3 is not a crowd, it’s an anecdote

Jon Crowcroft, C2B(I)D’15

http://www.cl.cam.ac.uk/~jac22

Vertices and Edges

Person, thing Relationship, encounter, expression, earnings

Place, timeProfile, History, State

Direction, Strength, Speed, temperature, value ($),

We know vertex is person – what’s 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…

3 things

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

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…

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

Sample Skew

Sometimes, you’re 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… … …

Hiding in Plain Sight

Or seeing the wood for the trees Human’s 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

Human’s 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

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…?

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 isn’t necessarily big

Its lots of small, representative, samples Over time… … … and yet the world changes

Psychohistory

Knowledge you’re 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 won’t work as well Oh, outcome was roughly what you expect

4 arguments for TV elim…

1. conditions us to accept someone else’s authority

2. facilitates consolidated power through the colonization of experience.

3. physically conditions us for authoritative rule

4. inherent biases of TVhttps://en.wikipedia.org/wiki/

Four_Arguments_for_the_Elimination_of_Television

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->$ Interweb->TV Crowding

Live long and prosper