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