1
Collective Intelligence 2014
June 10-12, 2014
The Ray and Maria Stata Center at MIT
Program
Proceedings are available on ArXiv.org: the link can be found at the top of the conference website, http://collective.mech.northwestern.edu/. Plenaries are being videotaped and included in the proceedings.
Sponsored by
2
June 10, Tuesday
5:30-7:30 PM: Registration and Opening Reception, R&D Commons (32-G401), 4th floor
June 11, Wednesday Unless otherwise noted, all sessions will be held in Kirsch Auditorium, 32-123, 1st floor
8:00 – 9:00 AM: Registration and Breakfast, outside 32-123, Charles M. Vest Student Street, 1st floor
9:00 – 9:05 AM: Welcome (Duncan Watts)
9:05 – 10:45 AM: Plenary Session 1 – Collective Decisions (Chair: Duncan Watts)
• Jens Krause Collective Intelligence in Fish and Humans • Naomi Leonard Leadership and Collective Decision-making • Radhika Nagpal Towards Collective A.I. • Stephen Pratt Collective Cognition by Insect Societies
10:45 – 11:15 AM: Break, Charles M. Vest Student Street, 1st floor
11:15 – 12:30 PM: Parallel Session 1
Track A: 32-123, 1st floor (Chair: Henry Farrell)
• The Mythical Swing Voter David Rothschild, Sharad Goel, Andrew Gelman, Doug Rivers
• Corporate Prediction Markets: Evidence from Google, Ford, and Firm X Bo Cowgill, Eric Zitzewtiz
• Theory of Mind Predicts Collective Intelligence David Engel, Anita Woolley, Lisa Jing, Chris Chabris, Thomas Malone
Track B: 32-141, 1st floor (Chair: Jens Krause)
• Collective Response to Perturbations in a Data-Driven Fish School Model Daniel Calovi, Paul Schuhmacher, Ugo Lopez, Clément Sire, Guy Theraulaz
• Collective Learning and the Emergence of Producer-Scrounger Dynamics Noam Miller, Ariana Strandburg-Peshkin, Iain Couzin
• Receptive-field-like Models Accurately Predict Individual Zebrafish Behavior in a Group Roy Harpaz, Elad Schneidman
Track C: 32-155, 1st floor (Chair: David Gibson)
• Ethnic Diversity Deflates Price Bubbles Sheen Levine, Evan Apfelbaum, Mark Bernard, Valerie Bartlett, David Stark, Edward Zajac
• Leveraging Diversity in Intercultural Creative Teams Julia Haines
• Leadership in Moving Human Groups • Margarete Boos, Johannes Pritz, Simon Lange, Michael Belz
12:30 – 2:00 PM: Lunch and Poster Session 1, Charles M. Vest Student Street, 1st floor
3
2:00 – 3:45 PM: Plenary Session 2 – Collective Cognition (Chair: Anita Woolley)
• Emily Falk Neural Predictors of Individual and Large Scale Behavior Change • Linda Argote Transactive Memory Systems and Collective Intelligence? • Niki Kittur Collaborative cognition: Augmenting thinking with crowds and computation • Betsy Sparrow Creative and Critical Thinking Online
3:45 – 4:15 PM: Break, Charles M. Vest Student Street, 1st floor
4:15 – 6 PM: Plenary Session 3 – IARPA Contest (Chair: Rajiv Sethi)
• Jason Matheny IARPA's Forecasting Tournaments • Barbara Mellers What makes forecasters perform well? (Lyle Ungar to present) • Lyle Ungar How to Aggregate Opinions for Crowd-based Forecasting • Van Parunak Aggregating Interpreted Signals: Approaches and lessons learned
7:00 – 9:00 PM: Dinner, Sala de Puerto Rico, Stratton Student Center, 2nd floor,
June 12, Thursday Unless otherwise noted, all sessions will be held in Kirsch Auditorium, 32-123, 1st floor
8:00 – 9:00 AM: Registration and Breakfast, outside 32-123, Charles M. Vest Student Street, 1st floor
9:00 – 10:45 AM: Plenary Session 4 – Collective Governance (Chair: Matt Salganik)
• Henry Farrell The Intelligence of Democratic Disagreement • Helene Landemore The Politics of Collective Intelligence • Beth Noveck Bringing Agile, Empirical Research into How We Govern • Karim Lakhani The Crowd as an Innovation Partner
10:45 – 11:15 AM: Break, Charles M. Vest Student Street, 1st floor
11:15 – 12:30 PM: Parallel Session 2
Track A: 32-123, 1st floor (Chair: Betsy Sparrow)
• Analytical Reasoning Task Reveals Limits of Social Learning in Networks Iyad Rahwan, Dmytro Krasnoshtan, Azim Shariff, Jean-François Bonnefon, Ethan Bernstein.
• Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces Jesse Shore, Ethan Bernstein, David Lazer
• Collective Search by Group Infotaxis Ehud Karpas, Elad Schneidman
4
Track B: 32-141, 1st floor (Chair: Emily Falk)
• Architectures of Virtual Decision-Making: The emergence of gender discrimination on a crowdfunding website Jason Radford
• Laboratories of Oligarchy? How the Iron Law Extends to Peer Production Aaron Shaw, Benjamin Mako Hill
• Human Communication Systems Evolve by Cultural Selection Nicolas Fay, Monica Tamariz, T Mark Ellison, Dale Barr
Track C: 32-155, 1st floor (Chair: Kate Starbird)
• When is a Crowd Wise? Clintin Davis-Stober, David Budescu, Jason Dana, Stephen Broomell
• Not as Smart as We Think: A study of collective intelligence in virtual groups Jordan Barlow, Alan Dennis
• When None of us Perform Better than all of us Together: The role of analogical decision rules in groups Nicoleta Meslec, Petru Curseu, Marius Meeus, Oana Fodor (Iederan)
12:30 – 2:00 PM: Lunch and Poster Session 2, Charles M. Vest Student Street, 1st floor
2:00 – 3:00 PM: Ignite Session (Chair: Beth Noveck)
• Ted Smith, City of Louisville. Citizen Science: Harvesting Data from the Commons • David Moore, Participatory Politics Foundation. AskThem • Chris Jones, DoD Collaboration Facilitators. Collaboration Facilitation • Molly W. Rubenstein, Artisans Asylum. Makerspaces: Crowdsourcing Creativity, Innovation, &
Entrepreneurship • Susan Leopold, Consumer Reports. Crowd Signal • Tara Montgomery, Consumer Reports. Value our Health
3:00 – 3:15 PM: Break
3:15 – 5:00 PM: Plenary Session 5 – Collective Problems (Chair: Paul Resnick)
• David Gibson Necessary Decisions from Vicissitudinous Talk During the Cuban Missile Crisis • David Mendonca Collective Behavior in Queueing Networks: The case of post-disaster debris removal
operations • Kate Starbird Crowdwork during Crisis: Designing for emergent collaborations • Luis von Ahn Duolingo: Free language education through crowdsourcing
5:00 – 5:15 PM: Concluding Remarks and Announcement of CI 2015 Chairs
5
Wednesday Posters (Information on hanging posters at the back of the program under general information)
Day
Post
er#
Paper Title Authors 1 1 Effect of Group Means on the Probability of Consensus Yoshiko Arima, Kyoto Gakuen University 1 2 Information Spread in a Connected World Alon Sela, Tel Aviv University; Hila Oved, Tel Aviv University; Irad Ben-Gal,
Tel Aviv University 1 3 Crowdsourcing for Participatory Democracies: Efficient
Elicitation of Social Choice Functions David Lee, Stanford University; Ashish Goel, Stanford University; Helene Landemore, UC Berkeley and University of Tampere; Tanja Aitamurto, Yale University
1 4 Obscuring the Task: Story and Theme as Motivators in an Emotion Annotation Game
Nathan Prestopnik, Ithaca College; Jasy Liew, Syracuse University
1 5 Ethnic Diversity Deflates Price Bubbles Sheen Levine, Columbia University; Evan Apfelbaum, MIT; Mark Bernard, Goethe University Frankfurt; Valerie Bartlett, Texas A&M; David Stark, Columbia University; Edward Zajac, Northwestern University
1 6 Collective Intelligence versus Team Intelligence Andreas Aulinger, Steinbeis University Berlin; Laura Miller, Steinbeis University Berlin
1 7 Collective Intelligence in Citizen Science - A Study of Performers and Talkers
Ramine Tinati, University Of Southampton; Elena Simperl, University Of Southampton; Markus Luczak-Roesch, University of Southampton; Max Van Kleek, University of Southampton; Nigel Shadbolt, University of Southampton
1 8 Human Communication Systems Evolve by Cultural Selection Nicolas Fay, University Western Australia; Monica Tamariz, University of Edinburgh; T Mark Ellison, University of Western Australia; Dale Barr, University of Glasgow
1 9 Market-Based Collective Intelligence in Enterprise 2.0 Decision Making
Henner Gimpel, KIT; Florian Teschner, KIT
1 10 The Role of Organizational Design in Local Open Government
Thomas Gegenhuber, JKU Linz; Stefan Etzelstorfer, JKU Linz; Philipp Allerstorfer, JKU Linz; Wendy Cukier, Ryerson University; Jaigris Hodson, Ryerson University
1 11 Analytical reasoning task reveals limits of social learning in networks
Iyad Rahwan, Masdar Institute of Science and Technology; Dmytro Krasnoshtan, Masdar Institute of Science and Technology; Azim Shariff, University of Oregon; Jean-François Bonnefon, CNRS and Universite de Toulouse
1 12 Crowdsourcing, Dialogue, and Fieldwork - Experiment on creating a vitalization plan in Yokohama bay area using integrated collective intelligence methods -
Masamichi Takahashi, Fuji Xerox Co. Ltd.; Minoru Mitsui, Fuji Xerox Co. Ltd.; Mihoko Wakui, Fuji Xerox Co. Ltd.; Ryoji Horita, Fuji Xerox Co. Ltd.
1 13 Rapid Foresight: a New Tool for Collective Vision Design Pavel Luksha, Moscow School of Management SK 1 14 Limits of Collective Judgement in Contested Domains: The
Case of Conflicting Arguments Edmond Awad, Masdar Institute of Science and Technology; Richard Booth, University of Luxembourg; Fernando Tohme, Artificial Intelligence Research and Development Lab (LIDIA), Universidad Nacion; Iyad Rahwan, Masdar Institute of Science and Technology
1 15 Surfacing Collective Intelligence with Implications for Interface Design in Massive Open Online Courses
Anna Zawilska, University of Oxford; Marina Jirotka, Department of Computer Science, University of Oxford; Mark Hartswood, Department of Computer Science, University of Oxford
1 16 GEM: A Model for Collective Ideation Dorit Geifman, University of Haifa; Hila Koren, University of Haifa 1 17 COLLAGREE: A Faciliator-mediated Large-scale Consensus
Support System Takayuki Ito, Nagoya Institute of Technology; Yuma Imi, Nagoya Institute of Technology; Takanori Ito, Nagoya Institute of Technology; Eizo Hideshima, Nagoya Institute of Technology
1 18 Crowdsourcing on Challenge.gov: Aligning Expectations between Public Managers and Citizens
Kevin Desouza, Arizona State University; Rashmi Krishnamurthy, Arizona State University
1 19 Consensus Decision-Making of Bayesian Agents on a Network
Simon Leblanc, Princeton University; Colin Twomey, Princeton University
1 20 Nuts4Nuts: geospatial information from Wikipedia Cristian Consonni, Fondazione Bruno Kessler 1 21 Not as Smart as We Think: A Study of Collective Intelligence
in Virtual Groups Jordan Barlow, Indiana University; Alan Dennis, Indiana University
1 22 Financial Incentives and the Performance of Crowds: A Closer Look
Mohammadmahdi Moqri, University of Florida; Brent Kitchens, University of Florida; Anuj Kumar, University of Florida
1 23 Error and attack tolerance of collective problem solving: The DARPA Shredder Challenge
Nicolas Stefanovitch, Masdar Institute of Science and Technology; Aamena Alshamsi, Masdar Institute of Science and Technology; Manuel Cebrian, National Information and Communications Technology Australia; Iyad Rahwan, Masdar Institute of Science and Technology
1 24 Receptive-field-like models accurately predict individual zebrafish behavior in a group
Roy Harpaz, Weizmann Institute of Science; Elad Schneidman, Weizman Institute of Science
6
1 25 Leadership in Moving Human Groups Margarete Boos, University of Göttingen; Johannes Pritz, University of Göttingen, Germany; Simon Lange, University of Göttingen; Michael Belz, University of Göttingen
1 26 On Manipulation in Prediction Markets When Participants Influence Outcomes Directly
Mithun Chakraborty, Washington Univ. in St. Louis; Sanmay Das, Washington University in St. Louis
1 27 Collaborative Bandits for Modelling Collective Behaviour Kristiaan Pelckmans, UU/IT 1 28 ‘Personality’, Social Strategy, and the Regulation of Public
Goods Investment: A Behavioural Ecological Perspective on Variation in Human Economic Behaviour
Joanna Bryson, University of Bath; Karolina Sylwester, University of Bath; James Mitchell, University of Bath; Simon Powers, University of Lausanne
1 29 Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Richard Absalom, The Hague; Marcus Luczak-Rosch, University of Southampton ; Dap Hartmann, Delft University of Technology; Aske Plaat, Tilburg University
1 30 Decision accuracy in complex environments is often maximized by small group sizes
Albert Kao, Princeton University; Iain Couzin, Princeton University
1 31 Robustness as A Property of Networks Supporting Change: An Example from Artificial Gene Networks
Yifei Wang, University of Bath; Joanna Bryson, University of Bath; Nicholas Priest, University of Bath
1 32 A Computational Model of Crowds for Collective Intelligence John Prpić, SFU - Beedie School of Busines; Piper Jackson, Simon Fraser University - Modelling of Complex Social Systems Program; Thai Nguyen, Simon Fraser University - Computing Science
1 33 WeDo: Exploring Participatory, End-To-End Collective Action
Haoqi Zhang, Northwestern University; Andés Monroy-Hernández, Microsoft Research; Aaron Shaw, Northwestern University; Sean Munson, University of Washington; Liz Gerber, Northwestern University; Benjamin Mako Hill, University of Washington; Peter Kinnaird, Carnegie Mellon University; Shelly Farnham, Microsoft Research; Patrick Minder, University of Zurich
1 34 Laboratories of Oligarchy? How the Iron Law Extends to Peer Production
Aaron Shaw, Northwestern University; Benjamin Mako Hill, University of Washington
1 35 Inferring Social Structure and Dominance Relationships Between Rhesus macaques using RFID Tracking Data
Hanuma Teja Maddali, Georgia Inst. of Technology; Michael Novitzky, Georgia Institute of Technology; Brian Hrolenok, Georgia Institute of Technology; Daniel Walker, Georgia Institute of Technology; Tucker Balch, Georgia Institute of Technology; Kim Wallen, Emory University
1 36 A User Interface for Outline Co-creation Mark Sales, Stormweaver Programming 1 37 It's Contagious: Modeling Information Transmission in
Collective Groups Amanda Chicoli, University of Maryland; Derek Paley, University of Maryland
1 38 Theory of Mind Predicts Collective Intelligence David Engel, MIT; Anita Woolley, Carnegie Mellon; Lisa Jing, MIT; Chris Chabris, Union College; Thomas Malone, MIT
1 39 Capturing collective conflict dynamics with sparse social circuits
Edward Lee , Wisconsin Institute for Discov; Bryan Daniels, University of Wisconsin–Madiso; Jessica Flack, University of Wisconsin–Madison; David Krakauer, University of Wisconsin–Madison
1 40 Proposal for a Survey and Interviews of Participants at the 2014 CI Conference
John (Jock) McClellan, Quinebaug Valley Community College
1 41 Network Ties And Performance Of Competing Teams Jose Uribe, Columbia Business School; Dan Wang, Columbia Business School 1 42 SciCast: Collective Forecasting of Innovation Charles Twardy, ; Robin Hanson, George Mason University; Kathryn Laskey,
George Mason University 1 43 Coupled Models of Cognition and Action: Behavioral
Phenotypes in the Collective Stephanie Goldfarb, HRL Laboratories; Vincent De Sapio, HRL Laboratories; Rajan Bhattacharyya, HRL Laboratories
1 44 Composing and Analyzing Crowdsourcing Workflows Panos Ipeirotis, NYU; Thomas Malone, MIT; Greg Little, Digital Monk 1 45 Modeling dynamic network evolution in the context of
strategic change Kenneth Foster, Rotman School of Management
1 46 Interval Elicitation of Forecasts in a Prediction Market Reveals Lack of Anchoring “Bias”
Kenneth Olson, George Mason University; Kathryn Laskey, George Mason University; Charles Twardy,
1 47 Transparency and Coordination in Peer Production Laura Dabbish, Carnegie Mellon University; Colleen Stuart, Johns Hopkins University; Jason Tsay, Carnegie Mellon University; Jim Herbsleb, Carnegie Mellon University
1 48 Collective intelligence in journalism: Extended search, blended responsibility, and ruptured ideals
Tanja Aitamurto, UC Berkeley
1 49 Representations Evolve to Minimise Production Perplexity T. Mark Ellison, University Western Australia; Nicolas Fay, University Western Australia
1 50 The Mythical Swing Voter David Rothschild, Microsoft Research; Sharad Goel, Microsoft Research; Andrew Gelman, Columbia University; Doug Rivers, Stanford University
1 51 Discovering New Sentiments from the Social Web Juan Galan-Paez, University of Sevilla; Joaquín Borrego-Díaz, University of Seville
1 52 Meme creation and sharing processes: individuals shaping the masses
Ian Miller, University of Toronto; Gerald Cupchik, University of Toronto
1 53 From Local Ecological Knowledge to Collective Ecological Intelligence: Monitoring Water Wells in Indonesia
Marc Böhlen, University at Buffalo; Ilya Maharika, Universitas Islam Indonesia
7
Thursday Posters
Day
Pos
ter#
Paper Title Authors 2 1 Toward a Local Perspective on Online Collaboration Hani Safadi, McGill University; Samer Faraj, McGill University 2 2 Positional change analysis reveals phases in bicycle pelotons Hugh Trenchard, Independent researcher 2 3 The Hazards of Interaction: When Isolation Benefits
Performance Sheen Levine, Columbia University; Michael Prietula, Emory University
2 4 Open Collaboration for Innovation: Principles and Performance
Sheen Levine, Columbia University; Michael Prietula, Emory University
2 5 Wisdom of crowds in practice Juho Salminen, LUT 2 6 Social Technologies for Developing Collective Intelligence in
Networked Society Aelita Skarzauskiene, Mykolas Romeris university; Birute Pitrenaite-Zileniene, Mykolas Romeris University; Edgaras Leichteris, Knowledge Economic Forum; Zaneta Paunksniene, Mykolas Romeris University; Monika Maciuliene, Mykolas Romeris University
2 7 Architectures of Virtual Decision-Making: The Emergence of Gender Discrimination on a Crowdfunding Website
Jason Radford, University of Chicago
2 8 Less-is-more in a 5-star rating system: an experimental study of human combined decisions in a multi-armed bandit problem
Wataru Toyokawa, Hokkaido University; Hye-rin Kim, Hokkaido University; Tatsuya Kameda, Hokkaido University
2 9 Collective intelligence in Massive Online Dialogues Taraneh Khazaei, University of Western Ontario; Lu Xiao, University of Western Ontario
2 10 When none of us perform better than all of us together: the role of analogical decision rules in groups
Nicoleta Meslec, Tilburg University; Petru Curseu, Tilburg University; Marius Meeus, Tilburg University; Oana Fodor (Iederan), Babes-Bolyai University
2 11 When is a crowd wise? Clintin Davis-Stober, University of Missouri; David Budescu, Fordham University; Jason Dana, Yale University; Stephen Broomell, Carnegie Mellon University
2 12 Crowdsourcing the Policy Cycle John Prpić, SFU - Beedie School of Busines; Araz Taeihagh, University of New South Wales - City Futures Research Centre; James Melton, Central Michigan University - College of Business Administration
2 13 Timebanking with a Smartphone Application Kyungsik (Keith) Han, Pennsylvania State University; Patrick Shih, Pennsylvania State University; Victoria Bellotti, Palo Alto Research Center; John Carroll, Pennsylvania State University
2 14 The Negative Effect of Feedback on Performance in Crowd Labor Tournaments
Tim Straub, Karlsruhe Institute of Technol; Henner Gimpel, KIT; Florian Teschner, KIT
2 15 The Informative Herd: why humans and other animals imitate more when conditions are adverse
Alfonso Perez-Escudero, Cajal Institute (CSIC); Gonzalo de Polavieja, Cajal Institute (CSIC)
2 16 Nowcasting the Bitcoin Market with Twitter Signals Jermain Kaminski, MIT Media Lab & Witten/Herdecke University; Peter Gloor, MIT Center for Collective Intelligence
2 17 Wisdom Of The Confident: Using Social Interactions To Eliminate The Bias In Wisdom Of The Crowds
Gonzalo De Polavieja, Instituto Cajal (CSIC); Gabriel Madirolas, Instituto Cajal (CSIC)
2 18 Transient Leadership and Collective Cell Movement in Early Diverged Multicellular Animals
Mircea Davidescu, Princeton University; Iain Couzin, Princeton University
2 19 An Idea Filtering Method for Open Innovations Ana Cristina Garcia, UFF; Mark Klein, MIT 2 20 Similar, Yet Diverse: A Recommender System Pinar Ozturk, Stevens Institute of Technolog; Yue Han, Stevens Institute of
Technology 2 21 Collective response to perturbations in a data-driven fish
school model Daniel Calovi, Université Paul Sabatier; Paul Schuhmacher, Université Paul Sabatier, CRCA; Ugo Lopez, Université Paul Sabatier, CRCA; Clément Sire, Université Paul Sabatier, LPT; Guy Theraulaz, Université Paul Sabatier
2 22 Collective Innovation: The Known and the Unknown Kai Wang, Stevens Institute of Technology 2 23 The Differing Effects of Intelligence in Collaborative Tasks Jordan Barlow, Indiana University; Alan Dennis, Indiana University 2 24 Understanding the “Few that Matter” in Online Social
Production Communities: The Case of Wikipedia Chrysanthos Dellarocas, Boston University; Mihai Grigore, ETH Zurich; Juliana Sutanto, ETH Zurich; Bernadetta Tarigan, ETH Zurich
2 25 Information processing and the evolution of unresponsiveness in collective systems
Colin Torney, University of Exeter; Simon Levin, Princeton University; Iain Couzin, Princeton University; Tommaso Lorenzi, Université Pierre et Marie Curie
2 26 Collective learning and the emergence of producer-scrounger dynamics
Noam Miller, Princeton University; Ariana Strandburg-Peshkin, Princeton University; Iain Couzin, Princeton University
2 27 Collective Search by Group Infotaxis Ehud Karpas, Weizmann Institute of Science; Elad Schneidman, Weizman Institute of Science
2 28 Collective health policy making in the Catalan Health System: applying Health Consensus to priority setting and policy monitoring
Tino Marti, Onsanity; Josep Maria Monguet, Universitat Politecnica de Catalunya; Alex Trejo, Onsanity; Joan Escarrabill, Hospital Clinic Barcelona; Carles Constante, Department of Health. Generalitat Catalunya
8
2 29 Identifying Expertise to Extract the Wisdom of Crowds David Budescu, Fordham University; Eva Chen, University of Pennsylvania 2 30 Translating Sound Adjectives by Collectively Teaching
Abstract Representations Mark Cartwright, Northwestern University; Bryan Pardo, Northwestern University
2 31 Corporate Prediction Markets: Evidence from Google, Ford, and Firm X
Bo Cowgill, UC Berkeley; Eric Zitzewtiz, Dartmouth College Economics Department
2 32 Using Prediction Markets to Incentivize and Measure Collective Knowledge Production
Thomas Maillart, UC Berkeley; Didier Sornette, ETH Zurich
2 33 Participation in Micro-task Crowdsourcing Markets as Work and Leisure: The Impact of Motivation and Micro-time Structuring
Ling Jiang, City University of Hong Kong; Christian Wagner, City University of Hong Kong
2 34 Selective Pressure for the Divergence in Decision and Experienced Utility
Paul Rauwolf, University of Bath; Joanna Bryson, University of Bath
2 35 Home Is Where the Up-Votes Are: Behavior Changes in Response to Feedback in Social Media
Sanmay Das, Washington University in St. Louis; Allen Lavoie, Washington University in St. Louis
2 36 lynks – introducing a new breed of plug and play interactive network visuals
Ulrich Mans, Leiden University; Gideon Shimshon, Leiden University; Eelke Heemskerk, University of Amsterdam
2 37 Leveraging Diversity in Intercultural Creative Teams Julia Haines, University of California, Irvine 2 38 Dispersion and Line Formation in Artificial Swarm
Intelligence Donghwa Jeong, Case Western Reserve Univ.; Kiju Lee, Case Western Reserve Univ
2 39 Popularity and Performance: A Large-Scale Study Peter Krafft, MIT; Julia Zheng, MIT; Erez Shmueli, MIT; Nicolas Della Penna, Australian National University; Josh Tenenbaum, MIT; Sandy Pentland, MIT
2 40 Eventiful: Crowdsourcing Local News Reporting Elena Agapie, Microsoft Research; Andés Monroy-Hernández, Microsoft Research
2 41 Criticality and information flow in an adaptive system Bryan Daniels, University of Wisconsin–Madiso; Jessica Flack, University of Wisconsin–Madison; David Krakauer, University of Wisconsin–Madison
2 42 Social Learning in Team Decision Making Joong Bum Rhim, MIT; Vivek Goyal, Boston University 2 43 Organizational Impacts of Crowdsourcing: What Happens
with "Not Invented Here" Ideas? Natalia Levina, New York University; Anne Laure Fayard, NYU Poly; Emmanouil Gkeredakis, Warwick Business School
2 44 Lessons Learned from an Experiment in Crowdsourcing Complex Citizen Engineering Tasks with Amazon Mechanical Turk
Matthew Staffelbach, Notre Dame; Peter Sempolinski, Notre Dame; David Hachen, Notre Dame; Ahsan Kareem, Notre Dame; Tracy Kijewski-Correa, Notre Dame; Douglas Thain, Notre Dame; Daniel Wei, Notre Dame; Greg Madey, Notre Dame
2 45 Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces
Jesse Shore, Boston University; Ethan Bernstein, Harvard Business School; David Lazer, Northeastern University
2 46 Social diffusion and global drift in adaptive social networks Hiroki Sayama, Binghamton University 2 47 Trade-based Asset Model using Dynamic Junction Tree for
Combinatorial Prediction Markets Wei Sun, George Mason University; Kathryn Laskey, George Mason University; Charles Twardy, Robin Hanson, George Mason University; Brandon Goldfedder, Gold Brand Software
2 48 New measures for evaluating creativity in scientific publications
Simona Doboli, Hofstra University; Fanshu Zhao, Hofstra University; Alex Doboli, Stony Brook University
2 49 Navigating Robot Swarms Using Collective Intelligence Learned from Golden Shiner Fish
Grace Gao, University of Illinois at Urbana-Champaign
2 50 To Crowdfund Or Not Liz Gerber, Northwestern University; Julie Hui, Northwestern University 2 51 Collective Innovation in Open Source Hardware Harris Kyriakou, Stevens Institute of Technology; Jeffrey V. Nickerson, Stevens
Institute of Technology 2 52 Information Transfer in Swarms with Leaders Louis Rossi, University of Delaware
9
GENERAL INFORMATION
Welcome to MIT and to Cambridge in conjunction with the 2014 Collective Intelligence Conference.
Registration/Information Desk
During the welcome reception on June 10th, the conference desk will be staffed for registration and information services in the R&D Commons (32-G401) on the 4th floor of the Ray and Maria Stata Center. On June 11-12th, the desk will be located outside of Kirsch Auditorium (32-123) on the 1st floor of the Ray and Maria Stata Center. The desk will be available during session hours, beginning at 8:00AM daily.
Welcome Reception
The welcome reception will be held in the R&D Commons (32-G401), on the 4th floor of the Ray and Maria Stata Center on June 10th from 5:30 to 7:30PM. Please check in at the registration desk prior to attending the reception. Badge
Please be sure to wear your badge for all conference sessions and events.
Session Locations
Sessions will be held in 32-123, 32-141 and 32-155 on the 1st floor of the Ray and Maria Stata Center. Food and beverages are not allowed in the session rooms. For safety reasons, we request that you be seated during sessions and do not stand or place bags in aisles or exits.
Meal Locations
Breakfast, breaks and lunch will be served in the Charles M. Vest Student Street located outside of the session rooms on the first floor of the Ray and Maria Stata Center. The Conference dinner will be held in Sala de Puerto Rico in the Stratton Student Center. If you indicated that you are attending the dinner, a ticket was included behind your badge.
Instructions to Speakers
Please check in with the AV technician at the front of the room during the break prior to your scheduled session.
Wireless Internet
MIT offers complimentary wireless access to guests. For wireless connections, visitors need to make sure their wireless card is on and enabled. Select MIT GUEST as the wireless network option. A connection will occur without registration. If you experience any difficulty connecting, you may contact the MIT IS&T Help Desk during regular business hours (8:00AM to 6:00PM) at 617-253-1101.
Parking
There is no conference parking available on the MIT campus. The closest public parking is at located at the 4 and 7 Cambridge Center garages, which are close to the Marriott Hotel. There is also a very small visitor lot on the corner or Massachusetts Avenue and Vassar Street.
Poster Sessions
Posters will be displayed either on Day 1 (Wednesday) or Day 2 (Thursday) located throughout the Charles M. Vest Student Street located on the first floor of the Ray and Maria Stata Center. A map is included following the poster listing. Each poster board, which is 4 feet by 4 feet, will have a number labeling it. Poster presenters are encouraged to hang their posters on the board with their corresponding number before the beginning of the morning session (between 8:00 and 9:00am), and take their posters down at the end of the afternoon break. Please use the provided pushpins only. Please do not mount your
10
posters onto foam core. The formal poster session will be over lunch on Wednesday and Thursday, and posters will also be available for viewing during breaks.
Public Transportation
There are a number of trains and buses, which provide public transportation to the Cambridge/Boston area. The No. 1 bus stops at MIT’s main entrance (77 Massachusetts Avenue) and provides service to Harvard Square and downtown Boston. The CT1 bus departs from Central Square and also stops at MIT’s main entrance. The CT2 bus departs from Kendall Square and stops at the corner of Vassar Street and Massachusetts Avenue. One-way bus fare is $2.00 (CharlieTicket/cash-on-board); $1.50/ (pre-purchased CharlieCard).
The MBTA Red Line provides train service to the MIT area via stops at Central Square (on Massachusetts Avenue) and Kendall/MIT (on Main Street). Both stops are approximately a 10-minute walk to the conference site. Subway fare is $2.50 (CharlieTicket/cash-on-board); $2.00 (pre-purchased CharlieCard). Most public transportation systems run between the hours of 5:30AM and 12:30AM, with some extended hours. You may find out more on scheduling and purchasing a Charlie Card, at the MBTA web site: http://www.mbta.com.
Taxis
Taxis are available during the day in Kendall Square outside the Marriott Hotel or at 77 Massachusetts Avenue (MIT's main entrance). You may also call Ambassador Cab at 617-429-1100 or Checker Cab at 617-497-9000.
Emergency Services
For emergency services while on campus, dial 617-253-1212 or 100 from any campus phone. MIT Campus Police will answer your call.
Smoking Policy
In accordance with the City of Cambridge's smoking ordinance, smoking is prohibited in all academic, administrative and service buildings on campus.
32-‐124
32-‐144
32-‐123
32-‐155
GATES
Dreyfoos
Colle
ctiv
e In
telli
gen
ce 2
014
Fo
od
1
9-2
7
11-1
8
Food
Ele
vato
rs t
o
4th
flo
or E
Ju
ne 1
0th
Welc
om
e R
ece
pti
on
an
d
reg
istr
ati
on
are
lo
cate
d o
n t
he
fou
rth
flo
or
Ray
and M
aria
Sta
ta C
ente
r, 1
st flo
or
map
Reg
istr
ati
on
, B
reaks
an
d L
un
ch
Ju
ne 1
1 -
12
Po
ster
loca
tio
ns
an
d n
um
bers
S
ess
ion
1:
Ju
ne 1
1th
S
ess
ion
2:
Jun
e 1
2th
Ple
nary
Tra
ck A
Collec&ve In
telligence: June
10-‐12, 2014
!"#$$"
!"#$$"
!"#$$"
%&'!%(#")
!"#$$"
&*$!!"#$$"
&*)$#!"
!"#$$"
)&+%&#'
!"#$$"
,(#"-&.'
*&/.
!"#$$"
!"#$$"
!"#$$"
0#(&'%&+
!"#$$"
1&-/-$(
2&!!&#
!"#$$"
&-0&.+
!"#$$"
3#&.4-/.5!"#$$"
1#$$.5!"#$$"
6#(!!
!"#$$"
!"&"$
!"#$$"
3#(."
%/.'!(#
2&!!&#
!"#$$"
!"#$$"
(!0(#.
!"#$$"
&*)$#!"
'&.3(#")!"
!"#$$"
$.'/6(""!"
%&'!%(#")
!"#$$"
&*$!!"#$$"
&*)$#!"
!"#$$"
)&+%&#'
!"#$$"
6&#-$"(.
,(#"-&.'
!"#$$"
!"#$$"
!"#$$"
%&+
*&/.
!"#$$"
*/'70
-(64
6(..$6"(#
!"#$$"
6)&#-(""$8!
%&+
'(645!"9
!"#$$"
!"#$$"
-&.'!'(%.$
!"#$$"
!"#$$"
!"#$$"
6#(!!
!"#$$"
!"&"$
!"#$$"
3#(."
!"#$$"
%/.'!(#
*&!!&6):!$""!
&2$.:$
&-0&.+
(!0(#.
!"#$$"
,(#"-&.'
&*)$#!"
!*&#"
!"#$$"
2/--&1$
!"
&*$!
*&!!&6)
:!$""! &2$.:$
0#(&'%&+
!"#$
%&'&(
)*+
,-."
/#0
".1
&22
3&,.
2
4-521
-%3&
,.2
67''7-
%83&
,.2
9,:&
%23&
,.2
;&<"
.258=
7"'9
!"%%
753&
,.25
*->&
%
?@A8
/-5
5BCD
"B8E
&2
F?G8
E&2
F?G8
C%%"
>8E&2
6"%9
-''E&
2
C1$"
.528*
2BH4
@?I8E
&2
J-)K
-.9
*2B8E
&2
/-7%
E&2
6."5
("E&
2
=&.9
E&2
J".1
-%%
L"5
28C%%
">8E
&2
E&K"
''3&
,.2
M.-)
8L-)
N-#O
8P-.
.)C5
2.&8!
,.Q
!
!
:
:
:
4-52(
-2"
;"579
"%28E
&2M-
.-("
J-)K
-.98
E&2
C%%"
>
?RS8
/-5
58CD"
T7572
&.8E
&2
*2"7%
<."%
%".8*
2-97,
1
/.!"/":"$
%)/"$)$&'
)("$-
*&##/(""
;<<5*$*(#/&-5'#/2$
'#&,$#
-&0(#&"(#+
U%7D"
.572)
8:-.
OE"
8/".
797"%
/..
#$!/'$.6$
"$6).(-(1+5!=:&#$
1&#&1$
1&#&1$
>56&*0#/'1$56$."$#
?.$@<A
/.!"/":"$
0#(&'
B0
?GG8
!*
RGG8
!*
VGG8
!*
@833
@GG8
!*WG
G8!*
XGG8
!*
YGG8
!*
?833
Y833
R833
T&'D&
8P'9(
=&.9
8P'9(
L$72
-O".
83&''
"("
*"%7&
.8J&,
5"
M.-)
8J&,
5"
4-52(
-2"
:7".
#"E-
<&.-
2&.)
J&1
<".(
8P'9(P,
5$8P
'9(
J-)9
"%8/
"1&.
7-'E7<
.-.)
0&..-
%#"8
P'9(
L.7(
$28P
.&2$
".5
L7%9
8!,%
%"'
0.")
Q,58P
'9(
3&1
Z2&%
E-<&
.-2&
.7"5
/-#
'-,.7%
8P'9(
*'&-
%E-
<&.-
2&.7"
5
M,((
"%$"
71E-
<&.-
2&.)
4M[M
849,
#-27&
%3"
%2".
=-7.#
$7'98
P'9(
*'&-
%E-
<&.-
2&.)
/#F
-7.8P
'9(P.
&K%8
P'9(
/-#
'-,.7%
8P'9(
L7''7
-18;
B807#O
5&%
3&("
%".-
27&%8
:'-%
2
3)#'&
2.&%
:-.5
&%58E
-<&.
-2&.
)
:.-2
2*#
$&&'
L-'O
"./
"1&.
7-'
M.""
%8P'
9(
L$72
-O".
8P'9(
C',1
%78:&
&'
4-521
-%E-
<&.-
2&.7"
5
4-52
3-1
Z,5
;-'Z$
8E-%
9-,8
P'9(
P7&'&
()
;&("
.58P
'9(
;&2#$
P'9(
L7"5
%".8P
'9(/,9
98P'
9(
J"-'2
$8*"
.D7#"
5
*,QQ&
'O8P'
9(/\!
8:."
55
/,#
O'")8P
'9(
!-%(
*'&-
%8P'
9(J".1
-%%8
P'9(
3&&'7
%(8!
&K".
C'<-
%)8M
-.-(
"
/\!
8/,5
",1
J7''8P
'9(
49("
.2&%
=.-%
#758P
722".
/-(
%"28E
-<
;-%9
&18J
-''
/#3
&.1
7#O8J
-''
P">'"
)8J-''
3$-Z
"'6.
"5("
8C,9
72&.7,
1*2.-
22&%8
*2,9
"%283
"%2"
.
9,8:
&%28C
2$'"2
7#83"
%2".
;&#O
K"''83
-("
L"5
28M-.
-("
M.""
%
3-..8
\%9&
&.8!
"%%75
8=-#
7'72)
P-O"
.8J&,
5"
=-7.#
$7'98
P'9(
/-#
'-,.7%
8P'9(
J-''
:'-5
1-8
=,57&
%8[
*#7"%
#"83
"%2"
.
9,8:
&%28C
2$'"2
7#8M)
1%-
57,1
J&,5
"
P'9(
9,:&
%28!
"%%75
83&,
.25
J7($
8T&'2
-("
;"5"
-.#$
8E-<
&.-2
&.)
*,Z"
.#&%
9,#27
%(M"
%".-
2&.8!
"528=
-#7'72
)
;-)8-
%98/
-.7-
*2-2
-83"
%2".
]"57(
".8*
Z&.25
8[=7
2%"5
583"%
2".
:'-5
1-8
*#7"%
#"8[
=,57&
%83"
%2".
6&#$
3"%2
".
L&&
9
J-)9
"%
/,%
.&"
M&&9
-'"
P"1
75
L-'#
&22
C',1
%7J&
,5"5
N&$%
5&%
C2$'"
27#83
"%2"
.
;7%-
'978P
'9(
*#7"%
#"583
&1Z'"
>P.
-7%8[
83&(
%727D"
6&#$
8\%527
2,2"
:7#&
K".8\
%5272
,2"8
Q&.
/#M
&D".
%8\%
5272,
2"Q&
.8P.-
7%8;
"5"-
.#$
E"-.
%7%(
8-%9
8/"1
&.)
:0*\
M-2
"58!
&K".
0.")
Q&&5
8!&K
".
/"9
7-8E-
<3&
1Z'"
>
=-.7<
&.^8/
-5""
$J-
''
BC;>
$%
!.
.DE
.D;
.%B;
.%BE
%@E
;B
F@
.;B&
FC
FF
.F
.;B
.G
.;<
FE
F; >
;;;<
;@
@;
@G@>
@D
@@ G
@F@C
@B
EF
;E
;E&
CEB
;CDF
DBD>
.%EE
.%;>
.%;B
.%;<
.%E;
.%;D
.%;F
.%;@
.%;E
%FD
%E<
%@F
%@@
%@;
.$ED
$@@$@F
$@G
$FC
BFBE
BB
$;D
$E@
$ED
$;>$;C
$;G
$@C
.$C<
$DD
$E
$;
$D;$F<
$D@
$DE
%;
%;;
%;D
D@
;
BF
E
;F
D<
%E
%>
%D
%;B %F
%D@
%D@&
%;@
.%E<
>&
.$;C
.$FG
.$F>.$FC FB
@E
%@D
.;B6
%D@0
B6
>BRV
0RV
M
$;F
%D;6
Mem
orial D
rive
Mem
orial D
rive
Key Loca&o
ns:
Registra9o
n, ope
ning re
cep9
on, all sessions and
poster lun
ches will be he
ld in th
e Ra
y and Maria Stata Cen
ter, Bldg 32, 1
st floo
r and
4th floo
r Dinn
er: Sala de
Pue
rto Rico, StraH
on Stude
nt Cen
ter: W20, 2
nd floo
r Co
nferen
ce hotels: Le Meridien and Hy
aH
Le M
erid
ien!
Kend
all M
BTA!
To H
yatt!
