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Tom Malone - Program for the Future Dec. 8

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  • Give you a sense of the landscape of all the different things out there in the world of CI. But, as you’ll see, I think an even better metaphor for what we need to do is mapping the “genomes”, not of individual organisms like humans, but of superorganisms that exhibit collective intelligence
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    • 1.
      • Thomas W. Malone MIT Center for Collective Intelligence
      The landscape of collective intelligence: Mapping the collective intelligence “genome” Copyright © 2008 Thomas W. Malone. All rights reserved. Presentation at the Program for the Future Conference, San Jose, CA, December 8, 2008
    • 2. What is collective intelligence?
      • Collective intelligence –
        • Groups of individuals doing things collectively that seem intelligent
    • 3. New examples of collective intelligence
      • Google
      • Wikipedia
      • Digg
      • YouTube
      • InnoCentive
    • 4. The Question How can people and computers be connected so that — collectively— they act more intelligently than any person, group, or computer has ever done before?
    • 5. Mapping collective intelligence “genomes”
      • Different types of collective intelligence embody different design patterns.
      • Let’s call these design patterns “genes.”
      • For each gene (and common combinations), we can map:
        • Examples
        • Situations where useful
        • Limitations
    • 6. Collaborators
      • Rob Laubacher
        • Research scientist, MIT Center for Collective Intelligence
      • Chris Dellarocas
        • Professor, University of Maryland
    • 7. Every activity must have genes to answer four questions What How Who Why Strategy Structure (Grouping) Process (Linking) Incentives (Alignment) Staffing
    • 8. Example: Genetic structure of Linux What How Who Why Who Crowd Hierarchy (Torvalds et al) What Creates software modules Decide which modules to include How As a collaboration As a hierarchy Why For fun, reputation, etc. For fun, reputation, etc
    • 9. Who? Who does the activity? Hierarchy One or a few people from a small, preselected group Selecting modules for Linux Crowd Anyone who wants to in a large group Editing Wikipedia
    • 10. Who? Who does the activity? Hierarchy One or a few people from a small, preselected group Selecting modules for Linux Crowd Anyone who wants to in a large group Editing Wikipedia
    • 11. When is the Crowd gene useful?
      • The resources useful in solving the problem are distributed widely (or in unknown places).
      • The problem be divided into pieces such that:
        • Single individuals can do the pieces.
        • Enough individuals can be found and are (or can be) sufficiently motivated to participate.
        • The current owners of necessary information are willing to share it with the “crowd.”
        • Gaming and sabotage can be managed satisfactorily.
    • 12. Why?
      • Money
        • Direct payment
        • Help in getting future payment (e.g., advertising, learning)
      • Love
        • Opportunities to socialize
        • Direct enjoyment of activity
        • Contribution to some larger cause
        • Enables access to something else desirable (e.g., captchas)
      • Glory
        • Recognition
    • 13. When are the Why genes useful?
      • There are many complex factors here!
      • Appealing to Love and Glory, rather than Money, can often (though not always) reduce costs.
      • It is often possible to influence the direction and speed of a group’s activities by providing Money or Glory based on specific goals and deadlines.
      Failure to get motivational factors right is probably the single greatest cause of failure in collective intelligence experiments.
    • 14. Types of organizational genes What How Who Why Crowd Hierarchy
      • Money
      • Love
      • Glory
    • 15. What?
      • Create
        • E.g., create Linux software modules
      • Decide
        • E.g., decide which Linux modules to include
    • 16. How? Crowd Independent Dependent Create Collection Collaboration Decide Many-to-many network Group decision
    • 17. How? Crowd Independent Dependent Create
      • Collection
      • Contest
      Collaboration Decide Many-to-many network Group decision
    • 18. Gene: Collection
      • Create pieces that can be made mostly independently of each other:
        • Wikipedia (collection of articles)
        • Digg
        • Slashdot
        • YouTube
    • 19. InnoCentive
      • Companies post scientific problems on website
        • E.g., how to synthesize a certain chemical compound
      • Over 100,000 scientists worldwide available to solve problems
        • Retired scientists, professors, students, contract research orgs, …
      • Successful solutions get awards up to $100,000 or more
    • 20. Gene: Contest
      • A subtype of Collection where one or a few items in the collection are selected as winners
      • Examples
        • InnoCentive
        • TopCoder
        • Netflix prize
        • Yahoo Answers
        • Goldcorp challenge
        • Threadless
        • Matlab
    • 21. When is the Collection gene useful?
      • Conditions for Crowd, plus:
        • The activity can be broken into small pieces that can be done mostly independently of each other.
          • E.g., each is a complete solution to someone’s problem
        • There are ways for people to find the pieces they want in the collection.
          • E.g., the pieces are rated (in a later step) for interestingness
        • There are enough people who want this kind of thing in the first place.
    • 22. When is the Contest gene useful?
      • Conditions for Collection, plus:
        • Only one (or a few) good solutions are needed.
        • People are motivated by winning the contest
          • E.g., for recognition or financial reward.
        • When whole solutions are required to enter contest:
          • People are willing to submit a whole solution with no guarantee of reward.
          • The creator of the contest wants to control financial risk.
            • No award is given unless someone solves the problem
    • 23. How? Crowd Independent Dependent Create Collection Collaboration Decide Many-to-many network Group decision
    • 24. Gene: Collaboration
      • Create pieces with important dependencies among themselves
      • Examples
        • Wikipedia (individual articles)
        • Linux
        • Climate Collaboratorium
    • 25. Climate Collaboratorium
      • Thousands of people collectively propose and analyze plans for what humans can do about global climate change
      • Plans expressed in “radically open” computer models
      • Assumptions and conclusions expressed as issues, positions, arguments (pro & con)
      • Experts and non-experts rate plausibility and desirability
      • Goal: Find better plans than we would otherwise!
    • 26. When is the Collaboration gene useful?
      • Conditions for Crowd, plus:
        • There are benefits of doing large scale things, and there is no satisfactory way of breaking the activity into small independent pieces
          • Otherwise Collection would be better
        • There are satisfactory ways of managing the dependencies among the pieces
          • See alternatives for Decision gene
    • 27. How? Crowd Independent Dependent Create Collection Collaboration Decide Many-to-many network
      • Group decision
      • Voting
      • Consensus
      • Other
    • 28. Schaumburg Flyers
      • Minor league baseball team near Chicago
      • Through Internet voting, fans decide batting order, pitching rotation, starting line-up, and which players to trade
      • Goal: Reality Baseball with fans all over the world visiting the web site frequently
      • Result: Team had disappointing season. Some people blamed voting for bad results.
    • 29. Kasparov vs. the World
      • Players:
        • Gary Kasparov (world chess champion, 1999) vs. the rest of the world (moves decided by majority vote)
        • Kasparov heavy favorite before play began.
        • Used on-line discussions, and suggestions by 5 well-known chess experts
      • Result: Kasparov won after 62 moves in 4 months.
      • He said it was the hardest game he ever played.
    • 30. Gene: Prediction markets
      • Example: HP’s prediction markets
      • Goal: Predict future sales of HP printers
      • Approach: Participants buy and sell predictions of future sales
        • E.g., Buy a “share” predicting sales in Sept. between 1501 and 1600 units
          • If correct: get $1
          • If wrong: get $0
    • 31. HP’s prediction markets (cont.)
      • Participants were mostly from HP sales force
      • 10 sales ranges
      • Each person starts with 20 shares
      • Market open for several days.
      • Result: Better predictions than official HP sales forecasts
    • 32. Other prediction markets
      • Google – no. of users for current products, launch dates for new products
      • Microsoft – release dates of products and internal tools
      • Others – Eli Lilly drug testing, US Presidential elections, movie revenues, technological progress, . . .
    • 33. Collective prediction
      • Goal: Combine people and computers to make best possible predictions
      • Combine: Prediction markets, statistical forecasting
      • System does routine work, people make corrections when appropriate
      • Examples: Sales forecasts, competitor actions
    • 34. When are the Group Decision genes useful?
      • Everyone in the group needs to abide by the same decision.
        • What prediction will we base our plan on?
        • What features will our product have?
        • What move will we make?
    • 35. How? Crowd Independent Dependent Create Collection Collaboration Decide
      • Many-to-many network
      • Market
      • Social network
      Group decision
    • 36. Many-to-many network examples
      • Markets
        • iStockPhoto
        • Amazon Mechanical Turk
      • Social networks
        • Blogosphere
        • Google rankings
        • Amazon recommendation system
    • 37. When are the Many-to-many Network genes useful?
      • Different people in the group can make their own choices, without being bound by the choices others make.
        • What book will I buy?
        • What web page will I look at?
        • What photo will I buy?
    • 38. How? Crowd Independent Dependent Create
      • Collection
      • Contest
      Collaboration Decide
      • Many-to-many network
      • Market
      • Social network
      • Group decision
      • Voting
      • Consensus
      • Other
    • 39. How? Examples Crowd Independent Dependent Create
      • Collection
      • YouTube videos
      • Wikipedia (collection)
      • InnoCentive
      • Collaboration
      • Linux
      • Wikipedia (article)
      Decide
      • Many-to-many network
      • iStockPhoto
      • Google rankings
      • Group decision
      • Kasparov v. World
      • Prediction markets
    • 40. Types of organizational genes What How Who Why Create Decide Crowd Few
      • Money
      • Glory
      • Love
      Crowd Independent Dependent Create Collection Collaboration Decide Many-to-many network Group decision
    • 41. Summary
      • New information technologies enable dramatic new possibilities for collective intelligence.
      • To take advantage of these possibilities, we need to understand the distinct characteristics of different types of collective intelligence.
      • Mapping the “genes” for four basic questions—Who, Why, What, and How—can help us do this.
    • 42. What’s coming?
    • 43. What´s coming?
    • 44. What´s coming?
      • There is only one time in the history of each planet when its inhabitants first wire up its innumerable parts to make one large Machine. …
      • You and I are alive at this moment.…
      • --Kevin Kelly, “We Are the Web,” Wired , 2005.
    • 45. What´s coming? (cont.)
      • [H]ere at the cusp of the third millenium… humans began animating inert objects with tiny slivers of intelligence, connecting them into a global field, and linking their own minds into a single thing. . . .
      • --Kevin Kelly, “We Are the Web,” Wired , 2005.
    • 46. What´s coming? (cont.)
      • [Three thousand years from now,] this will be recognized as the largest, most complex, and most surprising event on the planet. . . .
      • It was the Beginning.
      • --Kevin Kelly, “We Are the Web,” Wired , 2005.

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