At various times, Rep. Barry Finegold (D – Andover), Rep. Jamie Eldridge (D – Acton), and Niki Tsongas have all led the field.

As the election approaches, however, the market seems to have settled on a consensus prediction: Niki Tsongas. … [Tsongas] is now priced at $68.50 per share in this market. This means that this market says that Tsongas has a 68.5% chance of winning the primary on Tuesday. In a distant second place is Jamie Eldridge, ….

It seems to this observer that this likely reflects the reality of next Tuesday – Niki Tsongas is likely to be the Democratic nominee in the October 1 special election to fill the congressional seat. We will see soon enough.

In the day and a half since Eisenthal posted, Tsongas has moved to 85.

[Cross posted on The Knowledge Problem.]

Massachusetts political observer David Eisenthal has posted his monthly update on the handful of prediction markets that he monitors, including a report on the Inkling Market for the Democratic Primary for the Massachusetts Fifth Congressional District Special Election. Eisenthal created and administers the market on Inkling. He observes:

The Fifth Congressional District market has continued to be very active and volatile. This has been, in fact, one of the most actively traded public markets on the Inkling platform ever. 275 traders have participated in this market – making more than 2,500 trades in a little more than two months – with 176 traders currently holding positions.

At the moment, Middlesex Community College Dean Niki Tsongas holds a commanding lead at $56.28 per share. State Rep. Jamie Eldridge (D – Acton) follows in a distant second place at $19.49 per share…

This observer believes that the current market levels are a reasonable representation of objective probabilities. However, the volume and volatility of the market means that it is likely to change quite a bit between now and September 4.

No, not “one of the most actively traded” – the most actively traded market. On Inkling’s list of markets by activity, it shows the Mass. Fifth District market as number five in number of traders, but as of this morning it has more than 2600 trades. This market has generated more activity than the sum of the next two most actively traded Inkling markets.

As noted here before, the market was blogged in a couple of Massachusetts-based political sites soon after it was created, some of which urged readers to get behind their favored candidates, and at least one candidate continues to actively urge his backers to show support by buying shares on the Inkling market.

Among other consequences of this market, at least one new member of Congress arriving this year will have become familiar with prediction markets before arriving in D.C. Perhaps as important, a few of his or her staff will likely have been actively participating in the markets. The markets will be less of an abstract thing out there vaguely connected to gambling and wasn’t-there-that-terrorists-thing? what-was-that-about? And more of something kind of interesting and fun and, just maybe, surprisingly accurate and potentially useful.

The folks interested in influencing public policy toward prediction markets should take notice.

UPDATE: Spotted on the website of the Maynard (Massachusetts) Democratic Town Committee:

Show your support

Have some fun!

Buy shares of your favorite Candidate for Congress at at: Inklingmarkets.com

The site also features the Inkling javascript widget that I’ve been unable to get to work here. I’m impressed.

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

—

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.

#1. I fully agree that vertical prediction exchanges (a la HSX) are performing a better service than vertical lines in a generalist prediction exchange. (I had some thoughts on that, last year.)

#2. The vertical prediction exchange that Alex Kirtland is talking about has popped up on Delicious and other places, because, yesterday, its founder has asked the beta users to help him promote the exchange.

#3. The founder of that “new” prediction exchange is already on the Midas Oracle list of blog authors, and will soon blog about it —the technical specifics are of interest.

#4. As for the other new “prediction market” cited by Alex Kirtland (Farmetrics), as I said the other day, I would be prudent about calling it a prediction exchange. Just because somebody writes a P.R. output with the keyword “prediction market” and places it at Google News does not mean that it is a prediction exchange, as we know it. The same remark goes for Numeria. Its founder uses the keyword “prediction markets” and paid the other guy $2,500 to be lined up in a prediction market conference, but that’s not the guarantee that what he promotes is of interest to the prediction market community. Actually, Numeria seems to have to do more with the Delphi method, and the linkage he wants to make with prediction markets is just that… an idea —just like thousands of other people have suggestions about prediction markets (sometimes interesting, sometimes unfeasible –and I include myself in the crowd). As for me, I’m being more and more prudent about what and who I index as “prediction markets”. (((I could be damn wrong, sometimes, of course. I’m just one observer, and there are plenty of others —smarter than I am.)))

From the BetFair’s Help sub-website:

How do betting exchanges work?

A betting exchange is a bookie which uses technology to make sure you get a better price. It does that by reducing its risk, which means it doesn’t have to build in extra margin as a safety cushion. The effect is that punters bet on the price they want, and not the price they’re told they can have.

Imagine two friends having a bet while watching a football match; an impartial observer holding the money while the game is played. A betting exchange works in the same way: our members bet against each other, and Betfair hold the stakes until the bet is settled.

What are the benefits of a betting exchange account?

Unlike traditional bookmakers, who employ people to compile the odds that they offer, Betfair don’t set odds, or bet in any event. We simply set up a market for our members to bet against each other. This means three main benefits for members:

- On average 20% better odds
If you want to back a selection, the odds are likely to be better than a traditional bookmaker, because individuals are betting on their opinion rather than building a margin into their price as a bookmaker does.

- Back or Lay
On a betting exchange you can back your selection to win in the “traditional” manner. Alternatively you can take the other side of the market and back a selection not to win (or “lay”), by offering odds to other members. The ability to back or lay a selection provides the added benefit of allowing you to “trade” a position and lay your bet back if it looks the likely winner, to guarantee yourself a profit.

- Bet in-play
Because there so many members around the world asking for and accepting odds while an event is “in-play”, you can back or lay right up to the final whistle of a football match or up to the last second of a horse race.

How do betting exchanges make money?

Betfair take a small commission from people who win on a market. If you lose on a market you pay no commission. For a greater explanation of our commission structure, how it works and how much you pay, see here

Why Betfair?

Betfair is by some way the world’s largest betting exchange and, with over 100,000 members betting each month from all over the world, there are members waiting to match your bet 24 hours a day 7 days a week.

Who are Betfair?

Betfair was launched in June 2000 by co-founders Andrew Black and Edward Wray. Generally regarded as one of Britain’s most successful and innovative companies, Betfair now employ over 600 people and have won a host of awards since our launch, including the prestigious Queen’s Award for Enterprise. You can find out more about the company, its performance and results at our corporate site:

www.betfaircorporate.com

Republished at Midas Oracle with the author’s permission – Emphasis added

—

Peter [McCluskey] says: “A for-profit prediction market that tries to both maximize profits and provide valuable information to voters faces conflicting pressures, since my observations of existing markets such as those on Intrade show little evidence of a significant correlation between the issues that create profitable trading volume and the issues on which a democracy needs more knowledge. If maximizing profit doesn’t provide useful evidence that the company is achieving its goal, then it’s unclear why we should think that a profit-oriented form is the best way to achieve that goal.”

I don’t see why we think we, as people who don’t have “skin in the game”, think we know what are the most valuable markets here.

First of all, markets depend on volume. The cost of setting up a new contract on an exchange like TradeSports is minimal, so if a contract isn’t trading, we can assume that either the market operators haven’t thought of it, or there just isn’t enough volume to make the market useful. Without the volume, why do you imagine you will get information out of the market prices? If there is enough volume, there is profit to be made anyway.

Second, trading in “idea futures” inevitably has a number of motives, but the most important motive for someone trading from the point of view of those who want information out of the market is the profit motive. That is unclearly phrased, so let me try explaining slightly differently. If you want information out of an information market, you want to know that the people who are trading are doing so in as unbiased a way as possible — the greater the extent to which traders are trading purely for monetary reasons, the greater the chance that they’re not going to allow their biases to interfere with their work, and thus the greater the chance that they’re revealing something previously unknown to an observer of the market.

Continuing on this point, the only way people will have sufficient monetary incentive to work hard on policing their bets in such a market is if there is sufficient volume. We aren’t going to get interesting new facts, I suspect, out of a market that has fifteen academics betting $20 in it. We’re much more likely to see serious research and effort devoted if a bet in the market is a $20,000,000 hedge. If a market can sink a $20,000,000 bet used to hedge, say, the price of a stock that would be damaged if the bet came true, said market is more than large enough to be profitable for the people running the exchange.

Thus, I don’t see why we need “public interest” exchanges to deal with “unprofitable” contracts when the contracts that are going to yield useful information are going to be quite liquid and profitable for all concerned anyway.

I have to add that I think that the profit oriented organizations, like TradeSports, have done far more for “proposition trading” than those of us with an academic interest in this. The profit oriented exchanges have strong incentives to build volume after all, and have done so. “Public interest” ideas futures markets will not have such an incentive driving them — the people running them will not share financially in the success of their market, so they will not be as strongly motivated as those who own for profit exchanges.

Lastly, let me argue against a proposition you advanced — that idea markets are of serious interest because they can help “democracies”. They can’t help democracies make better decisions. Democratic regimes routinely ignore all sorts of useful information merely because said information supports politically unpopular positions. Democracies blissfully ignore the benefits of free trade, implement minimum wages, regulate businesses to death, etc. The information on all of those problems is well known and established, and yet democracies pay it no heed at all and continue doing what is politically expedient. I doubt idea futures are going to yield solid information for policymakers because I doubt that policy makers pay attention to solid information. They pay attention to polling data instead.

If you truly wish to make democratic states a viable means of ordering legal systems, you will have to first find a way to get them to pay attention to well understood economic theory. If you can first do that, then worry about gathering information from idea exchanges.

Perry E. Metzger

First published at Overcoming Bias dot com

Republished at Midas Oracle with the author’s permission – Emphasis added