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

1. Innovation networks and alliance managementLecture 3Small world networks& Trust

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 • Network theory and background • Business alliances as one example of network strategy • Assignment 1: analyzing an alliance network • Assignment 2: analyzing an alliance strategy • Final exam: content of lectures and slides plus literature online

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. Part 1 - Small world networks NOTE • Edge of network theory • Not fully understood yet … • … but interesting findings

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 <address of person in Boston>” • 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. 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. 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 participate • Email to <address>, 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. 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. 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. Connecting the improbable … 3 2

11. Bacon / Hauer / Connery

12. The top 20 centers in the IMDB (2004?) • Steiger, Rod (2.67) • Lee, Christopher (2.68) • Hopper, Dennis (2.69) • Sutherland, Donald (2.70) • Keitel, Harvey (2.70) • Pleasence, Donald (2.70) • von Sydow, Max (2.70) • Caine, Michael (I) (2.72) • Sheen, Martin (2.72) • Quinn, Anthony (2.72) • Heston, Charlton (2.72) • Hackman, Gene (2.72) • Connery, Sean (2.73) • Stanton, Harry Dean (2.73) • Welles, Orson (2.74) • Mitchum, Robert (2.74) • Gould, Elliott (2.74) • Plummer, Christopher (2.74) • Coburn, James (2.74) • Borgnine, Ernest (2.74) NB Bacon is at place 1049

13. “Elvis has left the building …”

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. 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. 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. 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. Combining game theory and networks – Axelrod (1980), Watts & Strogatz (1998?) • Consider a given network. • All connected actors play the repeated Prisoner’s Dilemma for some rounds • 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 • Repeat 2 and 3 until a stable state is reached. • Conclusion: to sustain cooperation, you need a short average distance, and cliquishness (“small worlds”)

19. How do these networks arise? • Perhaps through “preferential attachment” < show NetLogo simulation here> Observed networks tend to follow a power-law. They have a scale-free architecture.

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. 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. 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. Part 2 – Trust A journey into social psychology, sociology and experimental economics

24. Often, trust is a key ingredient of a tie • Alliance formation • Friendship formation • Knowledge sharing • Cooperative endeavours Trust

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. The Trust Game as the measurement vehicle

27. P P S T R R The Trust Game – general format S < P < R < T

28. The Trust Game as the measurement vehicle

29. Ego characteristics: trustors Note: results differ somewhat depending on which kind of trust you are interested in. • 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

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

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. Alter characteristics: some are trusted more • Nationality

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. The effect of payoffs on behavior

35. P P S T R R Trust Games: utility transformations

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. 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. 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