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NITLE Prediction Markets, CNI 2008 presentation
Citation preview
Prediction Markets for Emerging Technology in Higher Education:An Experiment in the Wisdom of
CrowdsCoalition
for Networke
d Informatio
nDecember
2008
Plan of the talk
1. Sketch of prediction markets2. NITLE program history3. Market structure and operations4. Reflections and next steps
NITLE
Nonprofit, working to advance technology in liberal education
Prediction markets
• The original: Iowa Political Stock Market (IPSM) (University of Iowa, Robert Forsythe et al)
• 1988 on: The Iowa Electronic Markets http://www.biz.uiowa.edu/iem/ This morning the market was at 50.2 cents
for a Clinton share and 48.6 cents for a Bush share, with Perot investors holding die-hard paper.
(The New York Times, August 19, 1992)
2008 Presidential election visibility
Iowa Electronic Markets, Intrade - 11/04/08http://b2e.nitle.org/index.php/2008/11/04/prediction_markets_forecast_obama_win
Prediction marketsGoogle,
sports, Foresight Exchange, many companies, DARPA
(Cowgill, Wolfers, Zitzewitz, 2008; http://www.ideosphere.com/)
Exploration for technology and higher educationWhat program relevance? Connections
with NITLE programs and interests:• Gaming• Crowdsourcing• Distributed intelligence (Surowiecki,
2004)• NITLE communities• Emergent technologies
Pilot phase
• Advisory group• Conference
demo (April 2008)
• Segmented markets
• Long-term props• Select vendor (
http://inklingmarkets.com/)
(NITLE Summit, San FranciscoApril 2008)
PilotExample proposition:
By what year will the majority of institutions provide a repository service for sharing scholarship and research?
• End of 2010 - 2011 academic year • End of 2009 - 2010 academic year • End of 2008 - 2009 academic year
Beta• Responses to pilot: unitary market• Public beta launch (August 2008)
Architecture• Web 2.0 approach
– Microcontent– AJAX operations– Social tools– Integration with
NITLE LET– Tag cloud– Fluid development– RSS feed
• Centerpiece: series of propositions
• Secondary media: email, blog
Propositions
Creation• Mechanism
(SurveyMonkey)
Propositions
Creation• Timing
PropositionsCreation• Language
Propositions• Details and price
Propositions
• Visualization
Propositions
• Comments
Limitations• Being played (Umich;
the Soros factor)– Monitor; self-
correction• Black Swan (Nicholas
Taleb; Asimov’s Mule)– Environmental
scanning• Risk of apophenia
– Social sifting
Lessons
• Needs to complement with other futurism methods– Delphi, scenarios, trend extrapolation
• “ “ “ “ “ venues and feedback mechanisms (surveys, blog, f2f, email)
Lessons
• Push-pull dynamic• Importance of decision criteria• Different timeline arcs demanded• Fun was had
•NITLE Liberal Education Today blog
Advantages
• Continuous, real-time feedback• Cross-sector appeal• Insight generator
Advantages• Distributed intelligence• Uncontrolled intelligence
(Bryan gets it wrong, part
323)
Further reflections• Complexity and analysis
(Jesper Krogstrup, ReBoot 2007; image from Lars Ploughmann, on Flickr)
Further reflections• Simplicity level• Pedagogical use• Campus strategic use
Next steps
• Continue operations through 20909• Add features
– Widgets– Expand RSS feeds– Inklings and NITLE sides
• Gather feedback
Next steps
• Inform our programming• Hopefully be useful to strategic
thinking• Research topics
– Market efficiency (price coherence, forecast accuracy) and biases
– Usage patterns– Platform for others’ research
Some sources• Cowgill, Wolfers, Zitzewitz, “Using
Prediction Markets to Track Information Flows: Evidence from Google” (2008)
• Iowa Prediction Markets research papers, http://www.biz.uiowa.edu/iem/archive/references.html
• Pennock, Lawrence, Giles, Nielsen, "The Power of Play: Efficiency and Forecast Accuracy in Web Market Games", Science 2001
• Nicholas Taleb, The Black Swan (2007)
NITLE Prediction Marketshttp://markets.nitle.org
National Institute for Technology and Liberal
Education(NITLE) http://nitle.org
Liberal Education Today blog http://b2e.nitle.org
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