Tag Archives: Yahoo!

Search engine futures!

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Cross posted from Oddhead Blog.

I am happy to report that on my suggestion intrade has listed futures contracts for 2008 search engine market share.

Here is how they work:

A contract will expire according to the percentage share of internet searches conducted in the United States in 2008. For example, if 53.5% of searches conducted in the United States in 2008 are made using Google then the contract listed for Google will expire at 53.5&#8230-

&#8230-Expiry will be based on the United States search share rankings published by Nielson Online.

I think this could be a fascinating market because:

  • Search engine market share is very important to these major companies, with dramatic effects on their share prices.
  • Search engine market share is fluid, so far with Google growing inexorably. However, Microsoft has cash, determination, Internet Explorer, and the willingness to experiment. Ask.com has erasers, 3D, ad budgets, and The Algorithm. Yahoo!, second in market share, often tests equal or better than Google, and new features like Search Assist are impressive.
  • The media loves to write about it.
  • A major search company might use the market to hedge. Well, this seems far-fetched but you never know. Certainly, from an economic risk management standpoint it would seem to make a great deal of sense. (Here, as always on this blog, I speak on behalf of myself and not my company.)

Finally, I have to comment on how refreshingly easy the process was in working with intrade. They went from suggestion to implementation in a matter of days. It&#8217-s a shame that US-based companies are in contrast stuck in stultifying legal and regulatory mud.

InTrade-TradeSports and BetFair-TradeFair are barred from advertising on Google, Yahoo! and MicroSofts networks of websites.

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Via Jason Ruspini and Daniel Horowitz, The Associated Press &amp- Reuters.

But I will remark that Google Ads serve both InTrade and TradeSports. [I don&#8217-t mind. Just a remark.]

Author Profile&nbsp-Editor and Publisher of Midas Oracle .ORG .NET .COM &#8212- Chris Masse&#8217-s mugshot &#8212- Contact Chris Masse &#8212- Chris Masse&#8217-s LinkedIn profile &#8212- Chris Masse&#8217-s FaceBook profile &#8212- Chris Masse&#8217-s Google profile &#8212- Sophia-Antipolis, France, E.U. Read more from this author&#8230-

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

  • Comments are now completely open on Midas Oracle.
  • Albert Einstein, Chairman of the Midas Oracle Advisory Board
  • Erratic –but not Stochastic– Charts
  • Barack Obama is the 44th US president.
  • We already have prediction markets in future tax rates. It’s called the municipal bond yield curve.
  • DELEGATES AND SUPERDELEGATES ACCOUNTANCY
  • O’Reilly – Money-Tech Conference

Predictocracy: Market Mechanisms for Public and Private Decisionmaking – THE MARKET WEB

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Predictocracy: Market Mechanisms for Public and Private Decisionmaking &#8211- by Michael Abramowicz &#8211- 2007-xx-xx &#8211- (fall)

Chapter: The Market Web &#8211- (towards the end of the book)

&#8212-

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&#8217-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 &#8212-which could well be since Bo Cowgill, David Pennock and Todd Proebsting are ambitious guys.

&#8212-

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 &#8212-for real estate, I&#8217-m thinking of Zillow, or some improved mechanisms derived on Zillow.

&#8212-

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&#8217-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&#8217- recommendations in some fraction of cases to discipline the market&#8217-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 &#8220-adjudication of plaintiff&#8217-s liability in a particular case.&#8221-

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&#8217- 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&#8217-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 &#8220-leaf&#8221- 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&#8217-ll mind this for a while. I&#8217-m sure that the Midas Oracle readers will find this idea original &#8212-and maybe, interesting.

Email Interview: Ken Kittlitz

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My responses to a set of questions Chris Masse recently emailed to me:

Chris. F. Masse: Ken Kittlitz, you co-founded the Foresight Exchange (it went by the name &#8220-Idea Futures&#8221- at the time) in 1994. Would you mind telling me two words on your co-founders? Which ones brought the most into the project? Are you still in touch with them? Do you know what they have become?

Ken Kittlitz: David McFadzean got the ball rolling by bringing one of Robin Hanson&#8217-s early prediction market papers to our weekly discussion group. Sean Morgan realized that the WWW, then still in its infancy, would be a great way to create such a market. Mark James, along with Sean, did most of the coding of the initial prototype. Duane Hewitt and myself did most of the work on a paper and presentation that our group presented at a conference the following year.

I&#8217-m still in touch only with David- he&#8217-s currently a software architect at QuIC, a company that creates financial risk analysis/mitigation products.

CFM: What was the spirit of your group at that time (in 1994). Did &#8220-entrepreneurship&#8221- mean something for you, guys? Did you envision a commercial venture, or was it just collegians&#8217- play?

KK: Our weekly discussion group was known as the &#8220-BS Group&#8221- (Biological Simulation, in case you&#8217-re wondering), so I&#8217-d have to admit that &#8220-collegians&#8217- play&#8221- is a fair summary. In 1995, we did try to turn it into a commercial venture, which quickly revealed our lack of business experience. We were all techies of one sort or another, and techies often struggle in the business realm.

CFM: Would you mind telling me two words on GMU professor Robin Hanson? How would you introduce him to some of our readers (I pity them) who have never heard of him?

KK: Robin&#8217-s one of the smartest people I&#8217-ve ever met and, unlike many smart people, not over-specialized. He has deep understanding of a number of fields: artificial intelligence, physics, economics and likely a few others I&#8217-m not aware of. He has a habit of coming up with fascinating, controversial ideas, prediction markets being just one example.

CFM: You co-founded this play-money prediction exchange (Foresight Exchange) in 1994. In 1999/2000, Andrew Black and Edward Wray created and launched BetFair in England. BetFair became one of the most successful British start-ups and its two co-founders are now sitting pretty on a small fortune. In hindsight, don&#8217-t you think that you should have moved to the U.K. and incorporated the Foresight Exchange there, using real money?

KK: In hindsight, I think that I should have done a massively-leveraged short sale of NASDAQ stocks in March, 2000. :-)

The best way forward is always hard to identify, even with tools like prediction markets&#8230-

When we tried to commercialize the original &#8220-Idea Futures&#8221-, starting a real-money market offshore was certainly something we considered &#8212- though at that point, somewhere in the Caribbean seemed the likely venue. Even back then, it seemed likely that prediction markets would be considered a form of gambling, and hence subject to draconian restrictions. The Caribbean can be a nice place to live, but the prospect of never being able to return to North America to visit family and friends was quite a disincentive.

CFM: One thing that strikes me when visiting the Foresight Exchange is that you forbid sports prediction markets, which are very popular on the betting exchanges. Even Bo Cowgill&#8217-s group of Googlers trade on sports, sometimes &#8212-I believe. Sports trading can be fun. Are you a jock hater?

KK: Not really, but the Foresight Exchange was created primarily to focus on science and technology claims. Having it cluttered with a couple of dozen &#8220-tonight&#8217-s game&#8221- claims per day wasn&#8217-t too appealing.

CFM: If I can count, you have more than 12 years of experience in the field of prediction markets. You&#8217-ve seen them all, in all colors and shapes. Do you agree with what Robin Hanson said at the Yahoo! Confab, namely that the DARPA&#8217-s PAM scandal ignited interest in corporate prediction markets? Was the PAM scandal a &#8220-tipping point&#8221-?

KK: No. I think the real tipping point was the publication of James Surowiecki&#8217-s &#8220-The Wisdom of Crowds&#8221-. Those of us interested in prediction markets tend to overestimate the PAM controversy&#8217-s importance- it was a big deal for us, but only an incremental step in the general public&#8217-s awareness of the topic. The interest generated by Surowiecki&#8217-s book showed that prediction markets had &#8220-arrived&#8221- &#8212- they weren&#8217-t just of academic interest, but instead had real-world applicability.

CFM: Note that the DARPA&#8217-s PAM prediction markets was to be public. Which leads to my next question. You and partner David Perry at Consensus Point help Fortune-500 companies setting up and running their own internal prediction markets. Have you ever had the case where one firm opened its corporate prediction markets to contractors and clients?

KK: Some of the firms we deal with are certainly interested in having a fairly wide audience, including customers and contractors, for their markets. I can&#8217-t go into specifics at the moment, however.

CFM: How is Consensus Point doing, so far? Can you draw for us the portrait of the firm that wants to use internal prediction markets? Is it always to forecast sales? Do you sense that the requests come from senior executives or from mid-level prediction markets-enthusiast managers?

KK: Consensus Point is doing very well so far. A lot of inquiries do indeed originate from mid-level managers and researchers, but a fair number also come from the executive level. Sales forecasting is a popular application of the market, but project completion times and commodity price forecasting have also proved to be frequent questions.

CFM: Sorry to ask you this question bluntly. Would TradeSports and Betfair make great competitors of Consensus Point if ever they decided one day to sell prediction market services to organizations?

KK: Quite possibly, but it&#8217-s certainly not a given. Both companies have great trading platforms, but their expertise is in running real-money, public markets. Corporations aren&#8217-t really looking for that sort of domain knowledge when considering how to implement and use a prediction market.

CFM: Would you mind describing in a few words the prediction market services you sell? I guess it&#8217-s web-hosted CDA, but are some firms interested in web-hosted MSR?

KK: We offer both hosted and on-site installations of our software, as well as training, analysis and consulting services. As for MSR versus CDA, see below.

CFM: Speaking of Market Scoring Rules, why did you decide to use this design as the engine for the Washington Stock Exchange? What is its main competitive advantage to CDA? How can MSR best be described: &#8220-betting&#8221- or &#8220-simplified trading&#8221-?

KK: The line between an MSR and a CDA is thinner than you might think! We have a market maker for each stock that provides liquidity by placing bid and ask orders- this is a convenient way of implementing an MSR within a CDA framework. An MSR really helps to start (and keep) the market going, because people always have a price they can buy or sell at. With an unadorned CDA, the bid/ask spread can be enormous, and trading volumes very thin. This alas, is often the case on the Foresight Exchange.

I&#8217-d describe an MSR as allowing for &#8220-simplified trading&#8221- rather than &#8220-betting&#8221-, though I suppose it depends on how much thought the person interacting with it puts in!

CFM: Just curious. When a prediction exchange decides to use MSR, does it have to pay fees or royalties to its inventor, Robin Hanson?

KK: I don&#8217-t believe so, but Robin is in a far better position to answer that question than I am&#8230-

CFM: What is the biggest mistake (if any) you have made since the grand opening of Consensus Point? What did you learn from this big mistake?

KK: No really big mistakes come to mind. Of course, such things are often only obvious in retrospect, so ask me again in a few years.

CFM: What are corporate prediction markets competing against (if any)? Internal polls? Groups of in-house experts? The firm&#8217-s executives? Something else?

KK: Generally, the firm&#8217-s executives. We haven&#8217-t encountered too many cases where firms have been trying to use internal polls as part of their forecasting efforts.

CFM: Are you positive that corporate prediction markets will show something for it? Will the economics literature soon be filled with business cases on how firms can clearly benefit from using internal prediction markets?

KK: Based on my experiences in the field thus far, I&#8217-m confident that prediction markets will prove to compare favorably with the other forecasting methods companies use. This isn&#8217-t to say that they&#8217-ll always yield good information, or be the best thing to use in all situations, but I think they will turn out to be valuable.

Am I positive of this? Not absolutely. But then, I try not to be absolutely positive of anything!

CFM: Now, the question that kills. Tell me frankly. Are corporate prediction markets a &#8220-fad&#8221- or are they just started?

KK: Great question! I think it largely depends on how the prediction market community presents the ideas. There&#8217-s a very real danger that the topic will be over-hyped and, consequently, ultimately dismissed, just as so many other trendy business ideas have been in the past. Today&#8217-s darling is often tomorrow&#8217-s pariah. That would be a shame, since (obviously) I think the markets have a lot of merit.

Note by &#8220-prediction market community&#8221-, I&#8217-m referring not only to those who create and sell prediction markets and associated services, but also people who blog about the topic, create vortals, etc. Not mentioning any names here --) .

CFM: Are prediction markets just one forecasting tool, or do they have a bigger function, in your view?

KK: The pragmatist in me says they&#8217-re just one tool, albeit a great one. The idealist finds something profoundly appealing in their ability to democratize how information is gathered and, ultimately, how decisions are made. The idealist thinks they&#8217-re something more.

BetFair: Which of these parties will have more seats in the US Senate following the 2006 US Senate Elections?

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Republicans: 49%

Democrats: 53.8%

Ex-BBC News Mike Smithson (of the Political Betting blog) wonders whether BetFair will count the two Independent U.S. Senators (Liberman and Sanders) in the Democratic camp.

Lieberman won re-election as the “Connecticut For Lieberman” party candidate – an independent political party he created after losing the 2006 Democratic primary election to Ned Lamont. He has said he will sit as part of the Democratic Senate caucus in the upcoming 110th Congress.

Sanders won yesterday in Vermont as an independent but will caucus with the Democrats and it is said will be counted as a Democrat for the purposes of committee assignments.

The problem that Betfair will have to resolve is that neither ran as a Democrat although they will be attached to the Democrats in the Upper House.

To add to the complication Nick Palmer, MP, posted this on the previous thread at 1.34pm – “I have it in writing from Betfair that they will count the two independents as Democrats. (I asked them a month or two ago before I put a tenner on.) If you have opposite advice in writing, they should be embarrassed!”.

Addendum: From one commenter&#8230-

The question was “Which of these parties will have more seats in the US Senate following the 2006 US Senate Elections?”

The options were Republicans and Democrats. The result is 49-49 with two independents.

It’s a draw- I can’t see how anyone can see otherwise.

Addendum 2: From Yahoo! News (whose data are provided by the Associated Press)&#8230-

Liberman (CT) and Sanders (VT) are counted as Democrats.

Addendum 3: From the Washington Post frontpage&#8230-

Editor&#8217-s Note: Independent members of Congress typically caucus with the Democrats.

Addendum 4: From the New York Times&#8230-

Full Senate Results &#8212- Republican: 49 &#8212- Democratic: 50 – Includes independents who align with the Democratic caucus. &#8212- [CFM’s NOTE: Virginia is still in play at the time of writing.]

Addendum 5: Mike Smithson&#8230-

So punters who are tempted into this market are risking money on how they think Betfair will settle the market.