1. The Structure of Networks with emphasis on information and social networks T-214-SINE Summer 2011 Chapter 16 Ýmir Vigfússon

2. How do you start a movement? http://www.ted.com/talks/derek_sivers_h ow_to_start_a_movement.html http://www.ted.com/talks/derek_sivers_h ow_to_start_a_movement.html

3. Experiment The hat contains 3 items ◦ 1 red, 2 blue (50% probability) ◦ 2 red, 1 blue (50% probability) Which one is it? ◦ You get to look at one item ◦ Then announce your guess BONUS! ◦ If you guess the correct color, you get a grade boost for the course of 5% (0.5/10). Let‘s do it!

4. Following the crowd We are often influenced by others ◦ Opinions ◦ Political positions ◦ Fashion ◦ Technologies to use Why do we sometimes imitate the choices of others even if information suggests otherwise? ◦ Why do you smoke? ◦ Why did you vote for a particular party? ◦ Why did you guess a particular color?

5. Following the crowd It could be rational to do so: ◦ You pick some restaurant A in an unfamiliar part of town ◦ Nobody there, but many others sitting at a restaurant B ◦ Maybe they have more information than you! ◦ You join them regardless of your own private information This is called herding, or an information cascade

6. Following the crowd Milgram, Bickman, Berkowitz in1960 ◦ x number of people stare up ◦ How many passers by will also look up? Increasing social force for conformity? Or expect those looking up to have more information? Information cascades partly explain many imitations in social settings ◦ Fashion, fads, voting for popular candidates ◦ Self-reinforcing success of books on high- seller lists

7. Herding There is a decision to be made People make the decision sequentially Each person has some private information that helps guide the decision You can‘t directly observe the private information of others ◦ Can make inferences about their private information

8. Rational reasons Informational effects ◦ Wisdom of the crowds Direct-benefit effects ◦ Different set of reasons for imitation ◦ Maybe aligning yourself with others directly benefits you  Consider the first fax machine  Operating systems  Facebook We will consider the first one today

9. Back to the experiment I lied about the grade bonus  ◦ Sorry! ◦ Why did I lie? What happened in the experiment? ◦ (or should have happened)

10. Back to the experiment First student ◦ Conveys perfect information Second student ◦ Conveys perfect information Third student ◦ If first students picked different colors  Break tie by guessing current color ◦ If first student picked same color  Say „red, red, blue“  What should he guess?  Should guess red regardless of own color!

11. Back to the experiment For all remaining students ◦ Guess what most others have been reporting ◦ An information cascade has begun Does this lead to optimal outcome? ◦ No, first two students may have both seen the minority color  1/3 * 1/3 = 1/9 chance ◦ Having a larger group does not help to fix it! Are cascades robust? ◦ Suppose student #100 shows #101 her color

12. Modeling information cascades Pr[A] where A is some event ◦ „What is the probability this is the better restaurant?“ Pr[A | B] where A and B are events ◦ „What is the probability this is the better restaurant, given the reviews I read?“ ◦ Probability of A given B.

13. Modeling information cascades Def: So:

14. Notation P[A] = prior probability of A P[A | B] = posterior probability of A given B Using Bayes‘ rule ◦ Applies when assessing the probability that a particular choice is the best one, given the event that we received certain private information Let‘s take an example

15. Bayes‘ rule, example Crime in a city involving a taxi ◦ 80% of taxis are black ◦ 20% of taxis are yellow Eyewitness testimony ◦ 80% accurate What is the probability that a taxi is yellow if the witness said it was? ◦ „True“ = actual color of vehicle ◦ „Report“ = color stated by witness Want: Pr[true = Y | report = Y]

16. Bayes‘ rule, example We can compute this: If report is yellow, two possibilities: ◦ Cab is truly yellow ◦ Cab is actually black ◦ So

17. Bayes‘ rule, example Putting it together Conclusion: ◦ Even though witness said taxi was yellow, it is equally likely to be truly yellow or black!

18. Second example Spam filtering Suppose: ◦ 40% of your e-mail is spam ◦ 1% of spam has the phrase „check this out“ ◦ 0.4% of non-spam contain the phrase Apply Bayes‘ rule!

19. Second example Numerator is easy ◦ 0.4 * 0.01 = 0.004 Denominator: So

20. Herding experiment Each student trying to maximize reward ◦ In your case, the grade... A student will guess blue if Prior probabilities Also know:

21. Herding experiment First student So if you see blue, you should guess blue

22. Herding experiment Second student ◦ Same calculations Should also pick blue if she sees blue

23. Herding experiment Third student ◦ Suppose we‘ve seen „blue, blue, red“ ◦ Want

24. Herding experiment So third student ignores own value (red) ◦ 2/3 probability that majority was in fact blue ◦ Better to guess blue! Everybody else makes the same calculation ◦ No more information being conveyed! A cascade has begun! ◦ When do cascades generally start?

25. General cascade model Group of people sequentially making decisions ◦ Choice between accepting or rejecting some option  Wear a new fashion  Buy new technology (I) State of the world ◦ Randomly in one of two states:  The option is a good idea (G)  The option is a bad idea (B)

26. General cascade model Everyone knows probability of the state ◦ World is in state G with probability p ◦ World is in state B with probability 1-p (II) Payoffs ◦ Reject: payoff of 0 ◦ Accept a good option: v g > 0 ◦ Accept a bad option: v b < 0 ◦ Expected payoff: v g p + v b (1-p) = 0 (def)

27. General cascade model (III) Signals ◦ Model the effect of private information ◦ High signal (H):  Suggests that accepting is a good idea ◦ Low signal (L):  Suggests that accepting is a bad idea ◦ Make this precise:

28. General cascade model Three main ingredients ◦ (I) State of the world ◦ (II) Payoffs ◦ (III) Signals Herding fits this framework ◦ Private information = color of draw

29. General cascade model Consider an individual ◦ Suppose he only uses private information If he gets high signal: ◦ Shifts ◦ To: What is this probability?

30. General cascade model So high signal = should accept ◦ Makes intuitive sense since option more likely to to be good than bad ◦ Analogous for low signal (should reject) What about multiple signals? ◦ Information from all the other people Can use Bayes‘ rule for this ◦ Suppose I see a sequence S with a high signals and b low ones

31. General cascade model So what does a person decide given a sequence S? ◦ Want the following facts Accept if more high signals than low ones ◦ Let‘s derive this

32. General cascade model How does this compare to p?

33. General cascade model Suppose we changed the term  Whole expression becomes p  Does this replacement make the denominator smaller or larger?

34. Herding experiment Using the model we can derive: ◦ People >3 will ignore own signal

35. Cascades - lessons Cascades can be wrong ◦ Accepting an option may be a bad idea  But if first two people get high signals – cascade of acceptances Cascades can be based on very little information ◦ People ignore private information once cascade starts Cascades are fragile ◦ Adding even a little bit more information can stop even a long-running cascade