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Designing for the crowd


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During most of it's history, software design has basically tackled the problem of creating tools that enable single users to perform a set of tasks in order to achieve a specific, predetermined goal. Though potentially large numbers of users could be using the same system, the process of getting to a result is reached through tasks performed by individual users. The advent of crowdsourcing (using the broad definition of the word), has so far mostly split large tasks in smaller pieces that can be then distributed among a big number of people, something also known as micro-tasking. Succesful examples like Amazon Mecahical Turk abound, but in almost all of the cases, the nature of the goal is directly de-composed into the tasks, meaning that the whole will never be greater than the sum of the parts. I suggest that by taking an interdisciplinary approach and learning from game design, system thinking, genetic programming, market economy and behavioural sciences a new breed of systems could emerge that would tap in the power of the crowd in interesting new ways. By designing systems built on rule-based models and allowing for emergent behaviours, the exploration of much more complex and less deterministic problems could be possible, including many of the world most pressing challenges. I propose to analyze a few preliminary examples and suggest ideas for further exploration.

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Designing for the crowd

  1. 1. Designing for the Crowd Designing apps that tap on the dynamic interactions of the crowdNicolás di
  2. 2. Sum ofthe parts
  3. 3. 1 single person with enough time• Mapping• Categorizing• Describing
  4. 4. Greater than the sum?
  5. 5. Collective sum of time Emergent Dynamics
  6. 6. Prediction MarketsHollywood Stock Exchange“In 2006 HSX.comcorrectly predicted32 out of 39 big-category Oscarnominees and 7 outof 8 top categorywinners”
  7. 7. Forecasting presidential elections • All but 1 candidate predicted between 1868 and 1940 • Iowa Electronic Market (IEM) (University of North Carolina)
  8. 8. Wisdom of the Crowd• Jack Treynor’s Jelly Bean experiment• Michael Mauboussin (2007) - 73 Columbia Business School students:• Guesses: 250 - 4,100• Average error: 700 (62%)• Actual number: 1,116.• Average guess:1,151 — just 3% off
  9. 9. Collaborative Design
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  11. 11.
  12. 12. Competitions-
  13. 13. Alternate Reality Games World Without Oil
  14. 14. Evolutionary Crowd
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  16. 16. Crowd-Apps Design = Game Design + Market Economy +Evolutionary Computing + Behavioral Science
  17. 17. Ideas?Nicolás di