IFTF's Future Now

Was the subpoena sent by txt message, too?

Earlier this year we noted a proposal by the NYPD to require that environmental monitoring devices used in New York City be registered by the police. Now the New York Times reports that lawyers representing the NYPD are asking for records from the TXTmob service, which was used by protesters at the 2004 Republication National Convention:

When delegates to the Republican National Convention assembled in New York in August 2004, the streets and sidewalks near Union Square and Madison Square Garden filled with demonstrators. Police officers in helmets formed barriers by stretching orange netting across intersections. Hordes of bicyclists participated in rolling protests through nighttime streets, and helicopters hovered overhead.

These tableaus and others were described as they happened in text messages that spread from mobile phone to mobile phone in New York City and beyond. The people sending and receiving the messages were using technology, developed by an anonymous group of artists and activists called the Institute for Applied Autonomy, that allowed users to form networks and transmit messages to hundreds or thousands of telephones.

Although the service, called TXTmob, was widely used by demonstrators, reporters and possibly even police officers, little was known about its inventors. Last month, however, the New York City Law Department issued a subpoena to Tad Hirsch, a doctoral candidate at the Massachusetts Institute of Technology who wrote the code that created TXTmob.

Lawyers representing the city in lawsuits filed by hundreds of people arrested during the convention asked Mr. Hirsch to hand over voluminous records revealing the content of messages exchanged on his service and identifying people who sent and received messages.

[h/t to Jess]

Technorati Tags: mobility, security, smart mobs

New study on Chinese-EU energy cooperation

SciDevNet reports on a new study proposing cooperation between the EU and China on alternative energy research and development:

China and the European Union (EU) can significantly advance low-carbon technologies if they cooperate closely on technological development and market access, according to a new report.

'Interdependencies on Energy and Climate Security for China and Europe', outlines common challenges faced by the China and the EU in dealing with the impact of climate change on energy security — despite differences in their economic development.

The report was presented in Beijing last month (28 February). Contributors include UK think tank Chatham House and the Chinese Academy of Social Sciences (CASS).

In order to meet its fast-growing energy demands, China will need to add power generation capacity of 1260 gigawatts by 2030. And despite stable economic development, the countries of the EU will need to generate 862 gigawatts of additional energy by 2030 to replace outdated generation facilities.

If conventional technologies are used, both China and the EU will be locked in a high-carbon development model, the report warns.

But if they work together, the EU and China — which together account for 30 per cent of the world's energy consumption — could create unprecedented opportunities for global transition to low-carbon energy generation, says the report.

China's huge energy demands, low-cost manufacturing, and cheap local technological talent offer a shortcut for the production of clean energy technologies such as wind, solar and clean coal.

China has already produced 80 per cent of the world's energy-saving lamps — many of which are based on technology created in the EU.

The report recommends that EU research bodies establish research and development centres in China and increase the involvement of Chinese expertise in the development of clean energy technology.

It also suggests that the EU builds 'low-carbon economic zones' in China and establishes a joint technology platform to improve energy efficiency in the building sector.

Technorati Tags: China, cooperation, energy, Europe

The Experience of Prediction Markets

I had been co-administering the prediction market for the Open Ex program at IFTF and pulled together a summary for people interested in Prediction Markets.  The IFTF market used InklingMarkets.com as a platform.

These ideas are pulled predominantly from the experience of running the market, rather than participating in the market.

Overview Observations

(1) Don't buy-and-hold. Prediction markets are less enticing the further into the future the event might be.  This is (obviously) because of staleness and the degree of uncertainty as a function of time, but also because a long-term market will hold someone's money trapped for a long time if they're "buy and hold." In other words, given no transaction costs for trading in and out, it's a poor strategy to buy and hold.

(2) Think ahead about how to prove something HASN'T happened. As a market administrator, you have to be very careful when constructing a market to clearly state winning conditions AND, in some cases, sources for establishing winning conditions. If you need to determine that an event has happened, since it's impossible to determine it *hasn't* happened I'd suggest requiring that the event needs to be reported in a limited number of specified, searchable venues. This might be a list of news sources, or even as specific as keyword search engine. Another route would be requiring that the players report on an event happening as proof that it occurred ( i.e. let them participate as news miner-researchers).

(3) Teach Game Strategy to Boost Confidence. There are two main flaws associated with trading. The first is given in this example:  If I believe that Obama's going to win the election, but I think it's about 60% likely and the market's trading at 95%, then I should SELL to bring the percentage in closer to my prediction. However if I sell and the market closes with Obama winning - as I expected him to do - I would lose my trade.  So the market force here actually pushes the market to an extreme price - 95%: the only people who buy at that price would think that 95% is an okay price.  People who believe he'd win but aren't quite so certain, do not sell because they'd be betting against their actual prediction.

The game strategy that would fix this would be to SELL at 95, but make sure that you cover your sell before the market closes regardless of the price.

Alternatively if you were constructing a prediction market software application, you could enable bids to be put in directly for any prediction, but the odds would be different depending on the existing distribution of the predictions. (This would be more of a betting market than a prediction market.)

The second flaw is the assumption that people will be unmoved by mob assessment.  Obviously the predictive value has been shown repeatedly in liquid markets with a lot of information and consensus building done outside the market (e.g. with elections, sporting events, etc.).  However I would imagine in a small market without information, participants might second-guess themselves. This could be a good thing if there is also a way to engage conversation; it could be a bad thing if accurate voices belong to people who lack self-confidence, such as people who rely on their intuition but who function in a strongly analytical environment.

There is an argument to the contrary, that in order for the market to be accurate there needs to be arbitrage players who are acting in uninformed ways and for reasons like entertainment, etc.



Recommendations for Market-Makers


(1) Players MUST trade into and out of the stocks frequently in order to ensure the markets are valid, rather than tending to show only the extremists' views.  This should be formally incentivized. One way would be that the winner is the person with the highest *weighted* score, where the weight is related to the number of trades placed.  (Some serious thought would have to go into that.) Players must also be explicitly taught how to sell an overpriced stock and cover before market close.

(2) Beware insider trading.  There should be conversations outside of the markets regarding the relevant news so that players can feel that they have some basic understanding of the issues. When I set up markets in the game I tried to consistently refer to at least one major news story so that players could get some context to begin with.  If there were more time available, a daily email news feed on all open market topics would be a great help if the markets aren't limited to highly visible news (e.g. the French Presidential Election).

(3) New players probably don't understand how quickly scores can change. Because trades are settled for $0 or $100 (to reflect the 0 or 100% probabilistic outcomes) it's not like real-life stock trading; if you Buy for 51 and you're correct, you make 49 on each share.  You can nearly double your balance in one successsful trade. (Or lose it.)  The leaderboard should be emailed frequently, and top winnings on each *trade* should be publicized .  "Fortunes" are made and lost quickly - this needs to be emphasized.  "The game isn't over until it's over."



Further Thoughts

I'm very interested in how to best capture the meta-market. 

(1) Options Markets. In financial markets, options are a way of pricing the expected future value of a security.  An options market with a rule excluding people from engaging in both the options market and the underlying stock market simultaneously might enable a long term forecast in a short-term timeframe.

For example:  Who will win the Nobel Prize in 2008? might have an associated options market: Will "other" in "Who will win the Nobel Prize in 2008?" be under 25% by midnight, Oct 31, 2007?

(2) Contingency Markets. I'm also interested in contingent markets for this purpose.  A "contingent market" might be a pair of related markets:  One market states that a thing happens, and you're paid the price of something if it does happen and refunded otherwise;  one market states it doesn't happen and  you're paid the price of the same thing if it doesn't happen and refunded otherwise.  The difference in the prices is the markets' expectations of the thing happening on the price of the item.

An example:  What will be the Net Revenues of a company if the CEO is fired? and What will be the Net Revenues of the same company if the CEO isn't fired?  The difference in the pricing will show you the expectation of the CEO leaving on the Net Revenues.

In this way you could have a contingent market, stop it when the action is either taken or not (a definitive short-term action) and then return to benchmark at the end of the period of time -- let's say one year -- what has occurred.  (Presumably everyone who has participated will have use of the funds in the interim and the settlement will really be an adjustment a year later.)

Telepresence: it's the details?

In the first years after its founding in 1968, one of the biggest projects the Institute for the Future undertook was a study of online collaboration systems, and their impact on organizational behavior. The dream of the electronic system that's as good as a real meeting refuses to die; but unlike some futuristic technologies (I'm talking to you, personal jet pack), this one seems to be getting closer to reality, as this weekend's New York Times article on the latest high-end telepresence systems suggests.

High-end videoconferencing — the magical ability to be two places at once — has had a bumpy past, plagued by jerky gestures, out-of-sync lips and sound and cumbersome equipment. Few executives liked what they saw, including unflattering pictures of themselves, and most thought the business tool was not worth the price.

But now, thanks to new technology, videoconferencing is delivering on its promise as an alternative to traditional business travel. The high-definition TV images are sharp. Broadband fiber-optic cable has replaced tired telephone lines. And the equipment is often installed in studios that are handsome and appropriately corporate....

Two things are notable about this upsurge in telepresence.

First, the video, audio, and connections are all unquestionably getting better. But what's really interesting to me about these systems, and what makes them more successful, are the low-tech details that HP, Cisco and other companies use to fill in the gaps between video and reality.

Cisco’s virtual meeting room includes an IP (Internet Protocol) phone, three broadcast-quality cameras, three ultrasensitive mikes, three 60-inch plasma screens, a crescent-shaped table that seats six and soft back-lighting.

“The table is maple to complement faces,” said a Cisco spokeswoman, Jacqueline Pigliucci. The studios are painted in identical colors, to give the impression that the people on the screen are in the same room.

The couple people I've talked to who've used these systems say that the room design is what really makes the illusion work. Another is that the service on these high-end systems is very good: as one consultant quoted in the New York Times article says, "Walk in a Halo room, and everything is ready to run." (No one ever has to reboot a real conference room.) Of course, seamlessness comes at a cost: about $18,000 a month, to be precise.

In other words, it's not just that the technology is getting better in the conventional, specs-are-getting-more-impressive kind of way: the experience of using these systems is changing for the better because their designers are paying more attention to deployment and maintenance. Nothing breaks the illusion of seamlessness like having to reboot the computer the video conference was supposed to run on.

The second notable thing is who's really using these systems.

It might be just an artifact of a very small sample size, but the heaviest users I've heard of are groups who already have standing meetings, not people who are using these systems to substitute for first-time meetings with prospective clients. The technology isn't bringing together people who have never met before, but is strengthening an connection between colleagues. As Business Week reported earlier this year,

A typical user is private equity star Blackstone Group. Several times a week, CEO Stephen A. Schwarzman gathers senior managing partners around a polished conference table in the firm's New York headquarters on Park Avenue for a five-way video call.... Blackstone has 40 video rooms stationed around the world. One executive is so enthralled with the system that he keeps the conference connection running in his office all day long. "We're big proponents of videoconferencing because of the way it enhances the quality of meetings," says Harry D. Moseley, Blackstone's chief information officer.

Financial and consulting firms have been particularly avid purchasers. Deloitte & Touche USA is installing a dozen $250,000 video suites made by Polycom so that various business units can collaborate on outsourcing ideas or interview job candidates from India. AIC Ventures, a real estate investment company, has three video rooms: one in its home base of Austin, Tex., another in Dallas, and one in Chicago. They are used for everything from reviewing new Web page designs to celebrating the close of a big deal with a (now crystal-clear) ring of a tabletop gong.

This is a bit like the experience we've had at the Institute with Google Docs: that collaboration tool has its uses for asynchronous collaboration among geographically-dispersed authors, but the best uses come when authors are in the same room, and able to talk about the document in real time.

Despite this, at least one telepresence consulting company argues that this isn't the future: "effective inter-company business," they maintain, "will be propelling this industry forward in the coming years (remember where you heard it first!).... The future of telepresence will be about connecting with vendors, customers, and joint venture partners... to lower the shared costs of business relationships."

Technorati Tags: collaboration, displays, office, video, work

Ride sharing, parking markets, and the future of property

From time to time, we've written about experiments using pervasive computing technologies to turn private goods into sharable public ones: using virtualization tools to make computers more green, using computers and mobile phones to make carsharing programs easier to use or create dynamic ridesharing systems, using RFID tags to make sharing books more secure, building networking sites to create virtual libraries. (We're hardly alone: John Thackara identified the growth of sharing as one of six big design trends when he talked at IDEO.) Today I came across two more car-related examples.

The first is Peasy.com (like Parking easy-- get it?) a new "online marketplace for parking spaces, enabling drivers to search for and book spaces before they leave home, and letting British homeowners monetize unused parking spaces by adding them to the Peasy network."

To rent out a parking space, the owner needs to register and enter all relevant details, including price, when the space is available, and whether it will be rented out daily, weekly, or both. Those who require parking can then search for suitable parking spaces and securely book them online, or first negotiate a better price. To protect the privacy of owners, searchers can't view exact addresses. Instead, they're given the street the space is on, as well as its postcode and location on a map. Once booked, the renter is provided with the exact address. If booking on a weekly basis, renters are also given the owner's contact details, enabling them to introduce themselves and arrange for collection of keys or remote controls if required.

As Springwise notes, this hits a nice combination: a growing demand for a service (in this case, safe parking spaces), and a growing familiarity with online marketplaces.

The bigger picture is that it allows property owners (in this case, people who have parking spaces) to generate more use (and revenue) from that property. Such tools-- and the cultural attitudes that make such arrangements acceptable-- are potentially significant because there are many kinds of property that are used only a fraction of their lives. Most offices, for example, are used for a few hours a week; most power tools are used for minutes, when they're designed with useful lifespans of hundreds of hours. You might never want to rent your bed out to someone who works night shifts, but being able to generate some cash from that power tiller-- that's a different story.

The second example is an under-development New York service, Hitchsters, that facilitates ridesharing among air travelers.

Everyone loves New York, except for when they have to take a cab to or from the airport and it ends up costing almost as much as airfare. Which is why smart New Yorkers are starting to plan their airport commutes via Hitchsters.com... a combination of a social networking and a ride matching site. Hitchsters' software connects travelers scheduled on the same flight and living in the same area of the city so they can save money by sharing a taxi.

This is more an example of a service that tries to reduce the costs of a service by getting more users involved-- in a sense, it's like an ad-hoc buyers' club for cab rides.

Indeed, car and ride sharing schemes of one sort or another seem to be one focal point for experiments in either sharing services more effectively, or turning private goods into public ones. When I was a columnist for Red Herring, I wrote a piece about dynamic car sharing; its reproduced in the extended post.

Technorati Tags: cars, collaboration, property, sharing

Web 2.0 video creator writes about empathy on the web

Michael Wesch, PhD, Assistant Professor of Cultural Anthropology at Kansas State University, who made the popular Web 2.0 video answers some questions at Battelle's Searchblog about the motivation behind his video.  I like that he talks about the opportunity not in terms of information but in terms of visibility and the chance for empathy.  Empathy through a computer screen is pretty hard, but it's worth the effort to make the state of the world more visible online - as in Gapminder or World Mapper, or to highlight violations of peoples' human rights - as in WITNESS.

For me, cultural anthropology is a continuous exercise in expanding my mind and my empathy, building primarily from one simple principle: everything is connected. This is true on many levels. First, everything including the environment, technology, economy, social structure, politics, religion, art and more are all interconnected. As I tried to illustrate in the video, this means that a change in one area (such as the way we communicate) can have a profound effect on everything else, including family, love, and our sense of being itself. Second, everything is connected throughout all time, and so as anthropologists we take a very broad view of human history, looking thousands or even millions of years into the past and into the future as well. And finally, all people on the planet are connected. This has always been true environmentally because we share the same planet. Today it is even more true with increasing economic and media globalization....

So if there is a global village, it is not a very equitable one, and if there is a tragedy of our times, it may be that we are all interconnected but we fail to see it and take care of our relationships with others. For me, the ultimate promise of digital technology is that it might enable us to truly see one another once again and all the ways we are interconnected. It might help us create a truly global view that can spark the kind of empathy we need to create a better world for all of humankind. I’m not being overly utopian and naively saying that the Web will make this happen. In fact, if we don’t understand our digital technology and its effects, it can actually make humans and human needs even more invisible than ever before. But the technology also creates a remarkable opportunity for us to make a profound difference in the world.

Nice little mention in Slate

A new Paul Boutin article about Google Docs has a gratifying little linkback to Future Now:

Google's word processor starts saving the file to backup servers as soon as you start typing—you don't have to remember to save it yourself. Files are automatically stored online, where you have the option of sharing them with other users. (You can also save them to your desktop.) I've used Google Docs to edit a Wired article with a co-author three time zones away. Eagle-eyed futurists have spotted a more surprising use: Co-workers in adjacent seats can edit the same file at the same time instead of hunching over each other's screens.

Google Docs has quickly turned into a must-use tool for a number of us. It's very interesting: it's part of a general trend in which we're using online tools to enhance face-to-face collaboration, and to tighten up work processes-- more or less the opposite of how these tools were "supposed" to be used.

Technorati Tags: collaboration, IFTF, work

What the hive mind is really interested in

A caution to enthusiasts of collective intelligence: the just-released 2006 Year-End Google Zeitgeist. Did global warming, Iraq, globalization, Web 2.0, or Polonium-210 make the list? No... top 10 searches were:

1. paris hilton
2. orlando bloom
3. cancer
4. podcasting
5. hurricane katrina
6. bankruptcy
7. martina hingis
8. autism
9. 2006 nfl draft
10. celebrity big brother 2006

Broadly speaking, this confirms my contention, based on a careful analysis of the popularity of videos on YouTube, that the hive mind is interested in 1) cats doing funny things, and 2) people doing embarrassing things.... Only there aren't any cats in the Top 10 (and forget leaving a comment mentioning Paris Hilton, because everyone else has thought of that joke, too).

[via Rough Type]

Technorati Tags: collective intelligence

Web 2.0 for Day Traders

Inkling, one of the Institute's technology partners, just launched Worthio - an sort of prediction market meets social site for stock market speculators. The site lets you rate stocks quickly on whether you're bullish or bearish, and presents the aggregate group opinion. You can also add people to your network and watch what they think. There are also discussion around stocks.

Interesting twist - will the "wisdom of crowds" devolve into "herd mentality" on the trading room floor?

Prediction Markets at Yahoo! ConFab

I'm in California this week for a bunch of internal workshops at the Institute, and having loads of fun running around the Valley. Last night I went over to Yahoo! for their first "yahoo.confab" event, which was about using prediction markets inside corporations. It was an amazing event. (There are professional-quality webcasts at 100kbps and 300kbps)

It was an incredibly timely opportunity. One of the projects I'm working on right now at IFTF is a new research program that focuses on new business opportunities in the open economy, called OpenEx. As part of OpenEx, we're running a prediction market hosted by Inkling Markets(we want you to play too! Shoot me an email for an invite or to get a prospectus for the OpenEx program).

I was both buoyed and dismayed by what I learned at this event. Buoyed by how much potential there is in prediction markets for improving decision-making and forecasting. Dismayed by how shallow the body of knowledge is on how to make them work well, and how to make them work well inside large organizations. (But then again buoyed that this is a big opportunity for the Institute to help advance the craft of creating and running good prediction markets).

A Brief History of Prediction Markets

Prediction markets have been around since the late 1980s, when a couple of professors started the Iowa Electronic Market (IEM) at the University of Iowa. HP ran the earliest corporate experiments in the late 1990s (see Bernardo Huberman's presentation to Howard Rheingold's class at Stanford). As Leslie Fine from HP Research described last night, that experiment only tried to predict a dozen events and only had 10-15 active traders during the 3 years it ran (1996-1999).

The modern era for prediction markets - and Robin Hanson of George Mason University showed a slide that corporate use of prediction markets is growing exponentially (but still quite a small total #) - began after the political disaster of DARPA's Policy Analysis Market in 2003. PAM sought to create a market for predictions for political and economic forecasts, but was killed by a couple of Congressional zealots and the media, who zeroed in on "the fact that PAM would allow trading in such events as coups d'état, assassinations, and terrorist attacks." Senator Wyden said at the time, "The idea of a federal betting parlor on atrocities and terrorism is ridiculous and it's grotesque" and Senator Dorgan called it "useless, offensive and unbelievably stupid". (source: Wikipedia article)

So while PAM failed, it brought the idea of prediction markets to a lot of people's attention, and as Hanson pointed out last night, he has been closely tracking media mentions of PAM over the last 3 years and there is a clear trend towards the media talking about PAM in a positive light. In the end, we make look back on the PAM debacle as a watershed moment for the broader institutional use of prediction markets.

Corporate Experiences

The discussion at Yahoo! brought to light some of the many useful characteristics of prediction markets for improving forecasting and decision-making inside companies:

• They aggregate information - the larger and broader the group, the more relevant information gets reflected in prediction market prices. In an ideal market, all relevant information is reflected in the price (the group's forecast), and any erroneous information is random and cancels itself out.
  
• They are better than polls and surveys at picking winners - both in terms of accuracy as well as what they measure. For instance, election polls often ask who you voted for, not who you think is going to win.
  
• They allow very granular forecasts - you can ask extremely specific questions, and break larger questions up into components to isolate trends and influences on broad indicators or measures like the company's stock price
  
• They encourage honest forecasts - because there is usually some sort of reward at stake in a good prediction market (money or prestige), people tend to vote with their mind not their heart. Also, since they are effectively anonymous if proper precautions are taken, people can make a bet without having to worry about what peers or bosses (or underlings) might think.
  
• They are fun - they get people interested and involved in forecasting, and can provide a platform for community inside companies.
  
• They update themselves - unlike polls and surveys which are a "push" forecasting mechanism, prediction markets are a "pull" mechanism. As new information emerges, traders react quickly by changing their positions and the market quickly moves to incorporate that new information.
  

The speakers told of some experiences at their firms:

Google, Bo Cowgill - is moving away from monetary rewards towards reputation/social rewards. Companies have to create elaborate schemes to use monetary rewards because of the regulatory restrictions on prediction markets that use real money. Also, people didn't pay attention to the rewards, but pay lots of attention to the lists of top traders. Exploring new ways to indicate performance - by team, by date of hire (newcomers vs old-timers), etc. Thinking about putting prediction market ranking into the company directory as a prestige measure. Thinking about how to allow traders to publicize/brag about their trading decisions within the platform or corporate directories.

HP, Leslie Fine - focused on what happens when prediction markets fail, which often happens in small groups. HP is rolling out a new product called BRAIN: Behaviorallly Robust Aggregation of Information in Networks, which combines aspects of prediction markets and polls. Pointed out that sometimes prediction markets disseminate too much information, which is not always good in corporate settings. BRAIN works by letting traders place bets on outcomes. They get rewarded if they win (and high performers are weighted higher), but traders don't see the aggregate forecast, only market sponsors do. HP used it reduce errors in DRAM price predictions (highly volatile commodity) from 4% to 2.5%, which had huge positive financial implications for HP. They also replaced a forecasting process that took many meetings over a period of weeks with a single meeting and an hour or so per person on the BRAIN system inputting forecasts! First production client will be Pfizer, which will use it internally to predict which new drugs in the research pipeline will be successful.

Yahoo! Research has a public prediction market to predict tech trends but is also building an entire virtual currency system for use inside Yahoo! called Yoo-topia. the virtual currency can be used for trading in prediction markets, for buying favors from other employees, or for bidding on what kind of restaurant to go to for dinner (the people that lose get Yoo-topia dollars as compensation for having to eat the food they didn't want). And it all runs off mobile devices. Cool!

Microsoft's Todd Proebsting told of a prediction market experience at Microsoft used to forecast a ship date for an internal software tool. They created a prediction market with assets for various estimates of the ship date: ahead of time, on time, one month late, etc. Within 3 minutes of trading starting, the share price for an on-time ship was down to 3 cents per share - the group was forecasting a 20:1 bet against shipping on time. The project manager was mortified. He quickly held a meeting and the team stripped out some features. The on-time stock jumped. Then the internal customers complained and the features were put back into the product. The team traders sent the on-time stock back down. The product eventually shipped a few months late, just as forecast by the prediction market.

Best Practices

So what was the takeaway? How do companies effectively utilize the many software and hosted prediction market tools available? The most surprising thing to me is how little advice or information the group had to share on this. Or more precisely, how little consensus there was. In theory, and James Suroewicki talks about this in his book The Wisdom of Crowds (he was the host last night), you do not want traders to have a stake or be able to influence outcomes. Yet many of the corporate examples did not comply with this important theoretical requirement for an efficient and accurate market, but they seemed to work anyways.

I think what it means is that companies need to experiment widely and watch closely what happens in prediction markets. But as Adam Siegel, IFTF's partner at Inkling pointed out, there are 3 main reasons why prediction markets fail:

1. People don't understand the concept
   
2. The interface isn't easy enough to use
   
3. Market structure was wrong: causes include questions that ask for opinions, poor descriptions, biased questions, and too long timeframes
   

Going forward with our OpenEx Prediction Market, it's that last issue that is going to provide the greatest challenge and the greatest opportunity. At IFTF, we generally focus on the 5-10 year time frame when we work with our clients. But this is really too long for effective prediction markets. Few have even been around long enough to even run something where 3 or 5 or 7 year markets have come to fruition, though one of the speakers said that the 10-year old Foresight Exchange has had some limited successes with long-term forecasts. This is something I really look forward to working with Inkling and our clients and anyone else who wants to play in our market to learn how to do well. It could just be the most exciting thing I ever work on here.