Transcript Document

Part 1 – Trust
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Trust is a Honda Accord
As opposed to:
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"Existentialist trust"
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Reliance on ...
2
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”
3
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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Trust has economic value (1)
contract
length
trust between NL and other country
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Trust has economic value (2)
after-sales
problems
trust between NL and other country
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The effect of payoffs on behavior
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Game theory: anyone?

Started scientifically with Von Neumann en
Morgenstern
(1944: Theory of games
and economic behavior)
•1950: John Nash (equilibrium concept). Nobel prize
for his work in 1994, together with Harsanyi en
Selten.
Nash
Crowe
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Trust Games: utility transformations
P P
S T
RR
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Next: experiment
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let lots of people play lots of different kinds of
Trust Games with each other
(how do you do that?)  Experimental economics
figure out what predicts behavior best: personal
characteristics of ego, of alter, or gamecharacteristics
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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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Solving the trust problem
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Norms
Changing the incentive structure (sanctions /
"hostages")
Repetition
(cf. Robert Axelrod "The evolution of cooperation")
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Part 2 - Small world networks
The way in which people are embedded in a
network of connections might affect, or even
completely determine, their behavior
NOTE
- Edge of network theory
- Not fully understood yet …
- … but interesting findings
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The network perspective
Two firms in the same market.
Which firm performs better (say, is more innovative):
A or B?
A
B
This depends on:
•Cost effectiveness
•Organizational structure
•Corporate culture
•Flexibility
•Supply chain management
•…
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The network perspective
Two firms in the same market.
Which firm performs better (say, more innovative): A or B?
A
B
Note
AND … POSITION IN THE NETWORK OF FIRMS
Networks are one specific way of dealing with
“market imperfection”
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Example network
(source: Borgatti)
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Example network: a food “chain”
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Example network: terrorists
(source: Borgatti)
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Kinds of network arguments
(from: Burt)
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Closure competitive advantage stems from managing risk; closed
networks enhance communication and enforcement of sanctions
Brokerage competitive advantage stems from managing
information access and control; networks that span structural holes
provide the better opportunities
Contagion information is not a clear guide to behavior, so
observable behavior of others is taken as a signal of proper
behavior.
[1] contagion by cohesion: you imitate the behavior of those
you are connected to
[2] contagion by equivalence: you imitate the behavior of those
others who are in a structurally equivalent position
Prominence information is not a clear guide to behavior, so the
prominence of an individual or group is taken as a signal of quality
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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?
It seems that 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 often 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, …
Conclusions thusfar:
 Low completion rate (around 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 typically 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:
1
1
2
2
2
2
2
(A few good men)
(Sleepers)
[Jackie Burroughs]
[Donna Goodhand]
[David Hayman]
[Robert Redford]
[Kevin Pollak]
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Connecting the improbable …
3
2
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Bacon / Hauer / Connery
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The top 20 centers in the IMDB
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
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)
(2.70)
(2.70)
(2.70)
(2.72)
(2.72)
(2.72)
(2.72)
(2.72)
(2.73)
(2.73)
(2.74)
(2.74)
(2.74)
(2.74)
(2.74)
(2.74)
(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 asked: 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, or at least pop up often,
but how do they emerge?
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Combining game theory and networks –
Axelrod (1980), Watts & Strogatz (1998?)
1.
2.
3.
4.
5.
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 (for instance the latest
fashion), 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
solve 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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Application to trust
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People (have to or want to) trust each other.
Whether or not this will work out, is dependent on
the context in which the interaction occurs  this
can be given a more concrete meaning: it is
dependent on in which kind of network the Trust
Game is being played!
Dealing with overcoming opportunistic behavior is
difficult, given that people are relatively poor at
using the other parties incentives to predict their
behavior. Perhaps it is better to make sure that
the network you are in, deters opportunistic
behavior.
cf. eBay: reputation
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Possible assignment
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For the programmers: have a look at the literature
on "games in networks".
Run a simulation where people are playing Trust
Games on a network. Try to determine, for
instance, how network characteristics affect
behavior in Trust Games.
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Take one other "soft topics" such as trust (regret?
envy? guilt?). Scan the literature for
implementations of that particular topic in terms
of abstract games. Explain and summarize the
findings.
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