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
  • Tom Malone - Program for the Future Dec. 8

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