
Review of Fortune’s Formula by William Poundstone: The stranger-than-fiction tale of how to invest
What is a better investment objective? Grow as wealthy as possible as quickly as possible, or Maximize expected wealth for a given time period and level of risk The question is at the heart of a fight between computer scientists and economists chronicled beautifully in the book Fortune’s Formula by Pulitzer Prize nominee William Poundstone. [...]
What is (and what good is) a combinatorial prediction market?
What exactly is a combinatorial prediction market? 2010 Update: Several of us at Yahoo! Labs, along with academic researchers, have theorized and written about combinatorial prediction markets for several years, as you’ll see below. But now we’ve gone beyond talking about them and actually built one. So the best way to answer the question is [...]
The right way to implement a multi-outcome prediction market: Linear programming
There are many examples of multi-outcome prediction markets, for example election markets with more than two candidates, or sports championship markets with dozens of teams. What is the best way to implement a multi-outcome prediction market? The simplest way is to effectively ignore the fact that there are multiple outcomes, breaking up the market into [...]
The wisdom of the ProbabilitySports crowd
One of the purest and most fascinating examples of the “wisdom of crowds” in action comes courtesy of a unique online contest called ProbabilitySports run by mathematician Brian Galebach. In the contest, each participant states how likely she thinks it is that a team will win a particular sporting event. For example, one contestant may [...]
Evaluating probabilistic predictions
A number of naysayers [Daily Kos, The Register, The Big Picture, Reason] are discrediting prediction markets, latching onto the fact that markets like TradeSports and NewsFutures failed to call this year’s Democratic takeover of the US Senate. Their critiques reflect a clear misunderstanding of the nature of probabilistic predictions, as many others [Emile, Lance] have [...]
Implementing Hanson's Market Maker
Robin Hanson invented a wonderful market maker well suited for use in prediction market applications with a long name: the logarithmic market scoring rule market maker, which I’ll abbreviate as LMSR. (In fact, Hanson invented an entire class of market scoring rule market makers, but the logarithmic variant seems the most useful.) Hanson’s two papers [...]
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Betcha's gambit
Betcha is bold. To say the least. The founder Nick Jenkins is either crazy, brilliant, or, like many founders, both. Betcha is a platform for peer to peer betting not unlike gottabet, betfair, or intrade. Except for two (intimately related) details: (1) all debts are on the honor system, and (2) it’s based in Seattle, WA, UIGEA. Betcha makes no bones about it ( no “wink wink” here): they expect users to bet on anything and everything including sports. But because coughing up is not strictly enforced, the site evades the letter of the gambling laws. To engender trust, Betcha verifies its users’ credit cards and tracks their reputation scores, but in the end all payments are voluntary. The site earns money via listing fees.
I can’t help but admire Jenkins and Co., and I hope their gambit succeeds: my heart is with them even if my head is a step behind. (For more legal discussion see Tom Bell and The Boston Globe.)
And as much as I like the concept, I do have to ding Betcha for one of the most convoluted, head-scratching explainers I’ve heard in a long time:
Whaa? Four (weak) analogies plus a long-winded footnote? C’mon, Betcha, please KISS.