Real Option Framework to Value
Network Architecture
and Network Neutrality
Mark Gaynor
Boston University
Saint Louis University (summer 2009)
mgaynor@bu.edu
Workshop III: Beyond Internet MRA:
Networks of Networks
Institute for Pure and Applied Mathematics (IPAM)
© Mark Gaynor 11/05/08 - IPAM
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Motivation
 What is the value of:
 end-2-end architecture
 Efficient centralized Vs flexible distributed architectures
 Network Neutrality
 It depends on market uncertainty!
 It is important to know what users want
 If you know and others don’t, then you can win big if market uncertainty is high
© Mark Gaynor 11/05/08 - IPAM
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Talk Outline






Real Options Approach
Market Uncertainty
Network Architecture
A simple real options model
What is network neutrality
Model applied to network neutrality
 Link the excepted ARPU to market uncertainty
© Mark Gaynor 11/05/08 - IPAM
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Traditional networks metrics
 What are traditional methodologies good for
 If I know what users want
 What if we don’t understand what users want?
 What services, with what features?
 How much will they pay for them?
 Does uncertainty imply more value to network neutrality
 Real options value uncertainty and choice
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Why “real options”
 Options theory models uncertainty, flexibility and choice
 Options illustrate how to limit risk, without capping gain –
a good design principle
 Real options extends the theory of options to non-financial
assets
 Building IT infrastructure, modularity in computer systems,
modularity and staged development of IT standards
 Real options illustrate when flexibility is worth more than
efficiency
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Options and Real Options
 Real Options
 Pharmaceutical development,
power plants,…..
Option pay-out
Strike price
0
Stock Profit
 Pay something today, to buy a
stock in the future, at today’s
price
 This limits risk, without
capping gain, unlike owning
the stock
Option Pay-out
 Classic call option
Excess risk of
stock
ownership
 Limit loss without capping gain
© Mark Gaynor 11/05/08 - IPAM
Profit/Loss from
Stock sale
Stock Price
6
Management Structure and
Experimentation
 What is Experimentation
 Features in devices and services
 Distributed management structure is easy to
experiment with
 Don’t need permission
 Don’t need to change network infrastructure
 A bad experiment only affects a few users
 Centralized management makes experimentation hard
 Need to change network infrastructure
 Need to ask permission
 Many users affected with bad experiment
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Distributed Vs Centralized
Wireless Music Service
Centralized management
People, Equipment and Data
centralized managed
Efficient use of resources
Know users and usage
Consistency of environment
Hard to change
In-flexible for new applications
Distributed management
P,E,D locally managed
Easy to experiment with
User control
Small scale non-intrusive
experimentation possible
Poor use of resources
Hard to know users and what they
are doing
Internet
Alice’s
favorite
song
Bob’s
favorite
song
Bob’s Home
PC
Alice’s Home
PC
Alice
Bob
Centralized
Alice
Centralized Music
Server PC
Distributed
(end-2-end)
Alice’s
favorite
song
© Mark Gaynor 11/05/08 - IPAM
Internet
Bob
Bob’s
favorite
song
Alice’s Home
PC
Bob’s Home
PC
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Market Uncertainty
 Market uncertainty is the inability to predict what
uses want
 How to measure it: Tushman and MacCormack
 Tushman: how well have historical predictions about
market been
 MacCormack: Emergence of dominate design
 Gaynor and Bradner: measure changes in standards,
stable feature sets
 The value of the best of many experiments is
related to the market uncertainty
© Mark Gaynor 11/05/08 - IPAM
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Best of Many Attempts to Meet the Market
(When is experimentation worthwhile?
When market uncertainty is high)
 Each attempt is an implementation with a particular feature set
 Extreme order statistics – Value of best of many attempts
 High market uncertainty => someone wins big (maybe you), low market
uncertainty => no big winners because its easy to satisfy users
Normal Density Function
0.1
0.05
E(X) = V = Mean
Experiment Value
3
U©10Mark Gaynor 11/05/08 - IPAM
2
1
0
-1
-2
0
U100
Experiment Value
3
34.13 34.13
%
13.59 %
13.59
% 2%
2% %
0.15
S.D=1
S.D=.25
2
S.D=1
0.2
1
0.25
0
0.3
-3
Probability
0.35
High market uncertainty implies
value in giving users more choices
-1
0.4
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-2
Q(x) is number of
s.d. s.d.’s from the mean
of the best experiment
-3
Ux = V + s.d * Q(x)
Probability
0.45
Normal Density Function
10
Definitions
 MU = Market uncertainty
 X is a random variable denoting the value of one choice the user has for a
service
 E(X) = V, s.d(X) = MU
 CP(A) cost to provide the service with architecture A
 F – is for flexible and expensive architecture
 C – is for controlled and efficient architecture
 VS(A) net value of service provided with A
 Assumptions – flexible networks allow users more choices, but cost more
 CP(F) > CP(C) – we call this the Business and Technical Advantages of
controlled (centralized) architecture
 BTA = CP(F) - CP(C)
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Model
 Value of controlled architecture
 VS(C) = V - CP(C)
 We assume only one choice for users
 Value of flexible architecture at the ed
 VS(F) = V - CP(F) + Q(x)*MU
 We assume x choices for users
Link to market uncertainty
 Flexible networks are more valuable when
 VS(F) - VS(C) > 0
 Which implies: Q(y)*MU > BTA
 This means the value of experimentation is greater then the
advantages of controlled/centralized architecture
© Mark Gaynor 11/05/08 - IPAM
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Results
Market Uncertainty Vs Cost Differential
Effects of MU on the Value of Experimentation
0-5
10-15 15-20
40-50
30
30-40
20-30
20
10-20
10
16-E
0
1-E
25
22
Cost Differential
6-E
19
MU
Q(x)*MU
0-10
11-E
16
17
13
9
5
1
# 1
trials
0
50-60
40
13
14
60-70
50
10
5
30-35
60
7
10
20-25 25-30
70
1
15
Value
25
20
5-10
4
30
Market Uncertainty
35
Q(y)*MU = Cost Differential
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Learning and evolution
MU Vs Cost-Diff
For 10 Experiements, GenFactor = 1/n
N o rm a l D e n s ity F u n c tio n
7
MU
1 .8
1 .6
5-6
5
4-5
4
3-4
3
10 Generations
2
1
0
0 .8
Cost-Diff
9
S .D =.2 5
5
S .D =1
3
1
2-3
1-2
0-1
4 Generations
1
1 .2
7 Generations
7
1 .4
1 Generation
0 .6
MU Vs Cost-Diff
10 Experiments, GenFactor = 1/2
0 .4
0 .2
3
7
E x p e rim e n t Va lu e
6-7
6
5-6
MU
5
4-5
4
3-4
3
10 Generations
2
7 Generations
1
0
Cost-Diff
9
7
5
3
© Mark Gaynor 11/05/08 - IPAM
1 Generation
2-3
1-2
0-1
4 Generations
1
2
1
0
-1
-2
0
-3
Probability
6-7
6
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Email Case Study
 Different ways to manage email
 Centralized web-based (hotmail) compared to
distributed ISP (PoP)
 Shift to centralized in 1996
 Low MU because of dominate design, stable
standards
 Market uncertainty is likely cause of this shift
 Technology or regulation can’t explain this shift
© Mark Gaynor 11/05/08 - IPAM
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Voice Case Study
 Distributed PBX compared to centralized Centrex
 Shift to centralized in 1996
 Low MU because good market predictions, stable feature set
 Market uncertainty is likely cause of this shift
 Technology or regulation can’t explain this shift
© Mark Gaynor 11/05/08 - IPAM
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Network Neutrality
 Users can use their bandwidth for anything they want,
in any way they want (Tim Berners Lee)
 The network does not bias traffic to and from users
based on its content, source, or destination
 Does not bias traffic to the disadvantage of the end
user
 A network service provider offering VoIP can not
give lower QoS to a competing VoIP service provider
in a neutral network
© Mark Gaynor 11/05/08 - IPAM
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Network Neutrality or Not
 Some say neutrality will not promote
investment in network infrastructure
 Some say non-neutrality will not promote
innovation in devices, applications, and
services
 How does uncertainty fit into this
© Mark Gaynor 11/05/08 - IPAM
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Openness in 700MHz Spectrum
 Google wanted in the 700MHz C block:
 “Open applications: consumers should be able to download and utilize any software
applications, content, or services they desire;
 Open devices: consumers should be able to utilize a handheld communications
device with whatever wireless network they prefer;
 Open services: third parties (resellers) should be able to acquire wireless services
from a 700 MHz licensee on a wholesale basis, based on reasonably
nondiscriminatory commercial terms; and
 Open networks: third parties (like internet service providers) should be able to
interconnect at a technically feasible point in a 700 MHz licensee's wireless network.
“ - http://googlepublicpolicy.blogspot.com/2007/07/promise-of-open-platforms-in-upcoming.html
 FCC wants:
 Any device and application
 Carriers want their devices, applications, services, and no
wholesale network pricing!
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Architecture Vs Neutrality
 Centralized networks such as the PSTN and
Cellular are easy to control and do not promote
network neutrality
 But, then can be neutral if the central manager allows it
 Distributed networks such as the Internet promote
neutral network
 But, it is possible for ISPs to bias traffic
 The PSTN has grown more distributed, and the
Internet has become more centralized
© Mark Gaynor 11/05/08 - IPAM
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End-2-end Vs Neutrality
 True e-2-e networks are by definition neutral
 The “old Internet” was e-2-e
 The “new Internet” is not e-2-e
 The ISP may filter and bias traffic to and from users
 The ISP may filter and bias traffic to and from other
ISPs
© Mark Gaynor 11/05/08 - IPAM
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Continuum of Network Architecture
Interne
t MPLS, NATs
PSTN
PBX
Centrex
IPv6
Very
Smart
Smart
Very dumb
Smart
Most efficient, with
most control
and accountability
© Mark Gaynor 11/05/08 - IPAM
Very smart
Most flexible with
least22
control
and accountability
Neutrality Model - Assumptions
and Notation
 A neutral network has a single provider for all
transport, content, and services
 A non-neutral network has a single transport
provider and many content/service providers
 The Transport provider offers a service bundle
(voice, video, email) including transport and its
pick of content/services
 The costs of a neutral and non-neutral network is
the same to build and manage
 We believe the non-neutral network is more expensive,
which strengthens our argument
 The network has N users
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Neutrality Model

R(SCP) = total revenue from customers for services and content to the
provider(s)
 Sum of revenue for all non-transport services and content.

R(TSP) = total revenue from customers for transport service to the transport
provider
 We assume the value of R(TSP) is proportional to the value of R(SCP), the more
the services and content is worth, the more the pathway to these services and
content is valued by users
 Let “P” be this proportionality constant, then:
 R(TSP) = “P” * R(SCP)
 This is similar to metered pricing that charges by byte, however in this case the “network
tax” is related to the value of the service to the user, not the network resources consumed
by the service

In this simple model the total value of the network is just R(SCP) + R(TSP)
 V(Total) = R(SCP) * (1 + “P”) – Total value of the network infrastructure.

V(Total) has two components, the transport service (i.e. R(SCP) * “P”) and the
service component (i.e. R(SCP))
© Mark Gaynor 11/05/08 - IPAM
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Expected Average Revenue Per
User (ARPU)
 Users will pay more for what they like better!
 Nobody really knows what users want for content
and services
 Nobody knows what users will pay for what
content
 There is a limit!
 Price to get 50% of users in uncertain markets =
Average Price (AP)
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Market Segmentation of Users
Probability
Normal Density Function
Users that
value service
a bit less
than average
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Users that
value service
a bit more
than average
1
s.d.
Users that
value service
far less
than average
S.D=1
34%
Users that
value service
far more
than average
34%
13%
13%
2%
2%
Mean
-3
-2
-1
0
100
AP
1
2
U2 U10
Experiment
Value
User Value
© Mark Gaynor 11/05/08 - IPAM
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U100 U1000
26
Simulation of Value of Choice
Var=1
Var=5
Var=10
25
20
15
10
5
0
-5 0
-10
-15
-20
-25
Content Value
Expe rime nt Value
Best Service/Content/Application
Best Content
20
40
60
80
100
Content Provider
Service/Content/Application
Provider
Expe rime nt Numbe r
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Value of Non-Neutral Network
(No Market Uncertainty)
 In this case, R(SCP) = the number of subscribers,
times the fixed price. The fixed price with no
market uncertainty is just “AP” the mean of the no
longer random variable describing the value of a
set of services selected by the service provider
 R(SCP) = “N” * “AP”
 V(Total) = “N” * “AP” * (1 + “P”)
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Value of Non-Neutral Network
(Market Uncertainty)
 The single provider only gets 50% of the users
because the other 50% of users value the service
bundle at less than the provider is charging
 V(Total) = “N/2” * “AP” * (1 + “P”)
 Market uncertainty reduces the value of a nonneutral network by 50%
1
s.d.
S.D=1
34%
34%
13%
13%
2%
2%
U2 U10
Experiment
Value
User Value
© Mark Gaynor 11/05/08 - IPAM
3
2
1
100
0
-1
Mean
-2
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-3
Probability
Normal Density Function
U100 U1000
29
Value of Neutral Network
(No Market Uncertainty)
 Users have choices, but don’t care much about them
 V(Total) = “N” * “AP” * (1 + “P”)
 Same value as non-neutral network
 But, in this case the transport provider does not get all of this
 We assume they get 75% of the content/service revenue because their
content/services are as good as any other service providers
 The other content/service providers get 25% of content/service
revenue
 Transport SP gets 0.75*N*AP for services + P*N*AP for
transport services
 All others split 0.25*N*AP content/service revenue
© Mark Gaynor 11/05/08 - IPAM
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Value of Neutral Network (Market Uncertainty)

V(Total) = (revenue of services from transport provider) +
(revenue from services for other providers) +
(revenue from new users) +
(transport revenue)


V(Total) = (“N/8” * AP) +
((3/8) * “N” * Ux) +
/25% of the 50% of users that value the
/bundled service the most - Very Happy
/rest of the 50% don’t get the bundle, but
/they pay more - i.e. Ux
Best of x choices
((34/100) * “N” * Ux) + /68% of the 50% of non-users become
/users because they have choice, they pay Ux
“P” * ((“N/8” * AP) + ((3/8) * “N” * Ux)) + ((34/100) * “N” * Ux))) /Transport
=
(1+P) * (“N/8” * AP + (71.5/100) * “N” * Ux)
 Total service revenue is (“N/8” * AP + (71.5/100) * “N” * Ux), and the network
transport tax is P times this service revenue
© Mark Gaynor 11/05/08 - IPAM
Value linked to uncertainty
31
Value Model
 AP = $100 - The average price users are willing to pay for
a service.
 SD = $100 – The standard deviation of the distribution
describing the value of services to users.
 “N” = 1000 – Number of potential users
 “P” = .50 - Fraction of revenue for services that a user pays
for transport – network tax.
 X = 100 – Number of choices a user has for services.
 U(100) is rounded to 2 standard deviations above AP, thus
U(100) = $300.
© Mark Gaynor 11/05/08 - IPAM
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Results
No Market Uncertainty
Non Neutral Network
Neutral Network
Transport
Services
Total
Transport
Services
Total
Transport SP
50K
100K
150K
50K
75K
125K
Other SPs
0
0
0
0
25K
25K
Total network value
50K
100K
150K
50K
100K
150K
High Market Uncertainty
Non Neutral Network
Neutral Network
Transport
Services
Total
Transport
Services
Total
Transport SP
25K
50K
75K
113,500
12,500
126,000
Other SPs
0
0
0
0
214,500
214,500
Total network value
25K
50K
75K
113,500
227,000
340,500
• Uncertainty decreases the value of a non-neutral network
• Uncertainty increases the value of a neutral network
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Conclusion
 Real options provide a way to model uncertainty
 Our model illustrates the tradeoffs between:
market uncertainty, the advantages of control and
central management, and the number of
experiments
 It shows that neutral networks generate more total
revenue (transport + services) in uncertain markets
 The question changes from neutrality or not, to
how to split the bigger pie
 Suggestions for what next
© Mark Gaynor 11/05/08 - IPAM
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Acknowledgements
 My thesis advisor H.T. Kung
 Carliss Baldwin and Marco Iansiti from
Harvard Business School
 Scott Bradner
© Mark Gaynor 11/05/08 - IPAM
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