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How to plan a heist: Challenges, models, andtactics for making inferences about social
information flow
Abe GongCSAAW - Nov. 2011
Heists!
Phases of a heist
1. The markA powerful, dangerous enemy who deserves to be taken down
2. The teamA group of misfits and outcasts with diverse talents
3. The planManipulates assumptions and information to get through themark’s defenses
4. The takedownThe plan is executed and all surprises are revealed
Phases of a heist
1. The markA powerful, dangerous enemy who deserves to be taken down
2. The teamA group of misfits and outcasts with diverse talents
3. The planManipulates assumptions and information to get through themark’s defenses
4. The takedownThe plan is executed and all surprises are revealed
Phases of a heist
1. The markA powerful, dangerous enemy who deserves to be taken down
2. The teamA group of misfits and outcasts with diverse talents
3. The planManipulates assumptions and information to get through themark’s defenses
4. The takedownThe plan is executed and all surprises are revealed
Phases of a heist
1. The markA powerful, dangerous enemy who deserves to be taken down
2. The teamA group of misfits and outcasts with diverse talents
3. The planManipulates assumptions and information to get through themark’s defenses
4. The takedownThe plan is executed and all surprises are revealed
The mark
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 tasknot unlike coming to grips with the Holy Ghost.
- V. O. Key, Public Opinion and American Democracy
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 tasknot unlike coming to grips with the Holy Ghost.
- V. O. Key, Public Opinion and American Democracy
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’tknow how people process it.
→ Our ”theories” are grossly underspecified.
The team
The team
I Judea PearlGraphical models of causality
I Claude ShannonInformation theory, esp. measurement
I Mark ZuckerbergLots and lots of data
The team
I Judea PearlGraphical models of causality
I Claude ShannonInformation theory, esp. measurement
I Mark ZuckerbergLots and lots of data
The team
I Judea PearlGraphical models of causality
I Claude ShannonInformation theory, esp. measurement
I Mark ZuckerbergLots and lots of data
The team: Pearl’s graphical causal models
I Correlation implies some kind of causation.A ≈ B ⇒
A→ Bor B → Aor C → {A,B}
I Graphical models let us pin down knowns and unknowns.
I d-separation allows us to ignore the rest of the network.
The team: Shannon’s mutual information
I Crisp, general measure of shared information.I (X ; Y ) =
∑y
∑x p(x , y)log( p(x ,y)
p(x)p(y))
I Works on conditional probabilities as well.
I Works on individuals and ensembles → allows aggregation.
I Provides a nice framework for discussing social influence.
The team: Zuckerberg’s mountains of data
I Lots of data about lots of people
I Includes text and other high-bandwidth signals
I Includes time stamps, and directed links
The plan
The plan: Objectives
Goal: A framework (axioms and notation) for testabletheories 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?
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
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
The plan: Size of flows
Shannon and Zuckerberg: behavioral aggregationGroup similar actors and assume they respond to information inthe same way.→ Allows us to parameterize f ().
Shannon and Pearl: causal aggregationGroup similar actors and assume they are receiving the sameinformation→ Makes more parts of the network measurable.
Shannon, Pearl and Riolo: simulationGroup similar actors so that all important info sources aremeasureable.Examples:
1. Convention bumps in political campaigns
2. ...?