Yearly Archives: 2008

Robin Hanson is not convinced by the Google experiment with enterprise prediction markets -to say the least.

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Robin Hanson in a comment on Marginal Revolution:

This is important work for organizational sociology, but not for prediction markets, as this does little to help us find and field high value markets.

Finally, somebody who speaks the truth.

See also the comment of economist Michael Giberson.

Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)

ROBIN HANSON TELLS THE TRUTH ON GOOGLES ENTERPRISE PREDICTION MARKETS.

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Robin Hanson:

Yes prediction markets are cool, Google is cool, and it is cool that Google had location data to show how location influences trading. But cool need not be useful. People are not asking the hard questions here: what value exactly is Google getting out of these markets, aside from helping them look cool?

Robin Hanson is a modern-day hero. Speaks the truth. Has a clear vision. Doesn&#8217-t mind to act as a contrarian, now and then. Like Winston Churchill. Is a real leader.

Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)

Prediction Market Efficiency vs. Prediction Market Accuracy

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Panos Ipeirotis in a comment here:

[W]e should try to separate two things: Market efficiency and market accuracy. Efficiency is the rate in which the market incorporates new information and prevents any arbitrage opportunities. Accuracy is the probability in which the market predicts the correct outcome of an event. The main claim to fame for the [prediction] markets is that they self-report their accuracy, and that “the prices are probabilities”.

We can measure the effectiveness of the market by following the outline discussed above. One axis is the price of the contract at time t before the expiration of the contract and the other axis is the rate in which this event happens. (…60% of the cases the event that trades at 0.6 happens, 30% of the cases the event that trades at 0.3 happens, and so on…). A perfectly accurate market should have a straight line as an outcome when time t gets close to 0. Any deviation of the experimental results indicates an accuracy bias. There are many papers that indicate the favorite-longshot biases in the market (underprice the favorite, overprice the longshots) so there is no need to really repeat this here. An interesting thing is to see how big it can be and still have reasonable accuracy. Furthermore, if we have systematic and robust biases, then we can use a calibration function that will adjust the market prices, compensating for the biases, to reflect real-life probabilities.

Measuring efficiency is a trickier concept. The general definition of efficiency is that “the market immediately incorporates all available information”. Being able to predict price movements indicates inefficiency. Having prices for an event summing up to anything other than 1, indicates inefficiency. However, it is difficult to have a definite proof that the market is efficient. We can only say that “we were not able to spot inefficiencies”. It is very difficult to prove that “the market is efficient”.

The two metrics are, of course, highly connected close to the expiration of the contract. If the market is not efficient, then it will not be accurate, as it will not have had incorporated all the available information, if any material information becomes available just before the expiration of the contract.

Panos Ipeirotis

Better Pricing for Tournament Prediction Markets

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Last year while working out a few thoughts on arbitrage opportunities in basketball tournament prediction markets at Inkling, it occurred to me that the Inkling pricing mechanism was just a little bit off for such applications. The question is whether something better can be done. An answer comes from the folks at Yahoo Research: yes.

Inkling’s markets come in a couple of flavors, so far as I know all using an automated market maker based on a logarithmic market scoring rule (LMSR). In the multi-outcome case – for example, a market to pick the winner of a 65-team single elimination tournament – the market ensures that all prices sum to exactly 100. If a purchase of team A shares causes its share price to increase by 5, then the prices of all 64 other team shares will decrease by a total of 5.

The logic of the LMSR doesn’t tell you exactly how to redistribute the counter-balancing price decreases. In Inkling’s case they appear to redistribute the counter-balancing price movements in proportion to each team’s previous share price (so, for example, a team with an initial price of 10 would decrease twice as much as a team with a previous price of 5). While for generic multi-outcome prediction markets this approach seems reasonable, it doesn’t seem right for a tournament structure. (I raised this point in a comment posted here at Midas Oracle last September, and responses in that comment thread by David Pennock and Chris Hibbert were helpful.)

The problem arises for pricing tournament markets because the tournament structure imposes certain relationships between teams that the generic pricing rule ignores. Incorporating the structure into the price rule in principle seems like the way to go. Robin Hanson, in his original articles on the LMSR, suggests a Bayes net could be used in such cases. Now three scientists at Yahoo Research have shown this approach works.

In “Pricing Combinatorial Markets For Tournaments,” Yiling Chen, Sharad Goel and David Pennock demonstrate that the pricing problem involved in running a LMSR-based combinatorial market for tournaments is computationally tractable so long as the shares are defined in a particular manner. In the abstract the authors report, “This is the first example of a tractable market-maker driven combinatorial market.”

An introduction to the broader research effort at Yahoo describes the “Bracketology” project in a less technical manner:

Fantasy stock market games are all the rage with Internet users…. Though many types of exchanges abound, they all operate in a similar fashion.

For the most part, each bet is managed independently, even when the bets are logically related. For example, picking Duke to win the final game of the NCAA college basketball tournament in your online office pool will not change the odds of Duke winning any of its earlier round games, even though that pick implies that Duke will have had to win all of those games to get to the finals.

This approach struck the Yahoo! Research team of Yiling Chen, Sharad Goel, George Levchenko, David Pennock and Daniel Reeves as fundamentally flawed. In a research project called “Bracketology,” they set about to create a “combinatorial market” that spreads information appropriately across logically related bets.…

In a standard market design, there are only about 400 possible betting options for the 63-game [sic] NCAA basketball tournament. But in a combinatorial market, where many more combinations are possible, the number of potential combinations is billions of billions. “That’s why you’ll never see anyone get every game right,” says Goel.…

At its core, the Bracketology project is about using a combinatorial approach to aggregate opinions in a more efficient manner. “I view it as collaborative problem solving,” Goel explains. “This kind of market collects lots of opinions from lots of people who have lots of information sources, in order to accurately determine the perceived likelihood of an event.”

Now that they know they can manage a 65-team single elimination tournament, I wonder about more complicated tournament structures. For example, how about a prediction market asking which Major League Baseball teams will reach the playoffs? Eight teams total advance, three division leaders and a wild-card team from the National League and the same from the American League. The wild-card team is the team with the best overall record in the league excepting the three division winners.

In principle the MLB case seems doable, though it would be a lot more complicated that a mere 65-team tournament that has only billions of billions of possible outcomes.

[NOTE: A longer version of this post appeared at Knowledge Problem as “At the intersection of prediction markets and basketball tournaments.”]

Robin Hansons concept of… Info Value

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Robin Hanson:

Info Value = the added accuracy the markets provide relative to other mechanisms, times the value that accuracy can give in improved decisions, minus the cost of maintaining the markets, relative to the cost of other mechanisms.

A highly accurate market has little value if other mechanisms can provide similar accuracy at a lower cost, or if few substantial decisions are influenced by accurate forecasts on its topic.

Wow, great formula. [BTW, I have slightly edited RH’s first sentence.]

I&#8217-m sure Mike Giberson will write another blog post for Midas Oracle about that formula &#8212-all that for free. Crowd-sourcing works for me. :-D

Rushkoff on Crowd Sourcing

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Douglas Rushkoff answering this year&#8217-s Edge question:

The Internet. I thought that it would change people. I thought it would allow us to build a new world through which we could model new behaviors, values, and relationships. &#8230- For now, at least, it&#8217-s turned out to be different. &#8230- The open source ethos has been reinterpreted through the lens of corporatism as &#8220-crowd sourcing&#8221- – meaning just another way to get people to do work for no compensation.

Unfortunately, that&#8217-s close to the truth for most play-money prediction market business plans.

Producing a CNBC show = just like making sausages

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Robert Scobble:

Last week I was on CNBC twice. Once on “Fast Money” and once on Donny Deutsch’s “The Big Idea.”

The Fast Money segment has been torn apart on the Internet but Roger Ehrenberg of the Information Arbitrage blog had the most intelligent analysis of it.

Here’s the key piece of the Donny Deutsch show, where we had a bunch of bloggers on the Microsoft Blogger Bus asking questions of Bug Labs’ CEO Peter Semmelhack. Bug Labs went onto win CNET’s “Best of CES” award for the emerging tech category.

Some things that are worth underlying about the difference between my video show and CNBC.

1. Expense. CNBC had dozens of people involved in the show, a huge booth, really expensive cameras, satellite time, etc. My show? Get a Nokia phone and go for it.
2. Makeup. Yeah, I wore it. There’s a video out there on the Internet somewhere. I’m not sure why Valleywag hasn’t found it yet.
3. You don’t get to say whatever you want. Donny’s show was tape delayed. If you try doing something wacky, they’ll just cut you out. But even if they keep you on, they have a director who is telling you what they want. She preps you for each segment, giving you “talking points.” If you don’t agree with those talking points you have to negotiate to have them changed. But if they don’t like your talking points they just won’t go with you. Second, she has a big sign and if she thinks you should make a point she makes it clear.
4. These shows are NOT about getting deep, or really getting a good understanding of CES. They were ALL about being entertaining! Hey, who knew? (I tried to pull a bunch of gadgets out and they said “we don’t care about the gadgets.”)
5. They filmed Bug Labs’ CEO for 10 hours for a two-minute segment. Now do you understand why so many CEOs let me come over and film them? I never take more than an hour with an executive, so it’s always easy to get onto someone’s schedule.
6. My show has very little editing, so it’s pretty rare that the context gets lost on an answer. On TV, though, things get cut up, sliced and diced, all for entertainment effect, not necessarily to tell the best story.

Watching CNBC = a waste of time.

Presenting on CNBC = a waste of time.

Viva office-made videos and YouTube.

Viva video blogging.

Read the previous blog posts by Chris. F. Masse:

  • We regret to inform you of the passing of BettingMarket.com.
  • Niall O’Connor, the one-data-point analyst
  • The best headline of the day –post Michigan
  • Enterprise prediction markets will be the next big thing when… hierarchies are flat.
  • Prediction Markets vs. Bookmakers — The Ultimate Argument
  • The Michigan primary as seen thru the prism of the InTrade prediction markets
  • BitGravity = video distribution network

Can the prediction markets survive without the over-selling from John Delaney and his little fanboys?

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Emile Servan-Schreiber:

[…] The classic first line of defense in these cases is to remind people that market “predictions” are really just probabilities, so any one outcome cannot invalidate the approach. The argument is sound and backed up by loads of data. But it would of course be much more convincing if we, as an industry, would remember to show at least as much humility when our market “predictions” appear correct instead. If you’re going to spread the idea that your market called all 50 states in the last U.S. presidential election because each correct outcome was predicted with over 50% chance, then you can’t hide behind probabilities when an 80% prediction comes to naught, as in Obama’s NH collapse. […]

Emile Servan-Schreiber makes a good point &#8212-see also Panos Ipeirotis, in the same vein.

But the over-selling is the reason [*] why InTrade (and not NewsFutures) has managed to infiltrate so many US media. If you suppress the magical touch, then InTrade is just a forecasting tool of convenience &#8212-for those too busy to look at the polls.

Give me one reason why the political analysts should follow InTrade instead of the polls, then?

What is the true nature of the prediction markets? How to use the prediction markets? Who should use the prediction markets? For what benefits? Once you have the answer to these 4 questions, you can tackle the next two problematics: How to market the prediction markets without over-selling them. How to report news thru the prism of the prediction markets while respecting their true probabilistic nature.

Welcome to the version #2 of the prediction market industry. Quite a horse of another color, now.

&#8212-

[*] UPDATE: The over-selling aspect is the topping over the real-money and the liquidity dimensions. The over-selling aspect wraps all that.

Ron Paul says Rosa Park and Martin Luther King are his heroes.

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No GravatarRon Paul on CNN – YouTube video

Our good friend Mike Linksvayer is so wrong, politically, once again.

&#8212-

Price for 2008 Republican Presidential Nominee at intrade.com

Price for 2008 Presidential Election Winner (Individual) at intrade.com

Read the previous blog posts by Chris F. Masse:

  • Bzzzzzzzzz…
  • Bzzzzzzzzz…
  • “No offense, but I think Radley Balko is the most valuable blogger in America right now.”
  • Are you a better predictor than John McCain?
  • What does climate scientist James Annan think of InTrade’s global warming prediction markets?
  • Inkling Markets, one year later
  • One trader’s view on BetFair’s new bet-matching logic