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

2. (with an emphasis on innovation and alliances)
Course aim
knowledge about concepts in network theory, and being able to apply that knowledge
(with an emphasis on innovation and alliances)

3. The setup in some more detail
Network theory and background
Introduction: what are they, why important …
Four basic network arguments
Network properties (and a bit on trust)
Kinds of network data (collection)
Typical network concepts
Visualization and analysis

4. Two approaches to network theory
Bottom up (let’s try to understand network characteristics and arguments)
as in … “Four network arguments” last week
and the trust topic today (2nd hour)
Top down (let’s have a look at many networks, and try to deduce what is happening from the observations)
as in “small world networks” (now)

5. What kind of structures do empirical networks have
What kind of structures do empirical networks have? (often small-world, and often also scale-free)

6. 3 important network properties
Average Path Length (APL) (<l>)
Shortest path between two nodes i and j of a network, averaged across all nodes
Clustering coefficient (“cliquishness”)
The probability that a two of my friends are friends of each other
(Shape of the) degree distribution
A distribution is “scale free” when P(k), the proportion of nodes with degree k follows:

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

8. 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”

9. Milgram’s original study (2)
An urban myth?
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

10. The small world phenomenon (cont.)
“Small world project” has been testing this assertion (not anymore, see
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.

11. Ongoing Milgram follow-ups…
6.6!

12. Two approaches to network theory
Bottom up (let’s try to understand network characteristics and arguments)
as in … “Four network arguments” last week
Top down (let’s have a look at many networks, and try to deduce what is happening from what we see)

13. 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”

14. The Kevin Bacon game Can be played at: http://oracleofbacon.org
number
(data might have changed by now)
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]

15. A search for high Kevin Bacon numbers…3 2

16. Bacon / Hauer / Connery (numbers now changed a bit)

17. The best centers… (2009) (Kevin Bacon at place 507)
(Rutger Hauer at place 48)

18. “Elvis has left the building …”

19. We find small average path lengths 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

20. How weird is that?
Consider a random network: each pair of nodes is connected with a given probability p.
This is called an Erdos-Renyi network.

21. APL is small in random networks
[Slide copied from Jari_Chennai2010.pdf]

22. [Slide copied from Jari_Chennai2010.pdf]

23. But let’s move on to the second network characteristic …

25. This is how small-world networks are defined:
A short Average Path Length and
A high clustering coefficient
… and a random network does NOT lead to these small-world properties

26. Networks of the Real-world (1)
Source: Leskovec & Faloutsos
Networks of the Real-world (1)
Information networks:
World Wide Web: hyperlinks
Citation networks
Blog networks
Social networks: people + interactions
Organizational networks
Communication networks
Collaboration networks
Sexual networks
Technological networks:
Power grid
Airline, road, river networks
Telephone networks
Internet
Autonomous systems
Florence families
Karate club network
Karate: 34 members, 2 years, disagreements between instructor and club administrator, the club split into two
Collaboration network
Friendship network

27. Networks of the Real-world (2)
Source: Leskovec & Faloutsos
Networks of the Real-world (2)
Biological networks
metabolic networks
food web
neural networks
gene regulatory networks
Language networks
Semantic networks
Software networks …
Semantic network
Yeast protein
interactions
Include/dependency graph
Language network
Software network

28. 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
They seem to be useful for a lot
of things, and there are reasons
to believe they might be useful
for innovation purposes (and hence
we might want to create them)

29. Examples of interesting properties of small world networks

30. 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”)

31. Synchronizing fireflies …
<go to NetLogo>
Synchronization speed depends on small-world properties of the network
 Network characteristics important for “integrating local nodes”

32. If small-world networks are so interesting and we see them everywhere, how do they arise? (potential answer: through random rewiring of given structures)

33. Strogatz and Watts 6 billion nodes on a circle
Each connected to nearest 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: starts at 0.75, decreases to zero (actually to 1 in 6 million)
Strogatz and Wats asked: what happens along the way with APL and Clustering?

34. 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, research on the spread of diseases...
The general hint:
If networks start from relatively structured …
… and tend to progress sort of randomly …
- … then you might get small world networks a large part of the time

35. And now the third characteristic

36. Same thing … we see “scale-freeness” all over

37. … and it can’t be based on an ER-network

38. Another BIG question: How do scale free networks arise?
Potential answer: Perhaps through “preferential attachment”
< show NetLogo simulation here>
Critique to this approach:
it ignores ties created by those in the network

39. (more) open problems and related issues

40. Applications to
Spread of diseases (AIDS, foot-and-mouth disease, computer viruses)
Spread of fashions
Spread of knowledge
Especially scale-free networks are:
Robust to random problems/mistakes
Vulnerable to selectively targeted attacks

41. “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
(randomly distributed)
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,

42. And another peculiarity ...
Seems to be useful in “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 tackle a given task, for instance the question 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?

43. The general approach … understand STRUCTURE from underlying DYNAMICS
Scientists are trying to connect the structural properties …
Scale-free, small-world, locally clustered, bow-tie, hubs and authorities, communities, bipartite cores, network motifs, highly optimized tolerance, …
… to processes
(Erdos-Renyi) Random graphs, Exponential random graphs, Small-world model, Preferential attachment, Edge copying model, Community guided attachment, Forest fire models, Kronecker graphs, …

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

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

46. 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”

47. The Trust Game – general formatP S T R
S < P < R < T

48. The Trust Game as the measurement vehicle

49. 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.

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

51. 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

52. Alter characteristics: some are trusted more
Nationality

53. 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

54. The effect of payoffs on behavior

55. Trust Games: utility transformationsP S T R

56. 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

57. Application to alliance networks
Take as given that firms (having to) trust each other. Then trust research suggests:
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.

58. … and …
Some kinds of networks might be more appropriate to tackle issues of trust

59. To Do:
Read and comprehend the papers on small world networks, scale-free networks, and trust (see website).
Think about applications of these results in the area of alliance networks !!
WARNING: online survey coming up next week …