1. 1 Part 1 – Trust

2. 2 Trust is a Honda Accord As opposed to: "Existentialist trust" Reliance on...

3. 3 Trust Working definition: handing over the control of the situation to someone else, who can in principle choose to behave in an opportunistic way “the lubricant of society: it is what makes interaction run smoothly” Example: Robert Putnam’s “Bowling alone”

4. 4 The Trust Game as the measurement vehicle

5. 5 The Trust Game – general format PP ST RR S < P < R < T

6. 6 The Trust Game as the measurement vehicle

7. 7 Ego characteristics: trustors Gentle and cooperative individuals Blood donors, charity givers, etc Non-economists Religious people Males...  Effects tend to be relatively small, or at least not systematic Note: results differ somewhat depending on which kind of trust you are interested in.

8. 8 Alter characteristics: some are trusted more Appearance Nationality We tend to like individuals from some countries, not others.

9. 9 Alter characteristics: some are trusted more Appearance - we form subjective judgments easily... -... but they are not related to actual behavior - we tend to trust: +pretty faces +average faces +faces with characteristics similar to our own

10. 10 Alter characteristics: some are trusted more Nationality

11. 11 Some results on trust between countries There are large differences between countries: some are trusted, some are not There is a large degree of consensus within countries about the extent to which they trust other countries Inter-country trust is symmetrical: the Dutch do not trust Italians much, and the Italians do not trust us much

12. 12 Trust has economic value (1) trust between NL and other country contract length

13. 13 Trust has economic value (2) trust between NL and other country after-sales problems

14. 14 The effect of payoffs on behavior

15. 15 Game theory: anyone? Started scientifically with Von Neumann en Morgenstern (1944: Theory of games and economic behavior) Nash Crowe 1950: John Nash (equilibrium concept). Nobel prize for his work in 1994, together with Harsanyi en Selten.

16. 16 Trust Games: utility transformations PP ST RR

17. 17 Next: experiment let lots of people play lots of different kinds of Trust Games with each other (how do you do that?)  Experimental economics figure out what predicts behavior best: personal characteristics of ego, of alter, or game- characteristics

18. 18 The effect of payoffs on behavior Trustworthy behavior: temptation explains behavior well Trustful behavior: risk ( (35–5)/(75–5) ) explains behavior well, temptation ( (95–75)/(95–5) ) does not  People are less good at choosing their behavior in interdependent situations such as this one  Nevertheless: strong effects of the payoffs on trustful and trustworthy behavior

19. 19 Solving the trust problem Norms Changing the incentive structure (sanctions / "hostages") Repetition (cf. Robert Axelrod "The evolution of cooperation")

20. 20 Part 2 - Small world networks The way in which people are embedded in a network of connections might affect, or even completely determine, their behavior NOTE - Edge of network theory - Not fully understood yet … - … but interesting findings

21. 21 The network perspective Two firms in the same market. Which firm performs better (say, is more innovative): A or B? AB This depends on: Cost effectiveness Organizational structure Corporate culture Flexibility Supply chain management …

22. 22 The network perspective Two firms in the same market. Which firm performs better (say, more innovative): A or B? AND … POSITION IN THE NETWORK OF FIRMS AB Note Networks are one specific way of dealing with “market imperfection”

23. 23 Example network (source: Borgatti)

24. 24 Example network: a food “chain”

25. 25 Example network: terrorists (source: Borgatti)

26. 26 Kinds of network arguments (from: Burt) Closure competitive advantage stems from managing risk; closed networks enhance communication and enforcement of sanctions Brokerage competitive advantage stems from managing information access and control; networks that span structural holes provide the better opportunities Contagion information is not a clear guide to behavior, so observable behavior of others is taken as a signal of proper behavior. [1] contagion by cohesion: you imitate the behavior of those you are connected to [2] contagion by equivalence: you imitate the behavior of those others who are in a structurally equivalent position Prominence information is not a clear guide to behavior, so the prominence of an individual or group is taken as a signal of quality

27. 27 The small world phenomenon – Milgram´s (1967) original study Milgram sent packages to a couple hundred people in Nebraska and Kansas. Aim was “get this package to ” Rule: only send this package to someone whom you know on a first name basis. Try to make the chain as short as possible. Result: average length of chain is only six “six degrees of separation”

28. 28 Milgram’s original study (2) Is this really true?  It seems that Milgram used only part of the data, actually mainly the ones supporting his claim  Many packages did not end up at the Boston address  Follow up studies often small scale

29. 29 The small world phenomenon (cont.) “Small world project” is (was?) testing this assertion as we speak (http://smallworld.columbia.edu), you might still be able to participatehttp://smallworld.columbia.edu Email to, otherwise same rules. Addresses were American college professor, Indian technology consultant, Estonian archival inspector, … Conclusions thusfar:  Low completion rate (around 1.5%)  Succesful chains more often through professional ties  Succesful chains more often through weak ties (weak ties mentioned about 10% more often)  Chain size typically 5, 6 or 7.

30. 30 The Kevin Bacon experiment – Tjaden (+/-1996) Actors = actors Ties = “has played in a movie with” Small world networks: - short average distance between pairs … - … but relatively high “cliquishness”

31. 31 The Kevin Bacon game Can be played at: http://oracleofbacon.org Kevin Bacon number Jack Nicholson:1 (A few good men) Robert de Niro:1 (Sleepers) Rutger Hauer (NL):2 [Jackie Burroughs] Famke Janssen (NL):2 [Donna Goodhand] Bruce Willis:2 [David Hayman] Kl.M. Brandauer (AU):2 [Robert Redford] Arn. Schwarzenegger:2 [Kevin Pollak]

32. 32 Connecting the improbable … 3 2

33. 33 Bacon / Hauer / Connery

34. 34 The top 20 centers in the IMDB (2004?) 1. Steiger, Rod (2.67) Steiger, Rod 2. Lee, Christopher (2.68) Lee, Christopher 3. Hopper, Dennis (2.69) Hopper, Dennis 4. Sutherland, Donald (2.70) Sutherland, Donald 5. Keitel, Harvey (2.70) Keitel, Harvey 6. Pleasence, Donald (2.70) Pleasence, Donald 7. von Sydow, Max (2.70) von Sydow, Max 8. Caine, Michael (I) (2.72) Caine, Michael (I) 9. Sheen, Martin (2.72) Sheen, Martin 10. Quinn, Anthony (2.72) Quinn, Anthony 11. Heston, Charlton (2.72) Heston, Charlton 12. Hackman, Gene (2.72) Hackman, Gene 13. Connery, Sean(2.73) Connery, Sean 14. Stanton, Harry Dean(2.73) Stanton, Harry Dean 15. Welles, Orson(2.74) Welles, Orson 16. Mitchum, Robert(2.74) Mitchum, Robert 17. Gould, Elliott(2.74) Gould, Elliott 18. Plummer, Christopher (2.74) Plummer, Christopher 19. Coburn, James (2.74) Coburn, James 20. Borgnine, Ernest (2.74) Borgnine, Ernest NB Bacon is at place 1049

35. 35 “Elvis has left the building …”

36. 36 Strogatz and Watts 6 billion nodes on a circle Each connected to 1,000 neighbors Start rewiring links randomly Calculate “average path length” and “clustering” as the network starts to change Network changes from structured to random APL: starts at 3 million, decreases to 4 (!) Clustering: probability that two nodes linked to a common node will be linked to each other (degree of overlap) Clustering: starts at 0.75, decreases to 1 in 6 million Strogatz and Wats asked: what happens along the way?

37. 37 Strogatz and Watts (2) “We move in tight circles yet we are all bound together by remarkably short chains” (Strogatz, 2003)  Implications for, for instance, AIDS research.

38. 38 We find small world networks in all kinds of places… Caenorhabditis Elegans 959 cells Genome sequenced 1998 Nervous system mapped  small world network Power grid network of Western States 5,000 power plants with high-voltage lines  small world network

39. 39 Small world networks … so what? You see it a lot around us: for instance in road maps, food chains, electric power grids, metabolite processing networks, neural networks, telephone call graphs and social influence networks  may be useful to study them We (can try to) create them: see Hyves, openBC, etc They seem to be useful for a lot of things, or at least pop up often, but how do they emerge?

40. 40 Combining game theory and networks – Axelrod (1980), Watts & Strogatz (1998 ? ) 1. Consider a given network. 2. All connected actors play the repeated Prisoner’s Dilemma for some rounds 3. After a given number of rounds, the strategies “reproduce” in the sense that the proportion of the more succesful strategies increases in the network, whereas the less succesful strategies decrease or die 4. Repeat 2 and 3 until a stable state is reached. 5. Conclusion: to sustain cooperation, you need a short average distance, and cliquishness (“small worlds”)

41. 41 How do these networks arise? Perhaps through “preferential attachment” Observed networks tend to follow a power-law. They have a scale-free architecture.

42. 42 “The tipping point” (Watts*) Consider a network in which each node determines whether or not to adopt (for instance the latest fashion), based on what his direct connections do. Nodes have different thresholds to adopt (random networks) Question: when do you get cascades of adoption? Answer: two phase transitions or tipping points:  in sparse networks no cascades  as networks get more dense, a sudden jump in the likelihood of cascades  as networks get more dense, the likelihood of cascades decreases and suddenly goes to zero * Watts, D.J. (2002) A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences USA 99, 5766-5771

43. 43 Open problems and related issues... Decentralized computing  Imagine a ring of 1,000 lightbulbs  Each is on or off  Each bulb looks at three neighbors left and right... ... and decides somehow whether or not to switch to on or off. Question: how can we design a rule so that the network can solve a given task, for instance whether most of the lightbulbs were initially on or off. - As yet unsolved. Best rule gives 82 % correct. - But: on small-world networks, a simple majority rule gets 88% correct. How can local knowledge be used to solve global problems?

44. 44 Open problems and related issues (2) Applications to Spread of diseases (AIDS, foot-and-mouth disease, computer viruses) Spread of fashions Spread of knowledge Small-world networks are: Robust to random problems/mistakes Vulnerable to selectively targeted attacks

45. 45 Application to trust People (have to or want to) trust each other. Whether or not this will work out, is dependent on the context in which the interaction occurs  this can be given a more concrete meaning: it is dependent on in which kind of network the Trust Game is being played! Dealing with overcoming opportunistic behavior is difficult, given that people are relatively poor at using the other parties incentives to predict their behavior. Perhaps it is better to make sure that the network you are in, deters opportunistic behavior. cf. eBay: reputation

46. 46 Possible assignment For the programmers: have a look at the literature on "games in networks". Run a simulation where people are playing Trust Games on a network. Try to determine, for instance, how network characteristics affect behavior in Trust Games. Take one other "soft topics" such as trust (regret? envy? guilt?). Scan the literature for implementations of that particular topic in terms of abstract games. Explain and summarize the findings.