourages individuals to improve their knowledge, learning from others and subsequently making the markets smarter. The markets ultimately assure success by pooling independent information sources from large crowds to promote liquidity.

The Harvard Business School article “Prediction Markets at Google” elaborates on the Haas paper by chronicling the development of prediction markets at Google. Google of course has a history of exploiting crowd wisdom with its industry-defining PageRank algorithm for search. And so with corridors of very smart employees and a “rule” that promotes 20 percent time allocation to innovation projects, it’s hardly surprising that Google has become a leader in prediction markets. With Google Prediction Markets (GPM), participants buy and sell decision securities using an allocation of Goobles, which are replenished quarterly. Individual markets that forecast product success, product launch date or competitor activities generally remain active for a single quarter only, at which time cash and other prizes are awarded. As might be expected, wily Google traders have written bots to take advantage of arbitrage opportunities in the evolving markets. While overall internal reception to prediction markets is somewhat mixed, the evaluation of market results is quite gratifying. Price appears to be an excellent proxy for the probability that a given decision will win in a market. In addition, the most expensive stock decisions overwhelmingly dominate as winners, and that domination increases as the markets progress in time to the end. 5

Prediction markets have become visible in hallowed business publications as well. The Wall Street Journal narrated the successful prediction market experience of Best Buy in a recent article. TagTrade, the Best Buy program, trades imaginary stocks based on answers to company manager questions involving forecasts and the success of ongoing or prospective initiatives. The company has focused attention on the organizational side of prediction markets, going to great lengths to assure that their “experiments” are non-threatening and encourage substantial, diverse and independent participation. Best Buy CEO, Brad Anderson, thinks narrowing the gap between management and workers has helped make the company more nimble and responsive, elevating sales and profits. Researchers from the University of Chicago have been enlisted to help evaluate their prediction market program, comparing market forecasts with official company predictions.6

Not to be outdone, the October 2008 Harvard Business Review opines on the wisdom of crowds for the advancement of philanthropy. With billions of dollars now inefficiently allocated across 1.5 million organizations, author Steven Goldberg conjectures that prediction markets could shed light on which non-profits are providing the highest social ROI, creating virtual stocks out of hypothetical questions related to organization performance, such as how many new charter schools would be built and running in a given time period.7 Ideally, the markets would create consensus judgments on the relative success of organizations competing for scarce funding dollars. One conjectured benefit of prediction markets in this space is agency obsession with evaluating and reporting performance data that can educate prospective philanthropists.

To prediction market devotees who assume markets always beat experts or statistical models comes a dampening note of caution from Cass Sunstein’s Harvard Business Review article. In what is known as the Condorcet jury theorem, several boundary conditions are mandated to assure the wisdom of crowds and prediction markets: the majority response must win and each participant must be more likely than not to be correct. If those conditions hold, a group decision has a higher probability of being right as the size of the plurality increases. On the other hand, when individuals are more likely incorrect, the probability that the majority decision is correct will tend toward zero as group size increases. Sunstein uses this theorem to explain the utter lack of market success predicting the appointment of John Roberts to the Supreme Court.8 Fortunately, this type of information deficit is more the exception for business prediction market problems than the rule.

If prevalence in reputable business writing is any indicator, prediction markets are starting to make inroads as a serious tool for business intelligence (BI). That prediction markets can forecast company demand as well or better than expert opinion, target surveys or statistical models is now generally established. That prediction markets can also be invaluable intelligence adjuncts for companies developing or testing their evolving strategies is a straightforward corollary. I would hope the dour 2004 Surowiecki observation that “the most mystifying thing about decision markets is how little interest corporate America has shown in them” is becoming a relic of the past.

References:

  1. Sharon Begley. “The Slippery Art of Polling.” Newseek, October 6, 2008.
  2. Iowa Election Markets. “Data Archive.” http://www.biz.uiowa.edu/iem/archive/, October 14, 2008.
  3. Steve Miller. “The BI Ensemble, Part 1: Weak Ties, Diversification, and the Wisdom of Crowds.” DM Review Online, April 25, 2008.
  4. Teck-Hua Ho and Kay-Yut Chen. “Discovering and Managing New Product Blockbusters: The Magic and Science of Prediction Markets.” Haas School of Business, University of California, Berkeley, April 30, 2007.
  5. Peter A. Coles, Karim R. Lakhani and Andrew P. Mcafee. “Prediction Markets at Google.” Harvard Business School, August 20, 2007.
  6. Phred Dvorak. “Best Buy Tabs ‘Prediction Market.” The Wall Street Journal, Tuesday, September 16, 2008.
  7. Steven H. Goldberg. “How Wise Crowds Can Advance Philanthropy.” Harvard Business Review, October, 2008.
  8. Cass R. Sunstein. “When Crowds Aren’t Wise.” Harvard Business Review. September 2006.

Referenced Works:

  1. James Surowiecki. The Wisdom of Crowds. Anchor Books: 2004.

Steve Miller is co-founder of a Chicago-based business intelligence (BI) services firm OpenBI, LLC, that specializes in delivering analytic solutions with both open source and commercial technologies. Miller has more than 30 years of experience in intelligence and analytics, having migrated from health care program evaluation, to database consulting with Oracle Corporation, to running a fast-growing BI services business at Braun Consulting. Advances in technology over that time have fundamentally enabled the use of quantitative methods for business differentiation. OpenBI, LLC, is all about helping customers attain that differentiation. You can reach him at steve.miller@openbi.com.

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