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

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Innovation in networks and alliance management

Lecture 3

Small world networks & Trust

TU/e - Innovation in networks and alliance management / The Network Society 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 background2. Business alliances as one example of network

strategy3. Assignment 1: analyzing an alliance network4. Assignment 2: <to be announced>5. Final exam: content of lectures and slides plus

literature online


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

TU/e - Innovation in networks and alliance management / The Network Society

If you (still) haven’t done so …

FILL OUT THE ONLINE SURVEY!

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Two approaches to network theory

Bottom up (let’s try to understand network characteristics and arguments)as in … “Four network arguments” last weekand 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)

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Part 1 - Small world networks

NOTE- Edge of network theory- Not fully understood yet …- … but interesting findings


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”


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


The small world phenomenon (cont.)

“Small world project” has been testing this assertion (not anymore, see http://smallworld.columbia.edu)

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.


Ongoing Milgram follow-ups…

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And some critique on Milgram’s research:

http://www.uaf.edu/northern/big_world.html


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 Collaboration networks

Technological networks: Power grid Airline, road, river

networks Telephone networks Internet Autonomous systems

Florence families Karate club network

Collaboration networkFriendship network

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 …

Yeast proteininteractions

Semantic network

Language network Software network

Source: Leskovec & Faloutsos


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 the observations)

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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”


The Kevin Bacon game

Can be played at:http://oracleofbacon.org

Kevin Bacon 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]


A search for high Kevin Bacon numbers…

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


The best centers… (2009)

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(Kevin Bacon at place 507)(Rutger Hauer at place 48)


“Elvis has left the building …”


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: 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 (=zero)

Strogatz and Wats ask: what happens along the way?


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.


We find small world networks in all kinds of places…

Caenorhabditis Elegans959 cellsGenome sequenced 1998Nervous system mapped small world network

Power grid network of Western States5,000 power plants with high-voltage lines small world network


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


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”)


Small world networks are (often) “scale free” (not necessarily vice versa)

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The BIG question:How do small world / scale free networks arise?

Perhaps through “preferential attachment”

< show NetLogo simulation here>

Critique to this approach: it ignores ties created by those in the network


“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


Open problems and related issues ...

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 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?


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 / scale-free networks are: Robust to random problems/mistakes Vulnerable to selectively targeted attacks


STRUCTURE from underlying DYNAMICS


Part 2 – Trust

A journey into social psychology, sociology and experimental economics


Often, trust is a key ingredient of a tie

- Alliance formation- Friendship formation- Knowledge sharing- Cooperative endeavours- ...


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”


The Trust Game – general format

P P

S T

R R

S < P < R < T


The Trust Game as the measurement vehicle


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.


Alter characteristics: some are trusted more

Appearance

NationalityWe tend to like individuals from some countries, not others.



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



Nationality


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


The effect of payoffs on behavior


Trust Games: utility transformations

P P

S TR R


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


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.


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 network !!

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

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