1. 1 Innovation networks and alliance management Lecture 3 Small world networks & Trust

2. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 2 Course design Aim: knowledge about concepts in network theory, and being able to apply them, in particular in a context of innovation and alliances 1. Network theory and background 2. Business alliances as one example of network strategy 3. Assignment 1: analyzing an alliance network 4. Assignment 2: analyzing an alliance strategy 5. Final exam: content of lectures and slides plus literature online

3. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 3 Course design (detail) 1. Network theory and background - Introduction: what are they, why important … - Four basic network arguments - Small world networks and trust - Kinds of network data (collection) - Typical network concepts - Visualization and analysis 2. Business alliances as one example of network strategy - Kinds of alliances, reasons to ally - A networked economy

4. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 4 Part 1 - Small world networks NOTE - Edge of network theory - Not fully understood yet … - … but interesting findings

5. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 5 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”

6. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 6 Milgram’s original study (2) Is this really true?  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 all small scale

7. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 7 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, … Conclusion:  Low completion rate (384 out of 24,163 = 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 5, 6 or 7.

8. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 8 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”

9. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 9 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]

10. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 10 Connecting the improbable … 3 2

11. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 11 Bacon / Hauer / Connery

12. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 12 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

13. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 13 “Elvis has left the building …”

14. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 14 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 ask: what happens along the way?

15. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 15 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.

16. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 16 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

17. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 17 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, and there are reasons to believe they might be useful for innovation purposes

18. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 18 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”)

19. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 19 How do these networks arise? Perhaps through “preferential attachment” Observed networks tend to follow a power-law. They have a scale-free architecture.

20. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 20 “The tipping point” (Watts*) Consider a network in which each node determines whether or not to adopt, 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

21. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 21 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 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?

22. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 22 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

23. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 23 Part 2 – Trust A journey into social psychology, sociology and experimental economics

24. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 24 Often, trust is a key ingredient of a tie - Alliance formation - Friendship formation - Knowledge sharing - Cooperative endeavours

25. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 25 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”

26. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 26 The Trust Game as the measurement vehicle

27. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 27 The Trust Game – general format PP ST RR S < P < R < T

28. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 28 The Trust Game as the measurement vehicle

29. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 29 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.

30. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 30 Alter characteristics: some are trusted more Appearance Nationality We tend to like individuals from some countries, not others.

31. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 31 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

32. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 32 Alter characteristics: some are trusted more Nationality

33. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 33 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

34. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 34 The effect of payoffs on behavior

35. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 35 Trust Games: utility transformations PP ST RR

36. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 36 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

37. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 37 Application to alliance networks Firms (having to) trust each other. It is not so much that firms themselves tend to differ "by nature" in the extent to which they trust each other. Dealing with overcoming opportunistic behavior might be difficult, given that people are relatively poor at using the other parties incentives to predict their behavior. Dealings between firms from countries with low trust, need to invest more in safeguarding the transaction.

38. TU/e - Innovation in networks and alliance management, 0ZM05/0EE10 38 To Do: Read and comprehend the papers on small world networks and trust (see website). Think about applications of these results in the area of alliance networks