3 is not a crowd, it

1
3 is not a crowd, its an anecdote

• Jon Crowcroft,
• C2B(I)D15
• http//www.cl.cam.ac.uk/jac22

2
Vertices and Edges
Person, thing Relationship, encounter, expression, earnings
Place, time Profile, History, State Direction, Strength, Speed, temperature, value (),
3
We know vertex is person whats edge?

• Relationship
• Kin, friend, colleague, ship-in-the-night
• External evidence (registry, dna, HR)
• Co-lo
• Shared air, drink/food, touch?
• In same record (video/photo/ticket?)
• Communicated
• Messaged, liked/mention/comment etc
• Wrote a letter

4
3 things

• Sampling the Crowd?
• Lost in the Crowd?
• Same old Crowd?

5
Samples

• Selection bias
• Has an Interweb Gadget
• WEIRD
• Recruitment smallworld re-enforcement
• Demographic baseline
• Reward (and punishment)
• Halo effects, Focussing Illusion
• Other behavioural economic fails

6
Ground Truth of Sample

• Takes old fashioned social science
• Diaries/Interviews etc, but beware
• Representativeness Bias
• Priors, sample size
• Regression
• Retrievability
• Imaginability
• exponentials, long range dependence etc
• General Anchoring effects
• http//psiexp.ss.uci.edu/research/teaching/Tversky
  _Kahneman_1974.pdf

7
Sample Skew

• Sometimes, youre not allowed
• FluPhone H1N1 Epidemic
• Self report symptoms
• Phone app tracks location/encounters
• Build spatial/temporal model of SIR.
• IRB ruled
• No location (privacy risk)
• No kids (informed consent hard)
• Nulls most the experiment

8
Hiding in Plain Sight

• Or seeing the wood for the trees
• Humans not good at this, but s/w v. good
• Re-identification from power of 4
• External data sets
• Yellowcab driver and celeb passengers
• Massachusetts health records
• Fb loan advice
• Other stuff already out there.be aware
• Humans are wired to infer stuff from 3
• But society used to be wired to hide
• Stuff that crowdsourcing may reveal
• But humans are very bad at some stuff

9
Hiding in plain sight

• Humans are bad at getting
• Small World (6 degrees)
• Exponentials (2x grains rice on next square)
• Large deviations (black swan)
• Makes informed consent nonsense
• Violate principal of least astonishment
• How to train the public?
• C.f. Thinking fast, and slow
• Cooling off period Examples?

10
Crowds from both sides now (looking at)

• Reproduceable, not repeatable
• Science and Business want evidence
• To make better decisions in future
• So model from data has to have persistence
• Hence, needs to be checked
• Need to assume no observer bias
• as well as sample problems discussed before
• So Big Data isnt necessarily big
• Its lots of small, representative, samples
• Over time and yet the world changes

11
Psychohistory

• Knowledge youre being observed can
• Lead to change of behaviour
• Well known in stock market
• Also google flu search term 2
• Also in Crowd funding
• We built tool to predict campaign success
• If investors all use that tool, I predict
• It wont work as well
• Oh, outcome was roughly what you expect?

12
4 arguments for TV elim

• conditions us to accept someone elses authority
• facilitates consolidated power through the
  colonization of experience.
• physically conditions us for authoritative rule
• inherent biases of TV
• https//en.wikipedia.org/wiki/Four_Arguments_for_t
  he_Elimination_of_Television

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Conclusions, Discussion

• 4 arguments (for the elimination of crowd) - I
  hope you agree these are not independent
• You really can't summarize complex information
• Nielson-gt
• Interweb-gtTV
• Crowding
• Live long and prosper