Hey—we've moved. Visit
The Keyword
for all the latest news and stories from Google
Official Blog
Insights from Googlers into our products, technology, and the Google culture
Putting crowd wisdom to work
September 21, 2005
Posted by Bo Cowgill, Project Manager
At Google, we're constantly trying to find new ways to organize the world's information, including information relevant to our business. Building on the ideas of
Friedrich Hayek
and the
Iowa Electronic Markets
, a few Googlers (Doug Banks, Patri Friedman, Ilya Kirnos, Piaw Na and me, with some help from
Hal Varian
), set up a predictive market system inside the company.
The markets were designed to forecast product launch dates, new office openings, and many other things of strategic importance to Google. So far, more than a thousand Googlers have bid on 146 events in 43 different subject areas (no payment is required to play).
We designed the market so that the price of an event should, in theory, reflect a consensus probability that the event will occur. To determine accuracy of the market, we looked at the connection between prices of events and the frequency with which they actually occurred. If prices are correct, events priced at 10 cents should occur about 10 percent of the time.
In the graph below, the X-axis indicates the price ranges for the group. The orange line represents the average price, which is how often outcomes in that group should actually happen according to market prices. The purple line is how often they did happen. Ideally these would be equal, and as you can see they're pretty close. So our prices really do represent probabilities - very exciting!
We also found that the market prices gave decisive, informative predictions in the sense that their predictive power increased as time passed and uncertainty was resolved. When a market first opens there may be considerable uncertainty about what will eventually happen; but as time goes on, some outcomes became more likely than others. The market prices should reflect this phenomenon, with the implied probability distributions becoming more concentrated over time.
Being geeks, we naturally used information theory to measure the entropy of our probability distributions:
In this graph, we have weeks before market expiration on the X-axis, and entropy (in bits) on the Y-axis. We've included some reference entropies to help your intuition, and you can see that in addition to accurate predictions, the distributions become steadily more informative and decisive (lower entropy) over time.
Our search engine works well because it aggregates information dispersed across the web, and our internal predictive markets are based on the same principle: Googlers from across the company contribute knowledge and opinions which are aggregated into a forecast by the market. Sometimes, just feeling lucky isn't enough, and these tools can help.
Labels
accessibility
41
acquisition
26
ads
131
Africa
19
Android
58
apps
419
April 1
4
Asia
39
books + book search
48
commerce
12
computing history
7
crisis response
33
culture
12
developers
120
diversity
35
doodles
68
education and research
144
entrepreneurs at Google
14
Europe
46
faster web
16
free expression
61
google.org
73
googleplus
50
googlers and culture
202
green
102
Latin America
18
maps and earth
194
mobile
124
online safety
19
open source
19
photos
39
policy and issues
139
politics
71
privacy
66
recruiting and hiring
32
scholarships
31
search
505
search quality
24
search trends
118
security
36
small business
31
user experience and usability
41
youtube and video
140
Archive
2016
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2007
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2006
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2005
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2004
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Feed
Google
on
Follow @google
Follow
Give us feedback in our
Product Forums
.
Follow
