Transcript Document

Innovation networks and
alliance management
Lecture 3
Small world networks
&
Trust
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Course design
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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
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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
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Part 1 - Small world networks
NOTE
Edge of network theory
Not fully understood yet …
… but interesting findings
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The small world phenomenon –
Milgram´s (1967) original study
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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”
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Milgram’s original study (2)
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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
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The small world phenomenon (cont.)
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“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.
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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”
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The Kevin Bacon game
Can be played at:
http://oracleofbacon.org
Kevin Bacon
number
Jack Nicholson:
Robert de Niro:
Rutger Hauer (NL):
Famke Janssen (NL):
Bruce Willis:
Kl.M. Brandauer (AU):
Arn. Schwarzenegger:
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(A few good men)
(Sleepers)
[Jackie Burroughs]
[Donna Goodhand]
[David Hayman]
[Robert Redford]
[Kevin Pollak]
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Connecting the improbable …
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2
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Bacon / Hauer / Connery
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The top 20 centers in the IMDB
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Steiger, Rod
(2.67)
Lee, Christopher
Hopper, Dennis
Sutherland, Donald
Keitel, Harvey
Pleasence, Donald
von Sydow, Max
Caine, Michael (I)
Sheen, Martin
Quinn, Anthony
Heston, Charlton
Hackman, Gene
Connery, Sean
Stanton, Harry Dean
Welles, Orson
Mitchum, Robert
Gould, Elliott
Plummer, Christopher
Coburn, James
Borgnine, Ernest
(2.68)
(2.69)
(2.70)
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(2.72)
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(2004?)
NB Bacon is at
place 1049
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“Elvis has left the building …”
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Strogatz and Watts
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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?
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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.
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We find small world networks in all kinds of
places…
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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
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Small world networks … so what?
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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
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Combining game theory and networks –
Axelrod (1980), Watts & Strogatz (1998?)
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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”)
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How do these networks arise?
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Perhaps through “preferential attachment”
< show NetLogo simulation here>
Observed networks tend to follow a power-law.
They have a scale-free architecture.
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“The tipping point” (Watts*)
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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
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Open problems and related issues ...
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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?
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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
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Part 2 – Trust
A journey into social psychology,
sociology and experimental economics
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Often, trust is a key ingredient of a tie
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Alliance formation
Friendship formation
Knowledge sharing
Cooperative endeavours
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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”
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The Trust Game as the measurement vehicle
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The Trust Game – general format
P
P
S
T
R
R
S<P<R<T
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The Trust Game as the measurement vehicle
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Ego characteristics: trustors
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Gentle and cooperative individuals
Blood donors, charity givers, etc
Non-economists
Religious people
Males
...
Note: results differ
somewhat depending
on which kind of
trust you are
interested in.
 Effects tend to be relatively small, or at least not
systematic
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Alter characteristics: some are trusted more
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Appearance
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Nationality
We tend to like individuals from some countries,
not others.
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Alter characteristics: some are trusted more
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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
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Alter characteristics: some are trusted more
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Nationality
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Some results on trust between countries
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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
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The effect of payoffs on behavior
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Trust Games: utility transformations
P P
S T
RR
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The effect of payoffs on behavior
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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
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Application to alliance networks
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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.
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To Do:
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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
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