Check that CFM page for updates. And contact me so I can make additions to the list. (I’ll then re-publish that updated list on Midas Oracle.)

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• Michael Abramowicz – Michael B. Abramowicz – (Law School, George Washington University, Washington, D.C., U.S.A.) — Post Archives at Midas Oracle
• Bernd H. Ankenbrand – Bernd Ankenbrand – (Lecturer, Witten/Herdecke University, Germany, E.U.) — Post Archives at Midas Oracle
• James Annan – (Global Environment Modelling Research Program, Frontier Research Center for Global Change, Japan)
• Werner Antweiler – (UBC Election Stock Market, Sauder School of Business, University of British Columbia, Vancouver, Canada)
• Kenneth J. Arrow – Kenneth Arrow – (Economics Department, Stanford University, California, U.S.A.)
• Tom W. Bell – Tom Bell – (Law School, Chapman University, California, U.S.A.) — Post Archives at Midas Oracle
• Joyce E. Berg – Joyce Berg – Profile – (Iowa Electronic Markets, Accounting, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Richard Borghesi – (Finance, Texas State University at San Marcos, Texas, U.S.A.) — Post Archives at Midas Oracle
• Kay-Yut Chen – (Information Services & Process Innovation Lab, HP Labs, HP, California, U.S.A.)
• Yiling Chen – (Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Eric Crampton – (Department of Economics, University of Canterbury, New Zealand) — Post Archives at Midas Oracle
• Ely Dahan – (Marketing, Anderson School, University of California at Los Angeles, Los Angeles, California, U.S.A.)
• Anita Elberse – (Marketing, Harvard Business School, Harvard University, Massachusetts, U.S.A.)
• Leslie R. Fine – Leslie Fine – (Information Dynamics Lab, HP Labs, California, U.S.A.)
• Gary William Flake – Gary W. Flake – Gary Flake – (MicroSoft, U.S.A.)
• Robert Forsythe – (Dean, College of Business Administration, University of South Florida, Florida, U.S.A.) — Previously: (Economics, Iowa Electronic Markets, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Lance Fortnow – Lance J. Fortnow – (Department of Electrical Engineering and Computer Science, McCormick School of Engineering, Northwestern University, Illinois, U.S.A.)
• Keith Jacks Gamble – Keith Gamble – (Economics (University of California at Berkeley) – California, U.S.A. — Post Archives at Midas Oracle
• Michael Gorham – (IIT Center for Financial Markets, Stuart School of Business, Illinois Institute of Technology, Illinois, U.S.A.) — Previously: (CFTC)
• Thomas S. Gruca – Thomas Gruca – Profile – (Iowa Electronic Markets, Marketing, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Refet Gurkaynak – (Department of Economics, Bilkent University, Turkey)
• Friedrich August Von Hayek — R.I.P. — The market as an information aggregation tool: The Use of Knowledge in Society —
• Robert W. Hahn – Robert Hahn – (Executive Director, Reg-Markets Center, American Enterprise Institute, Washington, D.C., U.S.A.) — Post Archives at Midas Oracle
• James D. Hamilton – James Hamilton – (Department of Economics, University of California at San Diego – California, U.S.A.) — Post Archives at Midas Oracle
• Robin Hanson – Robin D. Hanson – (Economics, James M. Buchanan Center, George Mason University, Virginia, U.S.A.) — Post Archives at Midas Oracle
• M. Todd Henderson – (Law School, University of Chicago, Chicago, Illinois, U.S.A.)
• Chris Hibbert – (Software Architect, Zocalo Project Manager, California, U.S.A.) — Post Archives at Midas Oracle
• Charles Holt – Charles A. Holt – (Department of Economics, University of Virginia, Virginia, U.S.A.)
• Bernardo A. Huberman – Bernardo Huberman – (Social Computing Lab, HP Labs, California, U.S.A.)
• Frédéric Koessler – (Economics, Centre Nationale de la Recherche Scientifique, France, E.U.)
• Anthony M. Kwasnica – Anthony Kwasnica – (Business Economics, Department of Insurance and Real Estate, Smeal College of Business Administration, Penn State University, Pennsylvania, U.S.A.)
• Nicolas Lambert – (Department of Computer Science, Stanford University, California, U.S.A.)
• John O. Ledyard – John Ledyard – (Economics and and Social Sciences, Division of Humanity and Social Sciences, California Institute of Technology, California, U.S.A.)
• Andrew Leigh – (Economics, Research School of Social Sciences, Australian National University, Australia)
• Steven Levitt – Steve Levitt – Steven D. Levitt – (Economics, Director of the Becker Center on Chicago Price Theory, Graduate School of Business, University of Chicago, Illinois, U.S.A.)
• Saul Levmore – (Law School, University of Chicago, Illinois, U.S.A.)
• Robert E. Litan – Robert Litan – (Vice President for Research and Policy, Kauffman Foundation, Kansas City, Missouri, U.S.A.)
• Thomas W. Malone – Thomas Malone – Tom Malone – (MIT Center for Collective Intelligence, Management, MIT Sloan School of Management, Massachusetts Institute of Technology, Massachusetts, U.S.A.)
• Charles F. Manski – Charles Manski – (College of Arts and Sciences, Norwestern University, Illinois, U.S.A.)
• Adam Meirowitz – (Department of Politics, Princeton University, New Jersey, U.S.A.)
• Paul Milgrom – (Department of Economics, Stanford University, California, U.S.A.)
• Tracy Mullen – (Information Sciences and Technology, Penn State University, Pennsylvania, U.S.A.)
• Forrest Nelson – Profile – (Iowa Electronic Markets, Economics, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• George R. Neumann – George Neumann – Profile – (Iowa Electronic Markets, Economics, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Ryan Oprea – Profile – (Director of the LEEPS laboratory, Economics, University of California at Santa Cruz, California, U.S.A.)
• Gerhard Ortner – (University of Applied Sciences, Austria, E.U.)
• Marco Ottaviani – (Management and Strategy, Kellogg School of Management, Northwestern University, Illinois, U.S.A.) – Formerly: (Economics Department, London Business School, United Kingdom, E.U.) — Post Archives at Midas Oracle
• David Paton – (Industrial Economics, Business School, Nottingham University, United Kingdom, E.U.)
• David M. Pennock – David Pennock – (Principal Research Scientist, Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Charles R. Plott – Charles Plott – (Economics and and Social Sciences, Division of Humanity and Social Sciences, California Institute of Technology, California, U.S.A.)
• David Porter – (Economics and Finance, Chapman University, California, U.S.A.)
• Todd Proebsting – Todd A. Proebsting – (Microsoft & University of Arizona, U.S.A.)
• Russ Ray – (Finance, College of Business, University of Louisville, Kentucky, U.S.A.)
• Daniel Reeves – (Micro-Economic and Social Systems, Yahoo! Research Labs, New York, U.S.A.) — Post Archives at Midas Oracle
• Paul W. Rhode – Paul Rhode – (US Economic History, College of Management, The University of Arizona, Arizona, U.S.A.) – Formerly: (University of North Carolina, North Carolina, U.S.A.)
• Thomas A. Rietz – Thomas Rietz – Profile – (Iowa Electronic Markets, Finance, Henry B. Tippie College of Business, University of Iowa, Iowa, U.S.A.)
• Richard Roll – (University of California at Los Angeles, Los Angeles, California, U.S.A.)
• Thomas W. Ross – Thomas Ross – (UBC Election Stock Market, Sauder School of Business, University of British Columbia, Vancouver, Canada)
• Rahul Sami – (School of Information, University of Michigan, Michigan, U.S.A.)
• Sam L. Savage – Sam Savage – (Consulting Professor, Stanford U., California, U.S.A.) — Post Archives at Midas Oracle
• Thomas C. Schelling – Thomas Schelling – (Economics, School of Public Affair, University of Maryland, Maryland, U.S.A.)
• Emile Servan-Schreiber – (NewsFutures, New York City, New York, U.S.A. & Paris, France, E.U.) — Post Archives at Midas Oracle
• Robert J. Shiller – Robert Shiller – Profile – (Economics, Yale University, Connecticut, U.S.A.)
• Bernd Skiera – (Electronic Commerce, Goethe University, Germany, E.U.)
• Vernon L. Smith – Vernon Smith – (Economics, Economic Science Institute, Chapman University, California, U.S.A.)
• Erik Snowberg – (Stanford University, California, U.S.A.) — Soon at CalTech
• Peter Norman Sorensen – (Finance, Department of Economics, University of Copenhagen, Denmark, E.U.)
• Martin Spann – (Marketing and Innovation, University of Passau, Germany, E.U.)
• Koleman Strumpf – Koleman S. Strumpf – (Economics, School of Business, University of Kansas, Kansas, U.S.A.) – Formerly: University of North Carolina, North Carolina, U.S.A.) — Post Archives at Midas Oracle
• Cass Sunstein – Cass R. Sunstein – (Law School, University of Chicago, Chicago, Illinois, U.S.A.)
• James Surowiecki – (The Wisdom Of Crowds) – (New York, U.S.A.)
• Paul C. Tetlock – Paul Tetlock – (Finance, Business School, University of Texas at Austin, Texas, U.S.A.) — Post Archives at Midas Oracle
• Philip Tetlock – Philip E. Tetlock – (Leadership, University of California at Berkeley, California, U.S.A.)
• Hal R. Varian – Hal Varian – (Chief Economist, Google, California, U.S.A.) — Formerly: (Haas School of Business, Department of Economics, University of California at Berkeley, California, U.S.A.)
• Dorina Tila – (Economics, George Mason University, Virginia, U.S.A.)
• Leighton Vaughan-Williams – (Economics and Finance, Business School, Nottingham Trent University, United Kingdom, E.U.)
• Michael Wellman – Michael P. Wellman – (Computer Science and Engineering, University of Michigan, Michigan, U.S.A.)
• Justin Wolfers – Justin J. Wolfers – (Business and Public Policy, Wharton Business School, University of Pennsylvania, Pennsylvania, U.S.A.) — Post Archives at Midas Oracle
• Eric Zitzewitz – Eric W. Zitzewitz – (Economics, Dartmouth College, Massachusetts, USA) – Formerly: (Stanford University, California, U.S.A.) — Post Archives at Midas Oracle

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In August 2004, Todd Proebsting, a researcher in Microsoft’s platform and services division, was approached by a manager in the company’s testing organization who had spent months helping to create a piece of software to be used by other Microsoft programmers. Although it was an internal product, the software still had a rigid development schedule and an official launch date: November 2004, just a few months away. The manager had heard a talk by Proebsting about something called a prediction market, a sort of stock market for ideas, in which Microsoft employees would in effect place bets on predictions, instead of on racehorses or football teams. A lot was riding on the timely completion of the testing software. “You said that a market could be used to predict schedules,” the manager said. “I want to know when my team will finish writing the software.” Proebsting created a market with six possible bets: that the product would ship before November, in November, in December, in January, in February, or later than February. His pool of bettors included members of the development team itself, other developers, and program managers from related teams, as well as internal “customers”—the programmers within Microsoft who would use the software. He showed them all how to use the market, gave them each US $50 with which to wager, and then sat back and watched prices fluctuate. “All six months were started equally at 16 2/3 cents on the dollar,” Proebsting says, meaning that you only had to bet that amount to win $1 if you were right. “Within seconds, the pre-November market went to $0.00 and never moved from there.” So much for beating the deadline. “The November date went down to 1.2 cents in about 3 minutes.” So much for meeting the deadline. “The director of the group came to see me. He asked, ‘What have you done?’?” “No one believes your product will ship on time,” Proebsting told him. The director replied, “No one on the team is telling me this.” After discussing things with his development team, the director came to accept what the market was “saying.” He decided to cut some of the software features that were holding things up. “And the price of the markets started to reflect that—the November price rose,” Proebsting says. “Then the internal customers got wind of the fact that some of their favorite features were being cut and demanded their features back. So the market then reflected that!” In other words, the markets that predicted the software would be very late went back up. “In the end,” Proebsting says, “the product shipped in February, which is what the market predicted.” [...]

Chris F. Masse, a [] consultant in Sophia Antipolis, France, who specializes in prediction markets, says that by 2010, “10 percent of Fortune 500 companies will have gone public about their use of internal prediction markets, and probably another 10 percent will be testing some projects.” [...]

Henry Berg, who runs the Information Markets group within Microsoft, notes that in many cases a company has no formal prediction methods in place. “An organization adopting prediction markets needs to make two major adjustments: deciding to start making formal predictions about the future and choosing to use prediction markets as the mechanism,” Berg says. “In my experience, the first adjustment is greater than the second.” [...]

BetFair, NewsFutures and HSX Research are not cited.

Other than that, it’s a nice article. — Bet on It! – (page two – page three) — Via Steve Roman

MicroSoft Research does investigate prediction markets —see Todd Proebsting (“I lead Microsoft Research’s Information Forecasting Exchange project“).

Google Research does not investigate prediction markets —see this interview. (The prediction markets effort at Google is part of the 20% project of a group of managers.)

Robin Hanson prefers talking about traders “changing the price” instead of traders “buying and selling shares”. MicroSoft Research’s Todd Proebsting is the man. He has implemented a great “buying and selling shares” MSR.

Previous: Conditional and Combinatorial Betting – by Chris Hibbert

The easiest compound question to pose is a simple conjunction of two others. InTrade had separate markets in whether Bush would be reelected in 2004 (“BUSH”), and whether Osama bin Laden (“OSAMA”) would be captured before the election. Justin Wolfers and Eric Zitzewitz asked InTrade to add a single combined contract that would pay off if both came true. Their paper, Experimental Political Betting Markets and the 2004 Election shows how the prices on these three contracts can be combined to show how one event would be likely to effect the other.

InTrade created three separate claims to cover combinations of the two base questions. They were “Bush wins election” (BUSH), “Osama is captured before the election” (OSAMA), and the combination: BUSH&OSAMA which would have paid out if both the others came true. Wolfers and Zitzewitz estimated the market’s conditional probability by comparing the price of OSAMA with the price of BUSH&OSAMA. If the price levels were rational, the difference between the two prices had to equal the chance that Osama would be captured and Bush would not be reelected. Since the market price of BUSH&OSAMA was 91% as high as the price of Osama, they concluded that that represented the conditional probability. A weakness of this conclusion is that while investors and arbitrageurs have an incentive to ensure that the price of BUSH is correct relative to ~BUSH, (and OSAMA with respect to ~OSAMA), there’s no bet that lets an arbitrageur exploit superior knowledge of the conditional probabilities.

Sometimes investors believe they know how one outcome will effect another, and want to bet directly on that linkage. If you were confident before the election that Osama’s capture would raise the probability of Bush’s reelection to 95% (above the level the the market prices implied), having the conjunctive bets didn’t provide a bet that would have looked beneficial to you. You might think you could buy Bush&Osama (because you believe Bush’s chances are improved if Osama is captured) and sell ~Bush&Osama (because this is the outcome your view says is least likely), but you’d lose both bets if Osama wasn’t captured (which is an outcome your prediction doesn’t specify.)

Conjunctive claims allow observers to deduce connections between claims, but since the investors aren’t directly rewarded based on the conditional probabilities, they have little incentive to ensure that the implicit conditional probabilities reflect their understanding of the connections between the outcomes. In order to evaluate different proposals we have to look at what investors would spend up-front, and then compare the possible outcomes and how the investor’s earnings change in each situation.

If Bush is a 60% favorite to be re-elected, and the market thinks there’s only a 10% chance Osama will be captured before the election, the odds on the conjunctions might be:

If you think Osama’s capture would improve Bush’s prospects to 95%, what should you buy or sell? Your prediction says that the ratio of Bush&Osama to ~Bush&Osama should be 19:1, but doesn’t have anything to say about Bush&~Osama or ~Bush&~Osama. If you buy Bush&Osama and sell ~Bush&Osama, you can make the prices match your beliefs better, but you’ll lose money if Osama isn’t captured. In order to support conditional bets directly, market operators have to find ways to allow traders to buy positions without exposing themselves to risks due to the independent cases.

A contract that acts like a conditional bet directly (written as BUSH|OSAMA, pronounced as “Bush given Osama” or “Bush conditional on Osama”) would pay off if Bush is elected, and return your investment if Osama bin Laden isn’t captured. That gives investors the right incentive.

In order to support betting on conditional probabilities, the bets have to be able to return the investors’ money in particular cases. I know of three detailed proposals that have this property. They are: betting on arbitrary boolean expressions, representing the complete cross-product of possible outcomes (providing a complete set of Arrow-Debreu securities), and using the independent claim as currency for purchasing the dependent claim. There are two additional suggestions that might work, but haven’t been written down in sufficient detail to be sure.

Robin described and implemented Combinatorial Information Markets which represent probabilities and traders assets explicitly for all possible combinations of outcomes. Fortnow, Kilian, Pennock, and Wellman described how you might try to support bets on arbitrary boolean combinations of conditions. Their conclusion seemed to be that solving the general problem would be computationally infeasible. They didn’t describe how to address the problems they found, but I think it’s possible that a market that supported only binary combinations could be designed. And finally, Peter McCluskey built (and released as open source) USIFEX in 1999. It allows the user to use the coupons of the independent event as the currency. This combination allows traders to express conditionals directly. Unfortunately, that system didn’t attract a user base quickly enough, and Peter stopped development soon after the initial release.

For an article on Decision Markets written in 1999, Robin Hanson suggested creating markets using assets that pay off in “units of A if B passes” (and “… if B doesn’t pass.”), and allow traders to trade the assets for each other. The price of A|B in terms of B (which can be built from component assets) expresses the conditional bet. Robin didn’t explain how to set up a market in which people trade assets for assets and didn’t describe how to let the users see how various combination bets would express the conditional claims they might have been interested in. (This is the first of the two incomplete suggestions.)

Robin’s Combinatorial Information Market design uses a complex internal representation and can support arbitrary conditional bets. He built a prototype implementation that allows the user to explore these conditionals by choosing assumptions, and then adjusting probabilities in the resulting hypothetical situations. I wrote a prototype of my own in E. Neither prototype is more than a proof-of-concept that the institution works, and neither has been operated for any general market. The strength of this approach is that users can express conditional connections between arbitrary claims; this aspect has been shown to be effective in a laboratory experiment. Robin ran tests of this market after he proposed its use for PAM, and there were apparently no problems in running it with 6 traders estimating all outcome combinations for 8 events. The glaring weakness is that it doesn’t scale well. It’s not clear how to build a version that would work even with a market with dozens of questions and hundreds of users. I’ll describe this market in more detail in a future post in this series.

Peter McCluskey built USIFEX in 1999. It works quite differently and doesn’t seem to have the performance problems of the other proposals. The primary idea for supporting conditional trading is that you buy units of A|B using units of B as currency when betting on a conditional question. The effect is that when buying A|B, you end up with coupons of ~B as part of the purchase, and that’s what ensures you’ll be repaid if the independent event doesn’t occur. USIFEX is open source, but it hasn’t been maintained since it was released in 2000. The code was resurrected for use in the Swiss MarMix exchange, (AFAICT without making any use of the conditional betting features). The biggest weakness of Peter’s approach, as I recall, was that it would have taken a lot of users to ensure that the conditional markets weren’t extremely thin. A longer description of USIFEX is also in the works.

Todd Proebsting built an implementation of the Hanson design that works without conditionals. Dave Pennock wrote up a description of Todd’s approach, focused on the Market maker. I intend to describe the implications of Todd’s approach for betting on conditionals in a future post. (This is the second incomplete suggestion.) I think it might be straightforward to extend Todd’s approach to support conditional betting without running into the exponential growth of Robin’s solution. The drawback is that the market operator has to separately capitalize and enable every conditional question that you want the system to support, while Robin’s approach enables all of them by default. It’s also possible that Zocalo Open Source Prediction Market software would be compatible with this approach, where it’s clear that Zocalo would require substantial modification to support the Hanson proposal.

Other Articles in this series

• PM intro: basic formats (2005-12-30)
• PMs with Open-ended Prices (2006-01-05)
• Looking at Both Sides (2006-04-17)
• Book and Market Maker (2006-04-28)
• Liquidity in N-Way claims (2006-07-19)
• Continuous Outcomes using Bands and Ladders (2006-09-20)
• Integrating Book Orders and Market Makers (2007-01-10)

Cross-posted from pancrit.org.

Chapter: The Market Web – (towards the end of the book)

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Michael Abramowicz:

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses.

Will trading play-money and/or real-money event derivative contracts become commonplace? It’s likely, at the contrary, that trading will remain an elite occupation and that prediction markets with appropriate liquidity will remain scarce. Unless Google, Yahoo! (with Yootopia) and/or MicroSoft has/have a secret plan to popularize betting exchanges —which could well be since Bo Cowgill, David Pennock and Todd Proebsting are ambitious guys.

—

Michael Abramowicz:

For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues.

Interest rate prediction markets would help, for sure. As for geographic forecasting, maybe non-trading mechanisms could help —for real estate, I’m thinking of Zillow, or some improved mechanisms derived on Zillow.

—

Michael Abramowicz:

The Market Web

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses. For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues. That way, as the market predictions change, the spreadsheet’s bottom line would change as well. Predictions in many prediction markets may be interrelated, and so market participants in one prediction market will often have incentives to take into account developments in other prediction markets. Prediction markets thus can affect one another indirectly, as participants in one update their models based on developments in another.

Sometimes, however, it might be desirable to construct links among prediction markets so that changes in one automatically lead to changes in another. Consider, for example, the possibility of a market-based alternative to class action litigation. In Chapter 8, each adjudicated case represented a separate prediction market, but often there will be issues in common across cases. Many thousands of cases may depend in part on some common factual issues, as well as on some distinct issues. Legal issues also may be the same or different across cases. Someone who improves the analysis of any common factual or legal issue can thus profit on that only by changing predictions in a very large number of cases. A better system might allow someone to make a change across a single market and have that change propagate automatically to individual cases.

The critical step needed to facilitate creation of the market web is to allow a market participant to propose a mathematical formula to be used for some particular prediction market. Some of the variables in that formula could be references to other, sometimes new, prediction markets. For example, a market participant might propose in a market determining how much amages the plaintiff should receive a formula dependent on variables such as the probability that the plaintiff states a cause of action, the probability that the plaintiff was in fact injured, the probability given injury that the defendant caused the injury, the probability given a cause of action that the defendant is subject to strict liability, the probability given no strict liability that the defendant was negligent, and the damages that the plaintiff should be awarded if liability is proved. This formula, for example, presumably would allow for no damages where the plaintiff probably does not state a cause of action. Each of the components of this formula might be assessed with a separate prediction market. We can easily build the market web by combining three existing tools. The first tool is a text-authoring market. The relevant text would be the formula itself, including specifications of other prediction markets that would be used to calculate specific variables. As with any text-authoring market, a timing market would determine when a proposal to change the text should be resolved. Other markets might become live only once proposals to take them into account were approved. Ex post decisionmakers would assess the wisdom of these markets’ recommendations in some fraction of cases to discipline the market’s functioning.

The second tool would be a simple normative prediction market corresponding to the text-authoring market. It might also be possible to have computer software that automatically parses the formula and consults various sources, but the market sponsor need not build this tool. Rather, ex post decisionmakers will assess the appropriate value for the normative prediction market based on the formula. An advantage of this approach is that it would make it easy to use complicated formulas, as well as formulas that depend in part on numbers from sources other than prediction markets, or from prediction markets of other types. In addition, this approach makes it easy to collapse a formula into a single prediction market, if that should prove desirable. The formula text simply would be changed to a description of the market to be created, such as “adjudication of plaintiff’s liability in a particular case.”

Finally, the third tool necessary is a mechanism for determining the market subsidy. A separate subsidy would be needed for the text-authoring market and the normative prediction market. Each of these subsidies could be determined by additional normative prediction markets, perhaps with fixed subsidies. The subsidy for the text-authoring market in turn would be distributed by the text-authoring market to individuals who have proposed particular amendments, and individuals who have participated in the assessment of particular amendments. The text-authoring market also could allocate a subsidy to the first individual who creates the market and proposes some text for it. When the text-authoring market produces a new formula reflecting additional prediction markets, the subsidy for the main prediction market would fall (since calculating a formula based on other prediction markets will often be relatively easy).

A single node in the market web would thus consist of a text-authoring market describing the node and providing a formula for calculating it, a normative prediction market, and a set of additional prediction markets for determining how to distribute a subsidy to the different components of the node. The nodes collectively create a web because the formulas link to other nodes; software, of course, could easily make these links clickable. At the same time, a mechanism is needed to determine what portion of the market subsidy each node should receive. A simple approach would be for a prediction market to be used for every link, to determine the portion of the subsidy for each node that should be allocated to each node linked to it. The total should add up to less than 1, leaving some portion of the subsidy for the node itself.

With these markets established, software could easily distribute a single subsidy for the market as a whole to market participants who have traded on individual nodes when the market closes. Market participants working on one portion of the web, meanwhile, would not have to assess the relative importance of one node to nodes that are only distantly related. It would also be straightforward to have a continuously open market, periodically collecting and distributing money in accordance with individual participants’ success on the market.

This assumes that the market web would be arranged on a single server. It is possible, though, that a node on one market web might link to a node on another market web. If market sponsors allowed such links, it could promote competition among prediction market providers. It also partially answers one potential criticism of using prediction markets for decisionmaking, that a software engineer might hijack the government by faking some prediction market results. Market participants at least will have incentives to identify fake prediction markets and not link to them. In principle, it is possible to have government decisions based entirely on decentralized prediction markets. A caveat is that the government might want to subsidized individual market web providers, and it might use centralized prediction markets to accomplish that.

Whether or not the markets themselves are decentralized, they would allow market participants to make it easier to assess the basis for market predictions. Indeed, the market web is in some ways a substitute for deliberative prediction markets, because both provide means of helping observers understand the basis for the market’s predictions. An observer could look at any individual node of the market web and understand how it has been calculated, though inevitably there must be some “leaf” nodes that themselves do not contain any formulas. At the same time, software might allow an observer to find all of the nodes that link to a particular node. So a market participant addressing a factual issue relevant to many cases could link to all of the cases represented by that factual issue. As a particular issue becomes increasingly important, the subsidy for that node should rise, and market participants can profit on their analysis of the issues relevant to that node without worrying about details of individual cases.

[...]

Brainy stuff. I’ll mind this for a while. I’m sure that the Midas Oracle readers will find this idea original —and maybe, interesting.

When Todd Proebsting, director of Microsoft’s Center for Software Excellence, tested a prediction market internally, managers quickly gave it their blessing.

The goal: to have 25 members of a development team predict when a Microsoft product would ship (this was an internal product, not one sold externally). The prediction market was set up in August 2004, and the product that “had been in the works for a long time” was scheduled to ship in November 2004.

Each “trader” received $50 in their account to start with, and was told that the more accurate their prediction, the more money they would make. The market opened with an initial price of on-time delivery set to 16 2/3 cents.

“The price of ‘before November’ dropped to zero right away,” Proebsting said. “The price of ‘on time’ in about two to three minutes dropped to 2.3 cents on the dollar.” Translated, that’s more than 30-to-1 odds against on-time delivery.

Then the woman who was responsible for scheduling started trying to convince her colleagues who were buying and selling future delivery dates. “She was able to talk (on-time delivery) up to around 3 cents,” Proebsting said. “People really enjoyed moving the price… They loved this.”

“The next day the director comes into my office and said, ‘What have you done?’” Proebsting said. But further investigation showed that the product actually was behind schedule, even though nobody was telling management, and it eventually shipped in February.