to external events. Proactive managers can use the analysis of those information flows to reorganize the company, if necessary, says Wolfers.
“A problem for economists is you can’t measure information flows, and a market actually kind of makes those flows measurable,” Wolfers says. “I would never suggest you set up a prediction market just to learn about the sociology of your organization. But it tracks, and can also change, how organizations operate.”
Although Wolfers concedes the most visible enterprise use of prediction markets is to help companies improve product and process development, he also says, “As an economist, I am much more enthusiastic about how prediction markets could help in producing better public policies.”
One public policy market that is gaining momentum is the University of Iowa’s Iowa Health Prediction Market, funded by a $1.1 million grant from the Robert Wood Johnson Foundation. The market supplies invited healthcare professionals with $100 to begin trading their forecasts on flu activity in the coming season (winners are allowed to spend their trading earnings on professional advancement, thereby reducing public opprobrium about people profiting from others’ illness).
Improving the flu markets’ utility will entail expanding the regions the markets cover, and also tackling the most challenging computational issues facing prediction market designers—creating combinatorial markets that allow a much wider range of possible outcomes, and more granular expression of them, than the traditional win-lose, bilateral markets such as election markets. Yahoo!’s Pennock is experimenting with multiple examples of these combinatorial markets, which allow both conditional “if” questions and conjunctive “and” questions to be combined in virtually unlimited multiple arrangements.
For the flu market, which Pennock says he has discussed with the Iowa researchers, a combinatorial interface would allow traders to bet on more than the expected severity of outbreaks in one region.
With a combinatorial interface, he says, “you would choose a region of the
country and choose a date range, and then also choose an outbreak range. This is a combination of things you think will happen—‘In this region, during this time frame, flu outbreak level will be red.’ And the market will price it for you.”
One enduring research problem on combinatorial markets is mitigating the effects a virtually unlimited spectrum of outcomes will have on creating markets that are so thin in trades they do not serve their purpose of aggregating information.
In such markets, which might bear a resemblance to an enterprise prediction market in that there are not enough participants to provide a statistically valid spread of opinion, Pennock says a market-maker algorithm might serve as a price setter within widely acceptable limits.
“I believe that approximation algorithms will be fine for the market maker, because people don’t really care about making bets on things that are incredibly unlikely, like 10-6 chance,” Pennock says. “But as long as you’re betting on something with a 10% chance of happening, we’ll be able to approximate pretty quickly with a market-maker price.”
Pennock says the continuous increase of computational power is making advanced research into some of these exponentially based markets feasible. “I don’t think it would have happened 10 years ago,” he says. “The horsepower to do a good approximation is somewhat more recent.”
Gregory Goth is an oakerville, ct-based writer specializing in science and technology.
Researchers at the University
of California, Davis, and
lawrence livermore national
laboratory have developed
software that makes the
analysis and visualization of
large data sets possible without
the use of a supercomputer,
reports Technology Review. the
researchers’ algorithm slices
data into manageable chunks,
then stitches it back together,
so the data can be manipulated
in three dimensions, all on a
computer with the power and
capacity of an expensive laptop.
the researchers’ algorithm
offers a method of obtaining
structural information about
materials, proteins, and fluids,
says attila Gyulassy, the UC Davis
researcher who led the project.
it allows users to “interactively
visualize, rotate, apply different
transfer functions, and highlight
different aspects of the data,”
he says.
the software uses a
mathematical tool called the
Morse-Smale complex, which
has been used to extract and
visualize elements of large
data sets by sorting them
into segments that contain
mathematically similar features.
the Morse-Smale complex has
been known for decades, but
it normally requires enormous
amounts of computer memory.
Gyulassy and his colleagues
overcame this memory problem
by writing an algorithm that
breaks apart a data set before
using the Morse-Smale complex,
then stitches the blocks back
together. as a result, only a
small amount of data is needed
at each step, so much less data
must be stored in memory.
peter Schröder, a professor
of computer science at
the California institute of
technology, notes that memory
has been one of the limiting
factors for the complex analysis
of massive data sets. “You can’t
even fit the stuff in memory,”
he says. “But [the researchers]
have addressed it.”
the researchers plan to
release an open source software
library this spring to allow
researchers to take advantage
of the approach, and revise it
according to their needs.
References:
Archives