How to plan a heist: Challenges, models, and tactics for making inferences about social              information flow      ...
Heists!
Phases of a heist    1. The mark       A powerful, dangerous enemy who deserves to be taken down
Phases of a heist    1. The mark       A powerful, dangerous enemy who deserves to be taken down    2. The team       A gr...
Phases of a heist    1. The mark       A powerful, dangerous enemy who deserves to be taken down    2. The team       A gr...
Phases of a heist    1. The mark       A powerful, dangerous enemy who deserves to be taken down    2. The team       A gr...
The mark
The mark: Information flow   Under what conditions can we infer...    1. that information has flowed among people?    2. the...
The mark: Information flow   Under what conditions can we infer...    1. that information has flowed among people?    2. the...
The mark: Challenges   Hidden networks:   We don’t know where people get their information.   Subtle signals:   Even when ...
The team
The team     Judea Pearl     Graphical models of causality
The team     Judea Pearl     Graphical models of causality     Claude Shannon     Information theory, esp. measurement
The team     Judea Pearl     Graphical models of causality     Claude Shannon     Information theory, esp. measurement    ...
The team: Pearl’s graphical causal models      Correlation implies some kind of causation.       A≈B⇒                    A...
The team: Shannon’s mutual information      Crisp, general measure of shared information.                                 ...
The team: Zuckerberg’s mountains of data      Lots of data about lots of people      Includes text and other high-bandwidt...
The plan
The plan: Objectives   Goal: A framework (axioms and notation) for testable   theories of information flow.   When can we i...
The plan: Existence of flows   Pearl (solo): Correlation implies (some kind of) causation.   Examples    1. Plagiarism    2...
The plan: Direction of flows   Pearl: Experiments, when possible.   Zuckerberg: Action space mining   Pearl and Zuckerberg:...
The plan: Size of flows   Shannon and Zuckerberg: behavioral aggregation   Group similar actors and assume they respond to ...
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Gong info heist

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Gong info heist

  1. 1. How to plan a heist: Challenges, models, and tactics for making inferences about social information flow Abe Gong CSAAW - Nov. 2011
  2. 2. Heists!
  3. 3. Phases of a heist 1. The mark A powerful, dangerous enemy who deserves to be taken down
  4. 4. Phases of a heist 1. The mark A powerful, dangerous enemy who deserves to be taken down 2. The team A group of misfits and outcasts with diverse talents
  5. 5. Phases of a heist 1. The mark A powerful, dangerous enemy who deserves to be taken down 2. The team A group of misfits and outcasts with diverse talents 3. The plan Manipulates assumptions and information to get through the mark’s defenses
  6. 6. Phases of a heist 1. The mark A powerful, dangerous enemy who deserves to be taken down 2. The team A group of misfits and outcasts with diverse talents 3. The plan Manipulates assumptions and information to get through the mark’s defenses 4. The takedown The plan is executed and all surprises are revealed
  7. 7. The mark
  8. 8. The mark: Information flow Under what conditions can we infer... 1. that information has flowed among people? 2. the direction of information flow? 3. the quantity of information flow?
  9. 9. The mark: Information flow Under what conditions can we infer... 1. that information has flowed among people? 2. the direction of information flow? 3. the quantity of information flow? To speak with precision about [information flow] is a task not unlike coming to grips with the Holy Ghost. - V. O. Key, Public Opinion and American Democracy
  10. 10. The mark: Challenges Hidden networks: We don’t know where people get their information. Subtle signals: Even when we know where the information comes from, we don’t know how people process it. → Our ”theories” are grossly underspecified.
  11. 11. The team
  12. 12. The team Judea Pearl Graphical models of causality
  13. 13. The team Judea Pearl Graphical models of causality Claude Shannon Information theory, esp. measurement
  14. 14. The team Judea Pearl Graphical models of causality Claude Shannon Information theory, esp. measurement Mark Zuckerberg Lots and lots of data
  15. 15. The team: Pearl’s graphical causal models Correlation implies some kind of causation. A≈B⇒ A→B or B → A or C → {A, B} Graphical models let us pin down knowns and unknowns. d-separation allows us to ignore the rest of the network.
  16. 16. The team: Shannon’s mutual information Crisp, general measure of shared information. p(x,y ) I (X ; Y ) = y x p(x, y )log ( p(x)p(y ) ) Works on conditional probabilities as well. Works on individuals and ensembles → allows aggregation. Provides a nice framework for discussing social influence.
  17. 17. The team: Zuckerberg’s mountains of data Lots of data about lots of people Includes text and other high-bandwidth signals Includes time stamps, and directed links
  18. 18. The plan
  19. 19. The plan: Objectives Goal: A framework (axioms and notation) for testable theories of information flow. When can we infer... 1. that information has flowed among people? 2. the direction of information flow? 3. the quantity of information flow?
  20. 20. The plan: Existence of flows Pearl (solo): Correlation implies (some kind of) causation. Examples 1. Plagiarism 2. Newton and Leibnitz 3. Surges in google trends
  21. 21. The plan: Direction of flows Pearl: Experiments, when possible. Zuckerberg: Action space mining Pearl and Zuckerberg: Timestamps and poor man’s causality Examples 1. Canary trap 2. memetracker 3. retweets 4. Christmas tree sales
  22. 22. The plan: Size of flows Shannon and Zuckerberg: behavioral aggregation Group similar actors and assume they respond to information in the same way. → Allows us to parameterize f (). Shannon and Pearl: causal aggregation Group similar actors and assume they are receiving the same information → Makes more parts of the network measurable. Shannon, Pearl and Riolo: simulation Group similar actors so that all important info sources are measureable. Examples: 1. Convention bumps in political campaigns 2. ...?

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