Incomplete InformationBayesian Nash Equilibrium and Asymmetric.docx

Incomplete Information
Bayesian Nash Equilibrium and Asymmetric Information
Learning Objectives
Complete versus incomplete information
Bayesian Nash Equilibrium
Games with incomplete information in discrete strategies
Games with incomplete information in continuous strategies
Asymmetric information
Game of Cheap Talk
©Vidya Atal, Montclair State University
Complete Information
All rules of the game fully known by all players and are
common knowledge
All strategies, sequence of moves and all pay-offs
Not so common in reality
Types of Information
Information
Complete information

Incomplete information
Perfect information
Imperfect information
Symmetric & Asymmetric
4
Incomplete Information in Discrete Strategies
Incomplete Information and Uncertainty
Uncertainty – random events
Nature’s move, Nature being “player 0”
Treat the two types of Player 1 as two players playing
simultaneously

A Bayesian Nash Equilibrium in a game is
a list of strategies, one for each type of player,
such that no type of player can get a better payoff by switching
to some other strategy that is available to her
given the beliefs about the types of players
while all other types of players adhere to the strategies
specified for them in the list
Bayesian Normal Form &
Bayesian Nash Equilibrium: (B12B0, D)
Exercise 1
Consider the following game. Nature selects A with probability
½ and B with probability ½. If Nature selects A, players 1 and 2
interact according to matrix A. If Nature selects B, players
interact according to matrix B.
Suppose that when the players choose their actions, they don’t
know which matrix they are playing. Write the normal form
matrix that describes this Bayesian game. What is the strategy

profile that will be played here?
Exercise 1(a) Answer
This is the case of symmetric incomplete information
Bayesian normal form matrix:
Equilibrium strategy: (Z, V)
Exercise 1 continued…
Now suppose that before the players select their actions, Player
1 observes Nature's choice. Player 2 does not observe Nature's
choice. Represent this game in the Bayesian normal form. What
is the Bayesian Nash Equilibrium in this game?
In this example, is the statement “A player benefits from having
more information” true or false?
Exercise 1(b, c) Answer
Bayesian Nash Equilibrium: (XAYB, W)
Player 1 does NOT benefit from more information

Incomplete Information in Continuous Strategies
Cournot Game with Incomplete Information
Two firms producing the same good, say bricks
Competing by independently (simultaneously) choosing how
much to produce (in thousands), assume all bricks are sold
Price that consumers are willing to pay depends on total number
of bricks
Firms’ marginal cost of production:
With probability with probability
Consider 2 types as 2 separate players
Bayesian Nash Equilibrium in Cournot Duopoly
Profits:
Best response functions:
1 chooses to maximize assuming , fixed
2L chooses to maximize assuming , fixed
2H chooses to maximize assuming , fixed

Bayesian Nash Equilibrium in Cournot Duopoly
Best response functions:
Bayesian Nash Equilibrium –
Asymmetric Information
Incompleteness of information is usually asymmetric
Each player knows her own capabilities and payoffs better than
others
Manipulating what others know and believe about you, you can
influence equilibrium outcome
Better informed player may want to:
Either conceal information or reveal misleading information
(bluff in poker)
Or reveal selected information truthfully (signaling)
Less informed player may want to:
Elicit information or filter truth from falsehood (incentives)
Remain ignorant (credible deniability)
Revealing Misleading Information

Cheap Talk
Example: Cheap Talk
Good, Mediocre, or Bad investment
Financial adviser better informed, who may overstate ROI
Adviser’s fee – 2% of $100 investment and 20% of gains
Return – (-50) in B, 1 in M, 55 in G
Reputation cost to adviser for misrepresentation – S if small, L
if large; 0<S<L
Dominated Strategies
in Cheap Talk
“Choose I if B” is dominated by “Choose N if B” --- eliminate
node a
“Choose I if M” is dominated by “Choose N if M” --- eliminate
nodes c, g
“Report B” or “Report M” lead to choosing N
Best Response Analysis in Cheap Talk
So when S<L<2, “babbling” equilibrium is the only BNE
(Always G, N if G) is a BNE
“babbling” – no information communication
(G only if M or G, I if G) is BNE if S<2.2 and L>2; say S=2,

L=3
“partial revelation” – not B
(G if and only if G, I if G) is BNE if S>2.2 and (L+S)>4.2; say
S=2.5, L=3
“full revelation” – G only when G
/
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Running Head: CHAPTER 16 -20 CHAPTER 16 -20 2
Cloud Computing
Ajay Masand
University of Cumberlands
ITS-532-06
Dr. Steven Case
06/20/2020
Cloud Computing
Chapter 16
The total cost of ownership
This is the analysis putting a single value on a complete life
cycle capital purchase. The value includes every ownership
phase like soft costs of management,
acquisition, and operation. (Kling, 2014) The total cost of
ownership hence includes the price of purchase when given
asset. The following are the ten items to
be considered in the determination of the total cost include: ·
Installation manpower · Electricity · Maintenance and service ·
Space of the facility · Project
management · Server Equipment and power supply · HVAC

equipment · Networking cost and software · System monitoring
· Rack and hardware
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management Server Equipment and power supply HVAC
equipment Networking cost and software System monitoring
Rack and hardware
Capital Expense
This is a type of expense experienced by businesses when trying
to create benefits for the future. A cost expenditure can be
incurred in a situation where
debt is taken by a user to add volume to assets. Capital expenses
include acquiring fixed assets such as building, intangible
resources like patents, and
also upgrade of the already on place facilities (Safonov, 2017).
The capital expense is also used to overtake new projects by a
firm. Making capital expenditure
on the fixed asset includes repairing a roof to building, building
factories, and purchasing equipment (Kling, 2014). Economies

of Scale
In cloud computing, economies of scale are used to refer to
cloud architecture, such as cooling equipment, network
bandwidth, and power supply
equipment. The cloud computing aspects are scaled up and are
using larger components subjected to lower unit costs. There
vital building blocks of the
cloud computing that are scaled out since they grow by
increasing in quantity (Kling, 2014). Economies of scale can be
used to describe the cost per unit
depending on the production capacity. For example, a company
making 200 widgets, can experience a cost of 10% each piece in
production of the widget
(Kling, 2014). Economies of scale make an organization pay for
only what it needs, the organization gets to save money, and the
company also saves money
when it streamlines the workforce. The zero upfront costs are
expected with organizations practicing economies of scale
(Kling, 2014). The economies of
scale are very good for the cloud computing environment.
Right resizing
This involves selecting the most cost-effective instance for the
workload of a company. Take for example when a company
decides to do lift and shift in
which case when an organization requires 16 GB memory RAM
for an application (Kling, 2014). In case a company needs
16GB, there will be a need for a large
instance for the company which will cost a lot of money. In
cloud computing matters, three steps can be applied to attain an

effective outcome when
performing right resizing (Kling, 2014). The steps are like
termination, rightsizing, and leveraging RIs. Moor’s Law
This is the law of double processing power over two years.
Moore's law still applies at the level of data center specifically
when considering the
consumption of cloud to satisfy the cloud computing future
(Ruparelia, 2016). The law also states that the number of
transistors in a single semiconductor is
supposed to be doubled every two years without any cost
incurred hence allowing the computer industry to offer more
power of processing in the lighter
computing device. Company Profit
Profit=Revenues-expenses=$2.5-$2.1=$0.4 Profit margin= (Net
income)/Revenue×100=0.4/2.5×100=0.16=16%
Chapter 17
Functional and Nonfunctional requirements
The functional requirement indicates what the software is
needed to do while non-functional requirements illustrate the
limitations under which software will
operate. For example, in sending emails, the functional
requirement illustrates how the system needs to send the email
in case a given requirement is met.
Nonfunctional requirement illustrates an email to be sent within
a given latency upon which within the given period. A
functional requirement is very
important since they define the performance of the system since
it re-counts the functionality of a particular system. The non-
functional requirement is

important in elaborating on the characteristics of the system.
The designer should avoid selecting a platform
During system development, the design phase helps in the
transformation of requirements of implementation into a
detailed and complete system design
specification (Noghin, 2018). After approval of a design, the
development team always kicks in to start the development
process. Selecting a platform is
not simple since there is always ever-increasing capabilities of
technology (Noghin, 2018). The evaluation, contraction, and
implementation are becoming
more and more complex especially for companies with many
departments looking to use the platform (Noghin, 2018).
Tradeoffs
The tradeoffs required by the designer revolves around choosing
the cloud configuration service and in most cases, while
building efficient, scalable, and
secure systems and IoT (Noghin, 2018).
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When a designer makes a trade-off, the designer is always
making a compromise, and hence every decision a designer
makes is always a trade-off. This
means that achieve something must be done at the expense of
the other. This requires the designer to be careful when
selecting their priorities. The
system maintenance phase is very expensive
The system maintenance phase is the most expensive since it is
the phase that is the longest in the life cycle of a system. Once
the software is developed, it
remains in operation as long as it is not rendered obsolete.
During the operation, the system is constantly maintained due to
changes in requirements. There
are conceptual methods needed to support software developers
with the maintenance process. The addition of new features to
an existing system is
sometimes very difficult compared to starting from scratch.
Maintenance of software requires training which is also
expensive.
Chapter 19
Scalability Scalability is the method that defines the ability of a
given network, process, organization, or software to manage
increased growth and demand at

the same time. Therefore, a system, software, or business,
which is known to be scalable is considered to be advantageous
since it is adaptable to the
demands of clients or users. Scalability is essential since it
contributes immensely to reputation, quality, competitiveness,
and efficiency. Small-scale
businesses are also required to be thoughtful about the
scalability because they exhibit the chance of growth. While
several areas in an organization are
considered to be scalable, some have proven to be impossible.
Scalability can also be achieved either through scaling out or
scaling up. For example,
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some applications can be scaled up by adding more servers,
CPUs, ore storage capacity to the already existing systems. The
problem associated with
scaling up is establishing the right balance between the
available resources, which is observed to be completely
difficult. Pareto Principle Pareto Principle is
the best way of optimizing, understanding, and assessing
virtually situations, especially the one involving distribution
and usage of some sort (Payne, 2012). In
that case, the potential relationships of Pareto Principles
involve aspects of work, organizational development, personal

life, and business (Payne, 2012). The
Pareto Principles can also be described by different names like
Pareto Theory, the, 80-20 principle, the principle of imbalance,
and the rule of the vital few.
The Pareto Theory is extremely essential for checking project
management, business development, and organizational
planning. Furthermore, leadership
skills can be effectively applied when the Pareto principles are
put into practice by organizations. This also applies to every
aspect of leadership theory or
approach. Pareto Principle is also useful in swift clarity to
complex situations and problems, especially when directing
resources to the correct project
(Payne, 2012). Vertical and Horizontal Scaling When analyzing
databases, horizontal-scaling is usually defined by the partition
of data, whereby each node for
scaling contains only part of the data required for scaling. On
the other hand, with the vertical-scaling, data usually reside on
a single node as the scaling
process is done via multi-core since the load spreading is
achievable between CPU and RAM of the machine. While the
vertical scaling is limited to one
machine, horizontal scaling is dynamic because several
machines can be added into the already existing pool. Examples
of vertical scaling include MySQL and
Amazon RDS while examples of the horizontal scaling are
MongoDB, Cassandra, and Google Cloud Spanner. Vertical
scaling is easy to achieve because
smaller machines can be switched into bigger ones. Database
read/write ratio Importance The database read/write ratio is
essential since it can standardize
disk speeds across different environments. However, most of the
applications can write and read different disks recurrently.
Read/write ratio is also

present in many measurements that are performance-related
such as latency, Disk Throughput, and IOPS. For that reason,
understanding such kind a
ratio is important for storage devices and array design.
Read/Write ratio is more essential than cloud users could
realize. The practice is to look at the IO
profile of the application. Although the step has proven to be
critical, many results are usually misinterpreted. The objective
of using databases read/write
ratio is to help with understanding how applications, which rely
on the ratio work, including the life cycle of writing and
reading the data. Some applications
like making assumptions while others spend more time, more so
when the writing and reading activity is less than 50%. Uptime
Percentage Calculation
Uptime referred to as the amount of time that any service tends
to be operational and available. In that case, an uptime of
99.99% is equal to 4 minutes and
19 seconds downtime.
Chapter 20
Cloud and TV broadcasting The advantages of cloud-based
services have proven to be notable because they are software-
based.
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In that case, one will not need a physical location to achieve
cloud operations. All of the broadcasting business and
operations have been virtualized
because of cloud-based services. This is proven by several
companies, which are delivering their channels through cloud
platforms. Broadcasting of channels
is currently delivered via cloud internet, which is also observed
to be virtually operated. The services provided by the cloud are
software-based and they
do not require a physical location for operations. For that
reason, the cost of real estate, manpower, and infrastructure has
drastically gone down. The
benefits brought by the technology of cloud-based broadcasting
encourage quick turnaround time and thereby making the ability
to develop and destroy
channels to be easy. Remote management and transparency are
also possible with cloud-based broadcasting. This is because
one cannot monitor a channel
through an internet browser. Intelligent Fabric Intelligent
fabrics are materials used in networking to help with true
flexibility and business agility. In that
case, any intelligent fabric incorporated in the network can
make cloud business more agile because the network will be
easy to deploy and maintained as

well. Intelligent fabrics also initiate affordable operations
because of minimized complexity, which is possible through
central management and automated
moves. These fabrics are also essential for comprehensive
visibility because they can ensure performance in real-time,
especially when integrated with the
profiles of virtual networks. Intelligent fabrics can also be used
for monitoring external stimuli because they can respond
accordingly when translating
technological components into data. However, intelligent
fabrics can be aesthetic depending on performance
enhancement, fashion, or design
objective. Data can also be recorded and handled quickly when
using network systems incorporated with intelligent fabric.
Cloud Technology and Mobile Application market Currently,
smartphones and tablets have access to wireless networks,
which are of high-speed and this
has allowed these devices to gain from cloud-based technologies
like any other traditional computer (Rountree & Castrillo,
2014). As cloud technology
continues to expand, many mobile application developers also
have the wish of ensuring success as they embrace the new
movement. However, the
landscape of mobile application is still evolving and developers
are encouraged to reach the application functionality that was
never witnessed before
(Rountree & Castrillo, 2014). Another factor that is driving the
market for mobile applications is mobile gaming. This is also
supported by mobile phones and
tablets, which have high-end technologies in terms of graphics,
which is the primary factor to be considered when installing a
gaming app on the PC or Tablet
(Rountree & Castrillo, 2014). The issue of mobile gaming is not

related to simple puzzles or basic card games but immersive
games like car racing and
sports games (Rountree & Castrillo, 2014). In that case, when
connective mobile phones to the cloud network, gamers will
have the advantage of experiencing
the best gaming applications (Rountree & Castrillo, 2014).
Importance of HTML 5
The essentiality of the HTML5 starts by ending the use of
browser plugins. It is because of HTML5 that aspects of the rich
media, which previously depended
on the use of plugins, currently use built-in (Millard, 2014).
therefore, new media tags like <audio> and <video> can be
witnessed. HTML5 is
important because it is supported by major vendors, especially
the ones engaged in the mobile space. The experience promoted
by HTML5 is universal and
cut across a larger spectrum of computer devices (Millard,
2014). Moreover, HTML5 is still evolving and the differences
experienced with many
implementation methods are expected to narrow down. HTML5
has also promoted the possibility of device ubiquity (Millard,
2014). This implies that once the
developer has developed something once, it can be possibly
used in a wider range of browsers (Millard, 2014). Cloud and
Operating System Future
Possibly, memory, disc space, and related resources are shared
by the cloud system. For that reason, it is easy to use many
operating systems on one
machine because of cloud technology (Catlett, 2013). The
subsequent use of the web and the internet have also changed
the traditional use of operating

systems. Users have been moving the key concepts of the
operating system to the cloud without relying on a specific
platform because cloud computing can
be accessed anywhere (Millard 2014) Conceivably cloud
computing can impact the future use of operating systems since
most of the computer users
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Source Matches (51)
Student paper 100%
Student paper 100%
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be accessed anywhere (Millard, 2014). Conceivably, cloud
computing can impact the future use of operating systems since
most of the computer users
prefer working with cloud-based applications such as Google,
Gmail, and Google Spreadsheets (Catlett, 2013). For that
reason, every computer will only need
a basic operating system to boot the operation into the web
mode. Personal computing will also not require an operating
system, which is a heavy-duty type.
1
Potential Location-aware applications The technologies of the
potential location-aware applications include the
implementation of the wireless access point
for identifying the physical location of the electronic gadget,
GPS, and infrastructure of the cellular phone (Catlett, 2013).
Users of mobile devices are also free
will to share information with the applications or location-
aware. The location-aware applications can also help users with
information such as reviews on
restaurants, traffic congestion, or map location marker (Catlett,
2013). Location applications are also browser plug-ins installed
in web-enabled gadgets.
The combination of wireless access points, phone towers, and
GPS satellites can be essential in establishing the location of

the user (Catlett, 2013).
Nonetheless, the physical location of the user will be
determined by how the user is connected to connection points,
which are perceived to be independent
(Catlett, 2013). Intelligent Devices The commonly known
intelligent devices are sensors, phablets, smartphones, smart
glasses, tables, and just to mention a
few. While many intelligent devices are portable, they must be
defined by their ability to interact, share, and connect to the
network remotely (Bhowmik,
2017). Intelligent devices are also related to sensors, which
have been collected together to form the Internet of Things.
However, the process of collecting
data by using collections of sensors or the Internet of Things
can be complex as establishing a video feed (Bhowmik, 2017).
Sensors are known to be
intelligent devices, which their data can be thought of in the
form of location, humidity, and sound of different
measurements of machines or the human
body (Bhowmik, 2017). Sensor devices are also incorporated
with built-in wireless connectivity, which encourages the
exchange of data and internet
connection. This is the same principle that can result in the
generation of Big Data.
References
Bhowmik, S. (2017). Cloud computing. Cambridge, United
Kingdom; New York, NY: Cambridge University Press. Catlett,
C. (2013). Cloud computing
and big data. Amsterdam: IOS Press. Kling, A. A. (2014). Cloud
computing. Farmington Hills, Mich.: Lucent Books, an imprint
of Gale Cengage Learning.

Millard, C. J. (2014). Cloud computing law. Oxford: Oxford
University Press. Noghin, V. D. (2018). Reduction of the Pareto
set: An axiomatic
approach. Cham, Switzerland: Springer
Payne, M. (2012). Pareto principle. Place of publication not
identified: PublishAmerica. Ruparelia, N. B. (2016). Cloud
computing. Cambridge,
Massachusetts; London, England: The MIT Press. Rountree, D.,
& Castrillo, I. (2014). The basics of cloud computing:
Understanding the
fundamentals of cloud computing in theory and practice.
Waltham, Mass: Syngress. Safonov, V. O. (2017). Trustworthy
cloud computing. Hoboken, New
Jersey: John Wiley & Sons, Inc.
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Student paper
University of Cumberlands
Original source
University of the Cumberlands
1
Student paper
06/20/2020
Original source
06/20/2020
1
Student paper
The total cost of ownership
Original source
Total Cost of Ownership
/
Student paper 66%

b-ok 100%
Student paper 79%
Student paper 81%
Student paper 68%
Student paper 65%
Student paper 64%
1
Student paper
Capital expenses include acquiring
fixed assets such as building, intangible
resources like patents, and also
upgrade of the already on place
facilities (Safonov, 2017).
Original source
As mentioned before, capital expenses
include the acquisition of fixed assets
like business equipment, or new
buildings, attainment of intangible
resources like patents, and upgrading
the already existing facilities (Safonov,
2017)
2
Student paper

Economies of Scale
Original source
Economies of Scale
1
Student paper
In cloud computing, economies of scale
are used to refer to cloud architecture,
such as cooling equipment, network
bandwidth, and power supply
equipment. The cloud computing
aspects are scaled up and are using
larger components subjected to lower
unit costs.
Original source
Economies of Scale Economies of scale
in cloud computing refers to aspects of
cloud architecture, like network
bandwidth, cooling equipment, and
power supply equipment (Safonov,
2017) These aspects of cloud
architecture are typically scaled up and
are using larger components subjected
to lower unit costs
1
Student paper
Economies of scale can be used to

describe the cost per unit depending
on the production capacity.
Original source
Economies of scale can also be used in
describing the reduction in the cost-
per-unit depending on the capacity of
production
1
Student paper
The economies of scale are very good
for the cloud computing environment.
Original source
Economies of scale have proven to be
a reality in cloud computing because it
is good for the environment
1
Student paper
This involves selecting the most cost-
effective instance for the workload of a
company.
Original source
Right-sizing is the technique of
selecting the most cost-effective
instance for the company's workload

1
Student paper
In cloud computing matters, three
steps can be applied to attain an
effective outcome when performing
right resizing (Kling, 2014).
Original source
In matters about cloud computing,
three steps can help with effective
results when performing the right
sizing
/
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Student paper 71%
Student paper 71%
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Moore's law still applies at the level of
data center specifically when
considering the consumption of cloud
to satisfy the cloud computing future
(Ruparelia, 2016). The law also states
that the number of transistors in a
single semiconductor is supposed to
be doubled every two years without
any cost incurred hence allowing the
computer industry to offer more
power of processing in the lighter
computing device.
Original source
Therefore, Moore's Law still applies at
the level of the data center, especially
when considering the consumption of
cloud to satisfy the future of cloud
computing (Ruparelia, 2016) Moore’s
Law also states that the number of
transistors in one semiconductor
should be doubled after every two
years without any added cost, thereby
allowing the industry of computers to
offer more processing power in
smaller and lighter computing devices
(Bhowmik, 2017)
1
Student paper
Profit=Revenues-
expenses=$2.5-$2.1=$0.4 Profit

margin= (Net
income)/Revenue×100=0.4/2.5×100=0.
16=16%
Original source
Profit=Revenues-
expenses=$2.5-$2.1=$0.4 Profit margin
= (Net
income)/Revenue×100=0.4/2.5×100=0.
16=16%
1
Student paper
The non-functional requirement is
important in elaborating on the
characteristics of the system.
Original source
Conversely, the non-functional
requirement is known to be
elaborating on the performance
characteristics of the system
(Bhowmik, 2017)
1
Student paper
Selecting a platform is not simple since
there is always ever-increasing
capabilities of technology (Noghin,
2018).

Original source
Platform selection is also not a simple
task because of ever-increasing
technology capabilities
1
Student paper
The system maintenance phase is very
expensive The system maintenance
phase is the most expensive since it is
the phase that is the longest in the life
cycle of a system.
Original source
System Maintenance Phase The system
maintenance phase might be the most
expensive because it is the longest
phase in the life cycle of software
development (Ruparelia, 2016)
1
Student paper
Therefore, a system, software, or
business, which is known to be
scalable is considered to be
advantageous since it is adaptable to
the demands of clients or users.
Scalability is essential since it
contributes immensely to reputation,

quality, competitiveness, and
efficiency.
Original source
For that reason, the business,
software, or system that is described to
be scalable is more advantageous
because of its being more adaptable to
the change demands or needs of
clients or its users Scalability is
important because it contributes to
efficiency, reputation, quality, and
competitiveness
/
Student paper 82%
Student paper 66%
Student paper 75%
businessballs 63%
1
Student paper
Scalability can also be achieved either
through scaling out or scaling up.
Original source

The advantage is that scalability can be
achieved by either scaling up or scaling
out
1
Student paper
The problem associated with scaling up
is establishing the right balance
between the available resources, which
is observed to be completely difficult.
Pareto Principle Pareto Principle is the
best way of optimizing, understanding,
and assessing virtually situations,
especially the one involving
distribution and usage of some sort
(Payne, 2012). In that case, the
potential relationships of Pareto
Principles involve aspects of work,
organizational development, personal
life, and business (Payne, 2012). The
Pareto Principles can also be described
by different names like Pareto Theory,
the, 80-20 principle, the principle of
imbalance, and the rule of the vital
few.
Original source
However, the problem with scaling up
is finding the right balance of
resources, which have also proven to
be extremely difficult It is remarkably
an easy way of assessing, optimizing,
understanding virtually any situation

involving the usage or distribution of
some kind (Payne, 2012) Therefore, the
potential uses and relationship of the
Pareto Principle cover most aspects of
business, work, personal life, and
organizational development (Payne,
2012) The Pareto Principles is also
known by several different names such
as Pareto’s Law, Pareto Theory, the 80-
20 rule, 80-20 principle, the Pareto’s
80-20 rule, the rule of the vital few, the
principle of imbalance, and the
Principle of the Least Effort (Noghin,
2018)
1
Student paper
The Pareto Theory is extremely
essential for checking project
management, business development,
and organizational planning.
Original source
Similarly, the Pareto Theory is
extremely useful for reference or when
checking project management,
organizational planning, and business
development (Noghin, 2018)
3
Student paper

Pareto Principle is also useful in swift
clarity to complex situations and
problems, especially when directing
resources to the correct project (Payne,
2012).
Original source
The Pareto principle is extremely
helpful in bringing swift and easy
clarity to complex situations and
problems, especially when deciding
where to focus effort and resources
/
Student paper 67%
Student paper 67%
Student paper 67%
Student paper 63%
Student paper 73%
1
Student paper
On the other hand, with the vertical-
scaling, data usually reside on a single
node as the scaling process is done via
multi-core since the load spreading is

achievable between CPU and RAM of
the machine. While the vertical scaling
is limited to one machine, horizontal
scaling is dynamic because several
machines can be added into the
already existing pool. Examples of
vertical scaling include MySQL and
Amazon RDS while examples of the
horizontal scaling are MongoDB,
Cassandra, and Google Cloud Spanner.
Vertical scaling is easy to achieve
because smaller machines can be
switched into bigger ones.
Original source
However, in the vertical scaling, the
data is expected to reside on a single
node, whereby the scaling is done
through the multi-core and as
mentioned before, the load is spread
between the RAM and CPU of the given
machine (Bhowmik, 2017) Horizontal
scaling is dynamic because it is easy to
add more machines into the existing
pool while the vertical scaling is limited
to the capacity of one machine,
whereby scaling beyond that capacity
usually involved downtime, which will
require an upper limit Examples of
horizontal scaling include Google Cloud
Spanner, MongoDB, and Cassandra
The examples of vertical scaling
provide an easy way of scaling,
whereby smaller machines are
switched to bigger ones

1
Student paper
Database read/write ratio Importance
The database read/write ratio is
essential since it can standardize disk
speeds across different environments.
Original source
The database read/write ratio is
important because it can attempt to
standardize the comparison of disk
speeds across various environments
4
Student paper
Read/write ratio is also present in
many measurements that are
performance-related such as latency,
Disk Throughput, and IOPS.
Original source
This is inclusive in most performance
related measurements such as the
latency, the Disk Throughput, IOPS
among others
1
Student paper

For that reason, understanding such
kind a ratio is important for storage
devices and array design.
Original source
Therefore, understanding this
particular ratio is essential in array
design and storage devices
1
Student paper
The practice is to look at the IO profile
of the application. Although the step
has proven to be critical, many …
Games with Sequential Move and Subgame Perfection
Learning Objectives
Solving sequential games using backward induction
Incredible threat and Nash Equilibrium
Subgame Perfect (Nash) Equilibrium
Stackelberg model of oligopoly
More examples of sequential game

Games with Sequential Move in Discrete Strategies
Solving A Sequential Game
Solving by Pruning (Backward Induction)
Sequential Rationality
An optimal strategy should maximize the player’s expected
payoff conditional on every information set where he has the
move
A player should specify an optimal action from each of his
information sets, even those that he does not believe (ex ante)
will be reached in the game
Incredible Threat and Nash Equilibrium
Two NE – (I, A) and (O, P)
Is (O, P) plausible?

Subgame
Subgame
All the branches of the tree that don’t rip off any information
set
Subgame Perfect Nash Equilibrium
A Subgame Perfect Nash Equilibrium (SPE) specifies a Nash
Equilibrium in every subgame of the original game
Three NE – (UA, X); (DA, Y); (DB, Y)
Only one SPE – (UA, X)

Example 2
Two NE – (OA, O); (OB, O)
SPE – (OA, O)
Exercise 1
Find out all the pure strategy Nash Equilibria and all the
Subgame Perfect Nash Equilibria

PAUSE HERE and find out the answers first
Solving Exercise 1Player 2ACADBCBDPlayer 1UE1, 41, 45,
25, 2UF1, 41, 45, 25, 2DE3, 36, 23, 36, 2DF2, 06, 22, 06, 2
NE:- (DE, AC); (DF, AD); (DF, BD)
SPE:- (DE, AC)
Strategic Thinking for Fun!!!!
A traveler gets lost on a deserted island and finds himself
surrounded by a group of n > 0 cannibals.

Each cannibal wants to eat the traveler but, as each knows, there
is a risk. A cannibal that attacks and eats the traveler would
become tired and defenseless. After he eats, he would become
an easy target for another cannibal (who would also become
tired and defenseless after eating).
The cannibals are all hungry, but they cannot trust each other to
cooperate. The cannibals happen to be well versed in game
theory, so they will think before making a move.
Does the nearest cannibal, or any cannibal in the group, devour
the lost traveler?
Games with Sequential Move in Continuous Strategies
Stackelberg (1934) Model of Oligopoly
Recall the Cournot Model of Oligopoly (Module 6)
Instead of choosing simultaneously, now suppose the firms
choose the quantity sequentially
Firm 1 is the leader and firm 2 is the follower
Price that consumers are willing to pay depends on total number
of bricks being sold:
Firms have the same marginal cost of production:
Stackelberg Model: Sequential Move Game
Firm i’s strategy:

Firm i’s payoff is its profit
For any , leader firm can predict follower firm’s behavior
it maximizes its payoff by choosing its best response
Knowing this, leader maximizes:
Hence, SPE is and
Any other Nash Equilibrium in Stackelberg Model?
Consider the following strategy:
Leader:
Follower:
Check that it is a NE, but it is not SPE because of incredible
threats from the follower
Exercise 2: Double Marginalization
A tire manufacturer produces at a cost of $10 per unit which he
sells to a retailer at $x and then the retailer in turn sells to
consumers according to the following demand: . The retailer has
no additional cost. Find out the SPE of this game. Calculate the
joint profit.
Suppose instead the manufacturer could sell directly to
consumers. Find out his profit maximizing q and calculate the
profit.
Compare the two profits. The difference comes from double
marginalization problem (having two monopolists maximizing

respective profits versus one)
Solving Double Marginalization Game
For solution, please watch the video presentation
Moral Hazard &
Mechanism Design
Learning Objectives
Risk taking ability
Moral hazard versus adverse selection
Mechanism design
The Principal-Agent problem
Risk Taking Ability
Risk versus Uncertainty
Taking risk is under your control, but uncertainty is not
One can take risk to manage uncertainty
Risk-taking ability differs individually

Risk-averse
Risk-neutral
Risk-loving
Risk Aversion
Payoff is value of money
More money better is increasing
Examples:
Risk aversion means
First two examples don’t satisfy this
Concave utility functions capture risk aversion
Levels of Risk Aversion
Consider
risk neutral
As gets closer to 0, risk aversion increases
Mechanism Design

Example 1: Airline’s Price Discrimination
Adverse Selection & Mechanism Design
Suppose 100 customers, 70 are tourists and 30 are business
executives
If airline knows the type of each individual, then PE = 140 and
PF = 300
Hence profit = (140 – 100)x70 + (300 – 150)x30 = 7300
Airline’s Price Discrimination continued…
But airlines don’t know the type of individual; they cannot
force a business executive to buy a first class ticket
Device price structure so that business executives buy first class
tickets and tourists buy economy class tickets
If PE = 140 and PF = 300, consumer’s surplus to a business
executive from first class = 300 – 300 = 0, whereas from
economy = 225 – 140 = 85
No one buys first class; airline’s profit = (140 – 100)x100 =
4000
Airline’s Price Discrimination continued…
Instead, design PF = 300 – 85 = 215 so that consumer’s surplus
to a business executive from first class = 300 – 215 = 85, hence
buys first class
Hence profit = (140 – 100)x70 + (215 – 150)x30 = 4750
First class ticket price can be raised only if economy class
ticket price raised, say PE = 170 and PF = 245. But tourists

would not buy; hence PE = 140
(Incentive Compatibility Constraint)
(Participation Constraint)
The Principal-Agent Problem
Moral Hazard & Mechanism Design
Adverse Selection vs. Moral Hazard
In adverse selection, the asymmetric information is regarding
the player’s type (screening needed)
In moral hazard, the asymmetric information is regarding the
player’s action (monitoring needed)
Mechanisms are designed by the principal (when possible) to
enforce the desired action by the agent
less informed party
more informed party
Principal
Agent
Hires
Performs
Adverse Selection

Moral Hazard
Self
interest
Self
interest
The Principal-Agent Problem
Principal (CEO) is hiring Agent (software engineer)
Agent can put High effort or Low effort. If L, project is
unsuccessful for sure and revenue to P is R=2. If H, project is
successful with probability ½ and revenue to P is R=6
Payoffs for monetary wages :
A is risk averse: if high effort, and if low effort
P is risk neutral:
In the ideal world of full information, P knows whether A puts
H or L
Hence offers contract “w=1 if H and w=0 if L,” which would be
accepted by A because his outside option is 0
Principal-Agent Problem with Asymmetric Info
P is less informed about A’s effort exertion, so designs a bonus
mechanism to ensure A exerts high effort
Expected Payoff & Profit Maximization
No bonus case:

Agent will always shirk, hence P will choose w to maximize (2-
w) so that
This gives us and Principal’s payoff = 2
Incentive or Bonus Mechanism
P’s objective:
such that:
(Incentive Compatibility:– high type payoff more than low
type payoff)
(Participation Constraint:– high type payoff more than
outside option)
At optimality, the constraints hold with equality because P’s
payoff is decreasing in w and b
Solving, we get the following contract: “ and if project
successful”
Check that the contract will be accepted by A
Make sure that P wants to offer this contract, i.e., P’s payoff
with bonus is more than without bonus:
Example 2: Highway Construction
Highway to be built by state’s contractor and government
decides how many lanes (n)
Social value: , Contractor’s fee = F (includes per lane cost of
$3B or $5B, depending upon soil condition)
Government’s objective:
If full information, government knows soil condition and

chooses n to maximize if cost $3B/lane, or if cost $5B/lane
Hence
Highway Construction continued…
Asymmetric information: Contractor knows actual cost but
government knows cost is low with probability 2/3
Government’s objective:
such that:
Participation constraints:
Incentive compatibility constraints:
(actually low cost type does not mimic high cost type)
(actually high cost type does not mimic low cost type)
Solving Highway Construction Problem
Combining 2(a) and 1(b), we get:
Hence 1(a) is automatically satisfied, so ignore it
Let’s ignore 2(b) for the time being and solve, then we can
check if it is satisfied as well
Re-writing the remaining two constraints:
Government wants to pay the least fees so that the two
constraints are satisfied, hence (Note that low cost contractor
gets a premium for being honest)

Solving Highway Construction Problem
Re-writing government’s objective:
This gives
Check that constraint 2(b) is satisfied indeed and (V – F) > 0
©Vidya Atal, Montclair State UniversitynLFLnHFHComplete
Information12361050Asymmetric Information1248630
Adverse Selection
Market Failure, Perfect Bayes’ Equilibrium, Signaling and
Screening
Learning Objectives
The Market for Lemons
Perfect Bayes’ Equilibrium
Bayes’ rule to update belief
Pooling and Separating PBE
A lot of examples including Job Market Signaling
Adverse Selection
In adverse selection, the asymmetric information is regarding
the player’s type (screening needed)
If an insurance policy costs 5₵ for every $ of coverage, then it
attracts all the people who know their risk is higher than 5%
(and some additional risk averse people)

Attracting an unfavorable, or adverse, group of people
Adverse Selection & Market Failure
The Market for “Lemons” (1970)
George Akerlof
The Market for “Lemons” (1970)
Private used car market for, say, 2010 Honda Element
Could be in truly excellent condition (call peach)
Buyer’s valuation - $16000; Seller’s valuation - $12000
Could be in bad condition that cannot be checked in usual
regular inspection (call lemon) – seller’s private information
Buyer’s valuation - $6000; Seller’s valuation - $3000
Buyers value more than sellers, so it is efficient to be traded
Assuming limited amount of sellers and a lot of buyers
Hence with symmetric information (i.e., buyers can find out the
car’s condition), PP = $16000 and PL = $6000, and all cars will
be sold
Market for Lemons and Asymmetric Information
Note that p ≥ 12000
Lemon seller won’t sell if p < 3000
Peach seller won’t sell if p < 12000
Also, expected payoff of buyer ≥ 0
f∙(6000 – p) + (1 – f)∙(16000 – p) ≥ 0
16000 – 10000f ≥ p
Combining, we get f ≤ 0.4

If more than 40% of the used cars are lemon, then peach market
fails and bad cars drive good cars out of the market
Nash Equilibrium ConceptsTiming of the gameSimultaneous
MoveSequential MoveInformation typeCompleteIncomplete
Pure and Mixed Strategy Nash Equilibrium
Subgame Perfect Nash Equilibrium (SPE)
Bayesian Nash Equilibrium (BNE)
Perfect Bayes Equilibrium (PBE)
Perfect Bayes Equilibrium
Bayes’ Rule - Pooling and Separating Equilibrium
Bayes’ Rule and Updated Belief
Initial belief (p)
Updated (conditional) belief (q)
Conditional upon receiving a gift from player 1, player 2’s
updated belief that 1 is a friend
where and are the probabilities that the friend and enemy types
of player 1 choose to give a gift
Perfect Bayes Equilibrium
Separating PBE

Different types of the player select different action
Pooling PBE
Different types of the player select the same action
A Perfect Bayes Equilibrium in a game is
a list of strategies, one for each player,
and a list of beliefs, one for each information set of the less-
informed player,
such that the strategies are sequentially rational given the
beliefs about the types of players
and less-informed players update their beliefs using Bayes’
Rule whenever possible
Example 1: Perfect Bayes Equilibrium
Separating with NFGE:
q = 0
2’s best response is R
But if 2 plays R, then 1’s best response is NFNE
Hence not a PBE
Separating with GFNE:
q = 1
2’s best response is A
But if 2 plays A, then 1’s best response is GFGE
Hence not a PBE
Example 1: Perfect Bayes Equilibrium (continued)
Pooling with GFGE:
q = p
iff p ≥ 0.5
2’s best response is A if and only if p ≥ 0.5 and R when p ≤ 0.5

But if 2 plays R, then 1’s best response is NFNE, hence not a
PBE
If 2 plays A, then 1’s best response is GFGE
Hence when p ≥ 0.5, there is a PBE in which q = p and (GFGE,
A) is played
Example 1: Perfect Bayes Equilibrium (continued)
Pooling with NFNE:
Bayes’ rule cannot be applied
Start with any value of
1’s best response is NFNE only when 2 plays R
2’s best response is R if and only if q ≤ 0.5
Hence there is a PBE in which q ≤ 0.5 and (NFNE, R) is
played
Algorithm to find Perfect Bayes Equilibrium
Start with a pooling or separating strategy for player 1
Separating when the types of the player behave differently,
pooling when the types behave the same
If possible, calculate updated beliefs using Bayes’ Rule
If Bayes’ rule cannot be used (when denominator is zero), then
arbitrarily select an updated belief checking different potential
values using steps 3 and 4
Given the updated beliefs, calculate player 2’s optimal strategy
Check whether player 1’s strategy is a best response to player
2’s strategy. If so, you have a Perfect Bayes Equilibrium
Example 2

Is there a separating equilibrium?
Yes: (LH, OI) with q = 1
Is there a pooling equilibrium?
No
q
(1 – q)
Exercise 1
No
Yes: (LL, OI) with q = 0.4
q
(1 – q)
Exercise 2
No
No

Job Market Signaling (1973)
Education adds value
Prospective employers pay a premium for hiring a well-trained,
intelligent labor
Education has another important role in the marketplace
An academic degree is a sign of quality to the extent that highly
productive people may be more likely than less productive
people to attain higher degrees (gross generalization)
Degrees may serve as signaling mechanisms
Michael Spence
Example: Job Market Signaling
Education is costly
Cost for H is 4 and for L is 7
Yes: (NN’, CC’) with p = 1/3 and q ≤ 0.4
Example: Job Market Signaling continued…
Yes: (EN’, MC’) with p = 0 and q = 1
The only way for the high type to get the managerial job is to

signal her type by getting education
Exercise: Beer or Quiche Game
No
Does there exist a pooling equilibrium where the wimp drinks
beer as well?
Yes: (BB, NF) with m = 0.1 and n ≥ 0.5
m
(1 – m)
n
(1 – n)

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