Cool Coolhunts

Peter A. Gloor MIT Center for Collective Intelligence

1

Organization Intranet/E Predict project success

World Web/Blog/ Social Networking sites

Predict movie box office success Predict political electionsPredict consumer sentimentPredict stocks

Organization Intranet/E

Unit of Interaction Research GoalsObservation Archives

World Web/Blog/ Social Predict movie box office success Networking sites Predict political elections

Predict consumer sentimentPredict stocks

Organization Intranet/E- Predict project success Mail/phone log Predict startup success

Individual Web/Blog/E- Predict personality characteristics Mail/phone log/ sociometric badges

Predict team success

2

Web and Blogs correspond to Real World

3

Predicting US Presidential Elections, Nov. 4, 2008

On Election Night Blog Buzz metric: 47% McCain 53% Obama

Corresponds to popular vote!

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Predicting Oscars and Box Offices Success

5 5

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Investment Trends

6

F/X Trading

EUR/USD returns Baseline Model R2 adjusted

“USD”/”EURO” 0.13*

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S&P Trading

S&P returns Baseline Model R2 adjusted

VIX/VIX 1st diff 0.064**

“United States”/”EURO” 0.042**

VIX/VIX 1st diff, “United States”/”EURO

0.169**

VIX: Chicago Board Options Exchange Volatility Index

(blend of options for S&P stock)= magnitude of expected changes in S&P 500 index over the next 30-day

8

Twitter Stockprediction

hope, fear, worry

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Individual Web/Blog/E Predict personality characteristics

Organization Intranet/E-Mail/phone log

Predict project success Predict startup success

Unit of Interaction Observation Archives

Research Goals

World Web/Blog/ Social Networking sites

Organization Intranet/E-Mail/phone log

Individual Web/Blog/E-Mail/phone log/ sociometric badges

Predict movie box office success Predict political electionsPredict consumer sentimentPredict stocks

Predict project success Predict startup success

Predict personality characteristics Predict team success

10

All founders of 12 Universities

11

The Digital Divide

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Individual Web/Blog/E-Mail/phone log/ sociometric badges

Predict personality characteristics Predict team success

Unit of Interaction Research GoalsObservation Archives

World Web/Blog/ Social Predict movie box office success Networking sites Predict political elections

Predict consumer sentimentPredict stocks

Organization Intranet/E- Predict project success Mail/phone log

Individual Web/Blog/E-

Predict startup success

Predict personality characteristics Mail/phone log/ sociometric badges

13

Predict team success

Creativity and Performance of Eclipse Developers

14

Individual Signals

Blue – drummer

Red – singer/leader

Green - audience

© 2011 Peter A. Gloor 15

Social Network of Nurses in PACU

The leadership communicates with each other RN 274 is by far the most between! The leadership communicates with each other

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Tom A

Thank Yllen Fillia Makedon

ou Robin Athey Tom Malone

Pascal Marmier Melina Becker Chris Miller Hans Brechbuhl Stefan Nann Gloria Busche Keiichi Nemoto Scott Cooper Tuomas Niinimäki Marius Cramer Daniel Olguin Olguin Marco DeMaggio Daniel Oster Pierre Dorsaz Maria Paasivaara Lyric Doshi Sandy Pentland Scott Dynes John Quimby Marc Egger Johannes Putzke Eric Esser Ornit Raz Kai Fischbach Renaud Richardet Hauke Führes Ken Riopelle Julia Gluesing Michael Schober Francesca Grippa Detlef Schoder George Herman Thomas Schmalberger Takashi Iba Shosta Sulonen Bill Ives Masamichi Takahashi Eric Johnson David Verrill Adriaan Jooste Christoph Von Arb Jermain Kaminsiki Ben Waber Min-Hyung Kang Andrew Westerdale Yared Kidane JoAnn Yates Reto Kleeb Wayne Yuhasz Jonas Krauss Xue Zhang http://www.ickn.org Dustin Larimer Antonio Zilli http://www.swarmcreativity.net Casper Lassenius Yan Zhao Rob Laubacher www.galaxyadvisors.com

Charles Leiserson

17© 2011 Peter A. Gloor

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15.599 Workshop in IT: Collaborative Innovation NetworksFall 2011 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.