CN collaborator (on Zocalo) and inventor of Prediction Markets and the term “idea futures,” Robin Hanson has been through quite a journey to getting tenure in the world of economics. He started out in artificial intelligence and… well, Jeremy Kahn’s profile from two years ago does a better job than I could of laying out the story. Read on for the full version of this blogpost and his article, “The Man Who Would Have Us Bet on Terrorism.”
Fortune magazine has done an impressive job of tracking the developments in prediction markets (with props to BW and Economist, too, of course), but it will be a long while before we get past the emotional and overhyped reactions stirred up by Poindexter’s tenure at DARPA. In the full version of this blogpost, read on to see the latest article on Intrade, by Andy Serwer.
…Notice that we used the word “bet” above. Because it involves putting money on everyday events (like sports), trading contracts on Intrade may seem a little like gambling. But Intrade executives say it’s not: For one thing, Intrade takes only a commission and does not act as “the house” like a Las Vegas casino. Also, investors on Intrade can, at least in theory, use skill to hedge their risk, as they do in the equity market.
There have been quite a few articles about the betting markets on the
next supreme court nomination.
The Washington Post noticed that there’s a difference between odds posted by bookies and the results of
markets. (The bookies were handicapped by having to make their
projects before the news was out about Sandra Day O’Connor retiring
first.)
But these markets suffer from the same problem as the market in which
Justice would retire first, and which Cardinal would be elevated to
Pope: a very small group has inside information, and everyone else is
just speculating. For the Supreme Court, it would be much more
relevant to have conditional markets in who, if nominated, would be
most likely to be confirmed, or to rule in certain directions. These
kinds of answers are more useful, both to the ultimate decision maker
(the President) and the various lobbyists, and also are the kinds of
questions that a much larger group of informed people could usefully
weigh in on.
Zocalo is now open source. I have placed source and binary tar files at sourceforge. The project name is zocalo. Here is the direct link to the file download page.
Of course, there are many caveats. The current version is intended to support a particular experiment that will be run at George Mason University. The underlying code has more generality than is needed to support the market that the GMU economists are studying. You can probably learn a lot about our approach by reading the code, but you will also notice that I used a methodology based on Extreme Programming–I didn’t go back and clean up code as often as I would have earlier in my career. If you find things that could be improved in the code, by all means, send my your fixes.
Here are the release notes I uploaded with the tar files:
This is the first public release of the Zocalo Prediction Market code. There is both a source and a binary release here. The INSTALL file gives instructions for installing and running a simple experiment. RELEASE-TODO lists a few of the many tasks left to be done. This is very early in the life of Zocalo; there is much work left to do. The source release includes javadoc as well as the source code, and an ant file (build.xml) that will allow you to build source and binary releases, run junit tests, or regenerate the javadoc. The binary release includes just what you need to run a simple experiment, including the installation instructions.
LICENSE gives the CommerceNet license for the base Zocalo code. Third party code that we have included is described in THIRD_PARTY_SOFTWARE. The only third party code that we include source for is mod_pubsub.
I was the judge for the
WTOGam claim on the Foresight Exchange, which concerned the suit by Antigua before the WTO on whether the gambling laws in the US discriminated against foreign providers of gambling services in a way that wasn’t allowed under the Trade Agreements the US had signed. The decision was final in April, and someone called my attention to it about a week ago.
The WTO’s decision is 143 pages long, and the press reports at the time had widely varying views of the results, so it took a bunch of
analysis (and consulting with at least one expert) to come up with a ruling.
The WTO appeals body’s decision was complex, (here’s their summary) overruling some parts of the original ruling, sustaining others, and declining to state an opinion on yet others, so deciding how WTOGam should come out wasn’t simple.
My ruling (lots of details there) hinged on the details of what the FX claim was supposed to be about (i.e. did theWTO appeals body overrule the decision of the original panel?) It
could have gone either way, but I ruled that the appeals body had upheld the original decision even though they significantly narrowed the grounds for the ruling.
A more important question, now that I’ve looked at all the details, is what did the WTO decide and what does it mean for gambling laws in the US? And will it have any effect on prediction markets?
Back on March 10th, Bernardo Huberman’s group’s work at HP showed up in the Economist’s Technology Quarterly as an example of internal markets at work. But what commodity should one make a market in?
As the article excerpt points out, electricity is sold by the kilowatt-hour, but computing consists of (at least) three distinct qualities: storage, bandwidth, and processing. Further complicating matters is that privacy and security constraints affect how we allocate each type of computing resource differently. So I don’t know how successful “computons” might be, but we need some handle on that concept to successfully decentralized ‘grid’ computing…
Economist.com: Who wants to buy a computon?
Building just such a market mechanism is the nut that Bernardo Huberman, a researcher at Hewlett-Packard (HP), and his team have been trying to crack. The key, Dr Huberman realised, was to have a system that can allow users to assign different priorities to tasks, to reflect their importance. This rules out any system that would simply give each user a priority without differentiating that user’s many tasks. It also rules out a reservation-style system of the sort that airlines use, since a lot of processor cycles (like aeroplane seats) would end up unused, and the system would not be able to accommodate new tasks as they arose, even if they were extremely urgent. In a grid, it must be assumed, demand is changing constantly and unpredictably, and so is supply (since individual host computers on the grid come and go).
Mr Huberman’s answer is Tycoon, a piece of software for computing grids that turns them into a sort of %u201Cstockmarket or clearing house%u201D, he says. Users start by opening a bank account and getting credits. They then open a screen that shows all the available processors, their current workloads, and a price list. Users place bids for various processors, using a sliding price dial that looks like a volume control. Allocation is proportional, so that if one user bids $2 and the other $1, the first gets two-thirds of the resource and the second one-third. If the deadline of one task suddenly moves forward, the user can up his bid and immediately get more processor cycles for that task. As users consume cycles, the software deducts credits from their account.
The HP team has so far tried out Tycoon on a cluster of 22 Linux servers distributed between HP’s headquarters in Palo Alto, California, and its offices in Bristol, England. Tycoon did well in these tests, and several amusing animated films were rendered using its system. HP has now given Tycoon to CERN, the world’s largest particle physics laboratory and a hotbed of grid-computing research, for more testing.
This is only the beginning, of course. Mr Huberman reckons that Tycoon, in its current form, could run clusters of 500 host computers with perhaps 24 simultaneous users. But the ultimate vision of grid computing is for one gigantic network spanning the globe and accommodating unlimited numbers of users. So a lot still needs to happen.
For a start, the metaphor for computing grids as %u201Cutilities%u201D, similar to water or electricity supplies, is misleading, since there is no equivalent of litres or kilowatt-hours. Processor cycles are just one component of computing resources, alongside memory, disk storage and bandwidth. Mr Huberman would like to combine all of these factors into one handy unit, which he wants to call a %u201Ccomputon%u201D (a cross between %u201Ccomputation%u201D and %u201Cphoton%u201D, the name for a packet of electromagnetic energy). Tycoon’s descendants would then help to allocate computons across the grid’s global market. Of course, Mr Huberman adds, that will happen in a different decade.
Tycoon allocates computer resources in distributed
clusters like PlanetLab, the Grid, or a Utility Data Center
(UDC) using a market-based mechanism where user pay for their
usage using a currency. Tycoon allocates the cluster more efficiently
than time-sharing schemes, and allows users to change their
allocation in seconds.
James Annan, the
climate scientist I blogged about earlier, is at it again. He is in the mainstream on the global warming question. AFAICT, that’s pretty unusual for someone who is
loudly
challenging others to stake their reputations on a specific bet.
Usually, people out of the mainstream use this tactic in order to
get some visibility for their unusual position.
The debate seems to have three factions: Skeptics, Alarmists, and the
mainstream. The mainstream is represented by the
IPCC report
(which Annan agrees with). The Skeptics believe that the IPCC report
is an extremist statement because it presents the alarmist view and
the mainstream together without preferring the less extreme scenarios.
There’s some disagreement about the roles, though. Annan thinks that
the two interesting positions are those who agree with the IPCC, and
those who think it is overly alarmist, while
Chip
Knappenberger (characterized by Annan as a Skeptic) thinks that
he and Annan are in agreement as to the likelihood of the rate of
(near) future temperature change, and proposes that they should work
together to bet against “the alarmists – those folks who entertain the
idea that the IPCC extreme temperature change scenarios are the most
probable.”
Annan started this particular brouhaha when he noticed that Richard
Lindzen forecast that over the next 20 years, the climate is as
likely to cool as warm, and said he would be prepared to bet on it.
When Annan
tried to arrange a specific bet, Lindzen insisted on 50:1 odds.
At that rate, Annan didn’t see any upside to the bet. Annan’s
characterization is that when Lindzen quotes odds that are that
steeply in his favor, he is saying that he doesn’t have much
confidence in his views. (I think he is precisely correct on this
point. When I’m confident of a position I give my opponent odds so he
is incented to take the bet.)
Anyway, this is the kind of conversation that I think Prediction
Markets encourage. There’s a public debate going on about global
warming, and in the popular press it’s hard to tell what each side
actually believes and with what confidence. In order to have a bet,
the two sides have to agree on what the terms are, and agree that they
disagree on some specific prediction. They can disagree on the odds
that some specified outcome will occur, or disagree on the value of
some measurement that can be taken at a specified date in the future.
In either case, there’s room for a bet. The best bets leave each side
believing that they are getting better than even returns.
Annan
makes this point repeatedly, and shows that he is
looking for
the kernel of the disagreement so he can create a bet that the other
party will see as advantageous. He then points out that someone who
gives odds for some event in a paper that will be referred to by the
media and by politicians when making policy decisions they should be
certain enough of the numbers to back them up with their own money, or
there’s little sense in claiming that they represent the advocate’s
best estimate.
Once a bet has been made, particularly if it’s done in a context like
a prediction market where the odds represent the opinions of more than
two antagonists, the odds and the prediction are publicly visible, and
everyone gets a better idea of what the divergent views are.
AFAICT, all of the arguments made in the discussion about the value of
betting were dealt with by Robin
Hanson in his earliest papers on Idea Futures.
Some examples:
-
uninformed betters (The existence of profit potential will draw in
more informed betters and give an incentive for contrarian research.) -
long time frames for results. (Robin suggested that the bet’s
values could be stated in terms of a market-neutral instrument like
T-Bills or the S&P 500. This would mean that the money held against
the bet would appreciate like any other investment.) -
Manipulation (Robin has done recentexperimental and
theoretical work to
show that manipulators increase the returns and incentives for betters
to move the market in the correct direction.)
The latest cover story in BusinessWeek, The Power Of Us, is about using the Internet to harness the power of decentralized talent — from web services to let small merchants build big stores, to aggregating marketing information fragmented across engineers, salespeople, and customers using markets.
It also quoted two of the contributors to our recent workshop at Supernova 2005!
On Prediction Markets:
Eli Lilly & Co., Hewlett-Packard Co., and others are running “prediction markets” that extract collective wisdom from online crowds, which help gauge whether the government will approve a drug or how well a product will sell. …
Corporate planners are even starting to use the wisdom of online crowds to predict the future, forecasting profits and sales more precisely. Prediction markets let people essentially buy shares in various forecasts, often with real money. Most famously, they’ve been employed in the University of Iowa’s experimental Iowa Electronic Markets to determine, with remarkable accuracy, the most likely winner of the Presidential election. The ease of organizing groups on the Net has caused an explosion in their use, says Emile Servan-Schreiber, CEO of NewsFutures Inc., a consultant that has run 40,000 prediction markets for companies and publications.
Hewlett-Packard Co.’s services division was having trouble a few years ago with forecasts in the first month of a quarter. So Bernardo A. Huberman, director of HP Labs’ Information Dynamics Lab, set up a market with 15 finance people not normally involved in such planning. They bought and sold virtual stock that represented a range of forecasts at, above, and below the official company forecast. Their collective bets yielded a 50% improvement in operating-profit predictability over conventional forecasts by individual managers.
On Amazon Web Services (AWS):
At Amazon.com, thousands of volunteers write buyer’s guides and lists of favorite products. Amazon also lets thousands of merchants, from Target Stores to individuals, sell on Amazon pages.
What’s more, Amazon is opening up the technology behind product databases, payment services, and more to 65,000 software developers. They’re creating new services, such as the ability to compare brick-and-mortar store prices with Amazon’s by scanning a bar code into a cell phone. Thanks in part to such moves, the company is solidly profitable on $6.9 billion in sales last year. “We’re all building this thing together — Amazon itself, outside developers, associates, and customers,” says Jeff Barr, Amazon’s Web services evangelist.
“… an economy of the people, by the people, for the people”
At the same time, peer power presents difficult challenges for anyone invested in the status quo. Corporations, those citadels of command-and-control, may be in for the biggest jolt. Increasingly, they will have to contend with ad hoc groups of customers who have the power to join forces online to get what they want. Indeed, customers are creating what they want themselves — designing their own software with colleagues, for instance, and declaring their opinions via blogs instead of waiting for newspapers to print their letters. “It’s the democratization of industry,” says C.K. Prahalad, a University of Michigan Stephen M. Ross School of Business professor and co-author of the 2004 book The Future of Competition: Co-Creating Unique Value with Customers. “We are seeing the emergence of an economy of the people, by the people, for the people.”
The Million-Monkey Money Manager:
One investment-management firm, Marketocracy Inc., even runs a sort of stock market rotisserie league for 70,000 virtual traders. It skims the cream of the best-performing portfolios to buy and sell real stocks for its $60 million mutual fund.
By now, most of you have seen announcements of the recent paper showing that Oxytocin increased the level of trusting behavior in an experimental setting. The aspect of these results that I want to
focus on isn’t the medical implications, or the possibilities for abuse or for clinical use, but the fact that economics experiments are being accepted as the standard measuring tool for social behavior in a medical context. Three of the four authors of the paper are economists, judging from their affiliations. The results were published as a letter in Nature.
The economists used a couple of fairly standard game designs that measure trusting behavior in an interaction. The first of two experimental subjects can choose to trust the second, even though there are no later interactions that give the first player an ability to reward or punish the second player’s behavior.
The authors went to a lot of trouble to design an experiment (read the paper!) that distinguished between the trusting behavior of the first players and the trustworthiness of the second. Only the former was enhanced. They then add a variant in which the second role is played by a computer. The first players were more trusting of people under the influence of Oxytocin than without it, but their play when a computers was making the second decision was unaffected by Oxytocin.
The experimenters administered questionnaires to determine whether it was the subjects’ expectations about the outcomes rather than their level of trust that was affected by the Oxytocin. The subjects had indistinguishable expectations about how much they would get by trusting, but still the subjects trusted more under the influence of Oxytocin.
All the news reports I’ve read accept the experimenters terminology, and write as if they agree that what is being measured is indeed trust. This is the first case I can think of in which a medical intervention had effects on interpersonal behavior that matched the kinds of behavior that experimental economists have been probing for a while. I’m impressed by the level of acceptance of economic experiments as measures of something as evanescent as trust. That’s a level of respect I don’t remember seeing before.
Thanks to Ben Sittler for the initial pointer, and the hint that this was relevant to my interests.
TradeSports, the Irish betting site that hosts a multitude of sports bets as well as questions on legal claims (which Supreme Court justice will leave the bench next), current events (which city will host the next Olympics, will Social Security reform pass this term) is making a move to enter the US market. Their recent market on the question of which Cardinal would succeed Pope John Paul II correctly picked Cardinal Ratzinger as the front runner. They had fine-grained markets in all the well-known Cardinals and had aggregated markets by region (Europe, Africa, South America) and by ethnicity.
TradeSports’s announcement says that they have established a subsidiary in the US, which will pursue an application with the CFTC to be a regulated exchange. They hope to offer contracts on “such subjects as weather, economic indicators and financial indices.”
Let’s hope the competition is good for TradeSports as well as HedgeStreet and anyone else who is pursuing this route more quietly. I hope at least one of the entrants can structure their application so the questions they offer are open-ended and new kinds of questions can be added dynamically. But I would expect the first applications to specify a complete list of their offerings in order to get approval. That means they’ll be limited to a narrow spectrum of financial derivatives. These derivatives are interesting, but won’t be traded widely very soon. It’s hard to attract large players with markets as thin as HedgeStreet has. More topical questions would lead to more interest, though it would, of course, be harder to argue that the point is “to manage certain financial risks that are currently not provided for on traditional futures exchanges.”