Great in Theory
The basic principle behind "
wisdom of crowds" is simple: knowledge of many is better than knowledge of one (the ol' "two heads are better than one"). Since
The Wisdom of Crowds book and the more recent
Yahoo! confab conference,
prediction markets are quickly becoming the latest "hot" topic for
many bloggers. The essential idea is that aggregated opinions of the crowd are more accurate in making future forecasts.
The concept is not new though and has been applied to numerous fields -- sports, financial markets, politics, pop culture, and even weather. For instance, consider betting exchanges like
Tradesports and
Betfair. Tradesports, for example, lets people place bets on, say, the outcome of the next presidential election.
More recently, at the Yahoo! conference, prediction markets are now being tested in organizations, collecting the opinions of employees in order to facilitiate better decision-making by senior level management. Look at
NewsFutures. In fact, places like
Inkling Markets has opened up the floor so any type user (enterprise, small business, personal, academic) can create and manage their own prediction market.
Great in Practice?
Read/Write.Web cites:
THE ISSUES: Reasons Why Prediction Markets Have Failed (by Adam Siegel of
Inkling)
»
Lack of understanding: People don't know what prediction markets are.
»
Incorrect market structure: People may not receive their reward.
»
Market rules are poorly described
»
Timeframe too long in question: e.g. How much will college graduates make in 2025?
»
Biases in questions posed
For the most part, I believe these mentioned issues can be mitigated over time as more people become familiar with prediction markets. Tradesports, for example, is not centered around questions (more topics), which helps eliminate biases.
THE REAL ISSUES
»
Incentive structure (gaming prevention): A proven, tested incentive structure (whether based on monetary or social rewards) must be in place to prevent users from "gaming" the system. It could be like online poker, and
we all know what happened to that.
»
Large population of users: Like with real-world markets, there needs to be enough "bets" made (liquidity) in order to generate an accurate prediction.
»
Differences across applications / fields: In the financial services sector, for instance,
domain expertise may be more valued than actual technology, especially since
serious investing in the stock market is no game. After all, if I had more knowledge than the market, would I share it with you? Too bad they don't have a class on stock-picking (what everyone actually wants to learn) at
Wharton. Instead, we have theoretical Investment Management classes that teach you about "diversification" and "asset allocation" conveniently without a real prescriptive lesson. All to be expected of course. There's no start-up out there posting "here's the secret sauce to my product," or no venture capitalist telling you "here's a step-by-step guide on how I choose my companies."
FINAL THOUGHTS
» When I took
Jeremy Siegel's macroeconomics class sophomore year, he told us that
Tradesports was very accurate in most of its predictions.
» Prediction markets are very powerful and great concept. I am very interested in seeing how these issues will be resolved.
technorati tags: wisdom of crowds, prediction markets, yahoo confab, tradesports, inkling markets, trading, financial services, wharton