How to enter a new market

4-1 - How to enter a new market

This weeks lecture covers:

1. Process of planning entry
2. Choice of markets - Market attractiveness
3. Choice of markets - Structural Entry Barriers
4. Choice of markets - Strategic Entry Barriers
5. Entry Strategies - Commitment
6. Entry Strategies - Judo Economics
7. Entry Deterrence - Structural Entry Barriers
8. Entry Deterrence - Limit Pricing
9. Entry Deterrence - Pre-emption

4-2. Choice of markets - Market attractiveness

1. Process of planning entry
2. Market attractiveness defined by Porter's Five Forces

1. Process of planning entry

1. Evaluate attractiveness of new market entry
2. Choose a market based on its attractiveness and entry barrier
3. Choose an entry type
4. Choose an entry strategy

Porter

1. More Competition = Less Attractive
2. Less Supplier = Less Attractive
3. Less Buyers = Less Attractive
4. More Entry Barriers = More Attractive
5. More Substitutes = Less Attractive

4-3 Choice of markets - Structural Entry Barriers

1. What are Entry Barriers
2. Structural Entry Barriers
3. Strategic Entry Barriers

2. Structural Entry Barrier

Control of essential resources - Control of resoures - DeBeers / diamonds - Control the Supplier capacity - Minnetonka / liquid soap - Patents: Sony and Philips / CD - Control the Distribution channel: Coca Cola / fast food chains - Control the location: Supermarkets - Control of Timing: Airlines / Arrival slots at airports - Rationing by government: Taxi cabs license; mobile phone spectrum

Economies of scale and scope - Minimum efficient scale: Semiconductor production or industries with large fixed costs - Cost advantages of incumbents thru experience of economies of scope: Airframe production - Doubling of production means 20% reduction in price - Reuse some parts i.e. economies of scope.

Marketing advantages of incumbents - Brand loyalty: Frequent flyer programmes - Switching costs: 1-year mobile phone contracts

4-4 Choice of markets - Strategic Entry Barriers

1. What are Strategic Entry Barriers

Considers deterring if 1. incumbent still earns profit 2. changes entrant's expectations

Potential Actions - Block Entry - Accommodate Entry

Decide by comparing the NPV of each option.

4-5 Entry Strategies - Commitment / Value Chain Reconfiguration

1. Commitment
2. Value Chain Reconfiguration
3. Judo Economics
4. Niche Markets

Types of commitment 1. High sunk cost investments - Production capacity - R&D - Advertising 2. Exit from other strategic market segments and focus on entry

Value Chain Reconfiguration - Innovators enter the market with inferior products. - Incumbents ignore the threat. - Over time the products improve and take large chunks of the market

Example: Bloomberg - basic financial data to small investment analysts and brokers - gradually improve data offerings and analysis

4-6 Entry Strategies - Judo Economics / Niche Market

1. Judo economics
2. Niche strategies

These strategies work when incumbents cannot retaliate because the cost of retaliation is much larger than the cost of accommodation

Consider an entrant with low price pL against an incumbent with high price pH. Entrant goes after a market xE out of a total market of x.

Incumbent does not retaliate if: pH xE < (pH - pL) x where left side represents cost of accommodation right side represents cost of retaliation

An example of judo economics working is Amazon vs. Barnes & Noble.

4-7 Entry Deterrence - Structural Entry Barriers - Commitment

Situation is that potential entrants are expected: 1. patent runs out 2. technology advances

Incumbent can either: 1. Block entry - costs money => lower profits 2. Accommodate entry - costs margins => lower profits

Raising Entry Barriers

Control of essential resources - Get exclusive access to essential resources - Control the supply route so it dries up

Economies of scale / scope - Lower costs through scale - Leverage experience advantage - Increase technological lead

Develop marketing advantages - Build brand loyalty - Raise switching costs for consumers - get people to buy complementary products - GO launcher and similar

Commitment - Commit to an aggresive behaviour after entry. - To eliminate moves that are profitable to the entrant

4-8 Entry Deterrence - Limit Pricing & Predatory Pricing

Both refer to aggresive pricing. Limit Pricing is before entry. Predatory is after entry has happened

Limit Pricing - Keep price low in spite of monopoly position - Signal to the potential entry - "low demand" (market may appear unattractive) - "low cost incumbent" (dangerous competitor) - Works only in presence of incomplete information

Example: Ferries and Eurotunnel

Predatory Pricing - Charging low prices (even below marginal costs) in the current competition - to induce exit - Works only in presence of incomplete information

Example: UK newspaper industry - Times vs Independent

4-9 Entry Deterrence - Pre-emption

Invest so that you can produce cheaply. - Over-investing - Pursue horizontal product differentiation - Choose locations of outlets more densely than optimal

Example - Coffeeshops at LSE

Prediction and The Many Model Thinker

This set of lectures cover:

1. Predictions
2. Diversity Prediction Theorem 
3. The Many Model Thinker

1. Predictions

We use categories to make sense of the world i.e. we "lump to live".

Categories for Predictions

We create categories.
Categories reduce variation (as measured by R-Squared).
The lower the variation, the better we get at predicting.
Different people create different categories.

Linear models for Predictions

Linear models assume a linear combination of components make up the whole.
The property of the whole can be predicted by knowing the property of the components.

2. Diversity Prediction Theorem

Relates the wisdom of the crowd to the wisdom of the individuals.
Crowd's accuracy depends on individual accuracy and crowd diversity.

Average individual errors are higher than average crowd error.
Diversity is the variation in prediction.

Diversity is the square of individual predictions from crowd average.

The Diversity Prediction Theorem states:

  Crowd's Error = Average Error - Diversity

  (c-v)^2 = 1/n * Sum(s[i] - v)^2 - 1/n * Sum(s[i] - c)^2

  where:
      v is true value
      s[i] is individual prediction
      c is crowd prediction
 The above is always true.

Read Wisdom of Crowds by Jim Surowiecki.

Wisdom of crowds come from reasonably smart people who are diverse.
Madness of crowds come from like-minded people who are all wrong.

3. The Many Model Thinker

Reason #1: Intelligent Citizen of the World

• Growth model - investing in capital to grow
• Solow Growth model - innovation matters
• Colonel Blotto Game - How adding new dimensions

Reason #2: Clearer Thinker

• Markov Models - history doesn't matter, some interventions don't help
• Tipping Points - difference between tipping points 
• Develop intuition of how things pan out out over time - different types of curves 
• Things aggregate differently - More is Different

Reason #3: Understand and Use Data

• Category Models & Linear Models
• Growth Models

Reason #4: Decide, Strategize, and Design

• Game Theory Models 
• Mechanism Design - design institutions and incentive structures
• Concept of Incentive Compatibility

 Understanding of how people behave

• Rational behaviour
• Psychological behaviour
• Rule-based behaviour

Learning and Replicator Dynamics

Replicator Dynamics

This lecture covers:

1. What is Replicator Dynamics
2. Fisher's Theorem
3. Fisher's Theorem vs Six Sigma

1. Replicator Dynamics

Tells us how a population changes/evolves over time as a function of payoffs and proportions.

    Set of types {1,2,3,...,N}

    Payoff of each type, Py[i]

    Proportion of each type, Pr[i]

Rational agents will choose the highest payoff.
Rule-based agents will copy someone else.

The Model

Define the weight of a strategy as:

     weight = Py[i] * Pr[i]  
This is reasonable because if Pr[i] = 0, then there the strategy cannot replicate.

The dynamics of the process is:

    Pr[t+1][i] = Pr[t][i] * Py[i] / Sum( Pr[t][i] * Py[i])

In other words, the proportion of a strategy in the next time step is the ratio of its weight over the sum of all weights.

Application of Replicator Dynamics

Shake-bow game.
Replicator dynamics also explains how it leads to an equilibrium in the shake-bow game.

SUV-Compact game
Replicator dynamics lead to sub-optimal Nash Equilibrium.

2. Fisher's Theorem

  "The change in average fitness due to selection will be proportional to the variance."

Consider the replicator dynamics in ecology:

    Set of types {1,2,3,...,N}

    Fitness of each type, Py[i]

    Proportion of each type, Pr[i]
The fitness wheel is a good metaphor where:

• the size of slice represents fitness
• the number of slices represent proportion

Fisher's Theorem is a combination of:

1. Model 1: There is no cardinal
2. Model 2: Rugged Landscape
3. Model 3: Replicator dynamics

The role of variation plays in adaptation.

Fisher's Theorem explain why you should be the worst musician in the best band.
For the same average, larger variation will result in greater gain or greater adaptation.

3. Variation or Six Sigma

Opposite Proverbs.
Models have assumptions but proverbs don't.

Context for Six Sigma
- Fixed Landscape (Equilibrium world)

Context for Fishers Fundamental Theorem
- Dynamic/Dancing Landscape (Cyclic, Random or Complex world).

Mechanism Design

Mechanism Design

Overview

Design better institutions - decide here are a set of actions people can take and the payoffs.

Two problems to overcome:

1. Hidden actions - can't see what people are doing
2. Hidden information - can't figure out information about people

Applied Mechanism Design

• Auctions
• Public Goods

Assume people are rational. And later on extend the model to account for psychological model of people and rules-based model of people.

Hidden Action and Hidden Information

We are designing incentive structures to induce people to take the right kinds of effort.

Hidden Action - you're an employer, how do you know if people put in the right amount of effort.
Also known as moral hazard problem.

The model:

• Action: effort = 0,1
• Outcome = {Good, Bad}
• Prob(Good|effort=1) = 1
• Prob(Good|effort=0) = p
• Cost effort =c

Incentive compatible - Makes sense to put in effort.

Comparative Statics - given a model, try and get some understanding of what happens when variables changes in value.

Auctions

Objective of seller is to get as much money as possible.
Types of Auctions: Ascending price, Second price, Sealed bid.

Roger Myerson developed a Nobel Prize-winning Theorem that with rational bidders, a wide class of auction mechanisms including Ascending price, Second price and Sealed bid produce identical outcomes i.e. the highest bidder wins and pays the price of the second highest bidder.

It doesn't matter which auction mechanism is used if all players are rational.

Clarke-Groves-Vickery Pivot Mechanism

Pay the marginal amount you'd have to contribute for the project to be viable.
However, this mechanism is not balanced i.e.the combined contribution may not be sufficient to pay for the public good.

Prisoners' Dilemma and Collective Action

 This lecture covers:

1. Prisoners' Dilemma - exploring the tension between cooperation and defection 
1. the tension is between individual preferences(defect) and socially preferred outcomes(coop)
2. they don't line up - Aristotle might have some insight into this - that the role of leadership is to remove tensions such as these.
2. Cooperation x7 - seven ways to get cooperation in a Prisoners' Dilemma
3. Collective Action Problems - Prisoners' Dilemma at scale
4. Common Pool Resource Problems - The No Panacea option.

The Prisoner's Dilemma

What is it

• Two Players
• Pareto Efficient - there's no way in which you make make every single person better off.
• Nash Equilibrium is DD

Where is it applied

• arms control, price competition, technological adoption, food sharing

People will tend to a bad outcome.

Cooperation x7

1. Repetition: Direct Reciprocity (Tit for Tat strategy)
2. Reputation: Indirect Reciprocity
3. Network Reciprocity
4. Group Selection
5. Kin Selection
6. Laws and prohibitions - e.g. illegal to talk on cell phones
7. Incentives - e.g. shovel sidewalk or get fined

Super Cooperators by Michael Novak.

Collective Action Problem / Free Rider Problem

Examples of Collective action Problem

1. Global Carbon Emissions
2. Fixing the flooding problem in the community (this is more of a Public Good problem)

An extension of the prisoners' dilemma problem where when I cooperate, lots of people benefit but when I defect, I will benefit alone.

The Model

Let Xj be the action of person j.
Xj is some amount of effort between 0 and 1 - how much we're contributing to the public good.

Payoff of j = -Xj + b*Sum(Xi | i from 1 to N)
b in (0,1)

Note that if b > 1, then we'll always contribute because Xj is 1 and regardless of what others do b*Xj is greater than 1.

Overconsumption followed by collapse.

Jared Diamond - Collapse

Common Pool Resource Problem

Examples of this include cows grazing in the commons, cod fishing or turkey hunting.

The Model

x[j] is amount consumed by j
X is total consumed
C is amount available

Amount Available Next Period:
    C[t+1] = (C[t] - X)^2


Solving Collective Action Problem and Common Pool Resource Problems

1. Particulars matter, for example:
1. Grazing in the Commons - the amount of grass is visible to all
2. OverFishing - the fish population is not visible so some form of monitoring is required
3. Upstream vs Downstream - focus more on upstream as they greatly influence outcomes

Eleanor Ostrom says particulars matter i.e. No Panacea 

Use PuTTY with AWS EC2 Instance

Steps

1. Start a new instance and save the corresponding private key file (.pem file)
2. Use PuTTYGen to convert .pem file to .ppk file
1. Load the .pem file
2. Save private key as .ppk file
3. Connect using PuTTY
1. Use the right EC2 hostname
2. Update Connection > SSH > Auth > Private Key File to point to the newly created .ppk file
3. Save the connection

Reference:

• http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/putty.html
• Get PuTTY here

Mapping changes over time

A fellow courserian mapped the decline of Monarch butterflies and it had several elements:

1. An external data source (in this case: Flickr) with geocoding
1. Photos are geotagged
2. A time line
1. 2008 to 2013
3. Good visualization 
• a heat map was used
4. A basemap
• Imagery was used I believe

If I'm running a hospital network and I have information such as aggregate patient location, I would be interested in seeing where patients are coming from i.e.

1. An internal data source with geocoding
• patients are located say via postal code
2. A timeline
• last 5 years
3. Good visualization
• a choropleth, or graduated circles
• or a heat map
4. A basemap 
• Either imagery or split by postal code or natural subdistrict

Beyond Model Thinking

 Next steps beyond model thinking

• Figure out entropy 
• Checkout - http://www.complexityexplorer.org/

Networks

Networks

Lectures cover:

• Logic
• what rules or organizations use to form connections?
• how it forms
• Structure
• what are the measures to compare networks?
• measures
• Function
• what properties emerges from the structure?
• what it does

Network Structure

• A set of nodes and edges
• edges can be undirected or directed
• Degree
• how many edges each node has on average
• node
• number of edges attached to a node
• network
• average degree of all nodes
• = 2 x Edges / Nodes
• neighbours of a node
• all other nodes connected by an edge to the node
• Theorem
• The average degree of neighbours of nodes will be at least as large as the average degree of the network
• i.e. Most people's friends are more popular than they are!
• Path Length
• definition
• Minimal number of edges that must be traversed to go from node A to node B
• Average Path Length
• Average path length between all pairs of nodes in a network
• how far it is from each node to another node

• Connectedness
• whether the entire graph is connected to itself
• definition
• A graph is connected if you can get from one node to any other
• Clustering Coefficient
• how tightly clustered are the edges
• definition
• percentage of triples of nodes that have edges between all three nodes
• What each measure tells us
• Degree
• Density of connections
• Social Capital
• A proxy for social capital
• Speed of Diffusion 
• How quickly information spreads
• Path Length
• # Flights Needed
• Social Distance
• Likelihood of information spreading
• unlikely to spread if path length is long
• Connectedness
• Markov Process - an essential precondition
• Terrorist Group Capabilities
• Connected groups are more capable
• Internet/Power Failure
• Information Isolation
• Disconnected people may not learn things
• Clustering Coefficient
• Redundancy/Robustness
• If there is a break, the network still works
• Social Capital
• Innovation adoption (triangles)
• How likely an innovation is likely adopted
• Picture = 1000 words

Network Logic

• Random attachment
• Connection procedure
• N nodes
• P probability two nodes connected
• Contextual Tipping Point
• For large N, the network almost always becomes connected when P > 1/(N-1)
• Small Worlds
• People have some percentage of "local" or "clique" friends and some percentage of random friends
• As people have more random friends, there's less clustering an shorter average path length
• Preferential Attachment
• Connection procedure:
• Node Arrives
• Probability connects to an existing node is proportional to the node's degree
• The degree distribution always results in a long tail
• A lot of nodes only have degree 1
• A handful of nodes with very high degree
• Results
• The exact network we get is path dependent
• The equilibrium degree distribution is not path dependent
• Always a long tail degree distribution

Network Function

• Micro decisions/processes when forming networks aggregates in emergent network properties
• Six Degrees
• Stanley Milgram & Duncan Watts
• Random Clique Network
• Formation Rules: Each person has
• C clique friends
• R random friends
• K-Neighbour
• All nodes that are of path length K to a node but not of any shorter path length
• Strength of weak ties

Path Dependence

Path Dependence

Lectures cover:

• What is Path Dependence?
• Construct Urn Models to understand
• different types of path dependence
• what causes path dependence
• difference between path dependent outcomes and path dependent equilibria
• Examples of Urn Models
• Polya Process, Balancing Process,
• Path dependencies is logically different from increasing returns
• Path dependencies are caused by externalities i.e. interdependencies between choices/decisions
• externalities with negative effects are more likely to be path dependent
• Compare path dependence and Markov Processes, Tipping Points and Chaos
• Key lessons - my own reflection of what this lecture means

Real-world examples:

• QWERTY typewriter keyboard
• increasing returns / virtuous cycle
• the more QWERTYs lead to more QWERTYs
• Technology
• AC vs DC
• Gasoline vs Electric Cars
• Common Law
• Influence of Precedent
• Institutional Choices
• Defined benefits
• Economic Success
• Ann Arbor vs Jackson
• Manifest Destiny of America
• Railroads 

 

Path Dependence

• Path Dependent
• Outcome probabilities depend upon the sequence of path outcomes.
• Doesn't necessarily determine, merely affects the probabilities
• What happens now depends on what happened along the path to get here
• Phat Dependent
• Outcome probabilities depend upon past outcomes outcomes but not their order
• Polya process is Phat

Urn Models

• Basic Urn Model
• Urn contains balls of various colors
• The outcome equals the color of the ball selected
• Bernoulli Model
• Fixed number of balls in the urn
• U = {B blue, R red}     ; U stands for Urn is a set of B blue and R red
• Process: Select ball and return to the urn
• P(red) = R/(B+R)
• Outcomes independent
• Polya Process
• U = {1 Blue, 1 Red}
• Process: 
• Select and return
• Add a new ball that is the same color as the ball selected
• Probabilities will change over time
• Result
• Any probability of red balls is an equilibrium and equally likely
• Any history of B blue and R red balls is equally likely
• Seeing just the set in the outcome doesn't tell you anything about the order (a Phat process) because any order is equally likely
• Example:
• Fashion: People buy leopard prints because there are more leopard prints
• Technology: People buy iPhones because they see iPhones
• Balancing Process
• U = {1 Blue, 1 Red}
• Process:
• Select and return
• Add a new ball that is the opposite color as the ball selected
• Result
• The balancing process converges to equal percentages of the two colors of balls
•  Examples
• Need to keep constituencies happy
• Selection of site for political conventions - northern or southern state
• Selection of site by Olympic committee - Asia, Europe, North America or South America
• Sway Process
•  U = {1 Blue, 1 Red}
• Process
• Select and return 
• In period t, add a ball of the same color as the selected ball and add 2^(t-s) - 2(t-s-1) of color chosen in each period s < t
• As you go back in time,the older events take on exponentially more weight over time
• Early movers have a bigger effect
• Distinguish between:
• Path Dependent Outcomes
• color of ball in a given period depends on the path
• Path Dependent Equilibrium
• percentage of red balls in long run depends on the path
• Outcomes and equilibrium
• Polya process: 
• Path-dependent outcomes
• Path-dependent equilibria
• Balancing process: 
• Path dependent outcomes
• Equilibria independent of path
• Examples of path-independent equilibria
• Manifest Destiny - America will stretch from sea to shining sea
• Railroads - Once railroads were invented, they will build themselves
• Mobile - Once invented, mobiles is the future

Path Dependence and Chaos

• Why is the difference between Path Dependence and Phat Dependence important
• Markov processes
• finite states
• fixed transition probabilities
• can get to any other state
• not simple cycle
• markov converges to a unique stochastic equilibrium
• Chaos
• Extreme Sensitivity to Initial Conditions (ESTIC)
• If initial points x and x' differ by even a tiny amount after many iterations of the outcome function, they differ by arbitraty amounts.
• Tent Map (an example of chaotic)
• x in (0,1)
• F(x) = 2x   if X <0.5,
•         = 2 - 2x if X > 0.5
• Tent Map is not path dependent
• Nothing happens along the way/path will change the end
• is deterministic
• Path dependence means what happens along the way has an impact on the outcome

Types of outcomes

• Independent
• Outcome doesn't depend on starting point or what happens along the way
• Chaotic
• Outcome depends on initial conditions
• Path dependent
• Outcome probabilities depend upon sequence of past outcomes
• Phat dependent 
• Outcome probabilities depend upon past outcomes but not their order

History is path dependent. The future is being written today.

• History/future is not independent i.e. what's happening now is happening regardless of what happened in the past. Independence means no structure.
• History/future is not chaotic i.e. initial conditions matter but it isn't the only thing that matter. Once we write the Constitution, the rest plays out deterministically
• History/future is path dependent not phat dependent because early events have a larger importance.

Path Dependence and Increasing Returns

• Increasing Returns
• More produces more
• Positive feedback / Virtuous cycles
• The more I have of something, the more I want the same thing 
• The the more other people do something, the more that other people will do it
• Example:
• The more people get QWERTY typewriters, the more people will get QWERTY typewriters
• Is increasing returns equivalent to path dependent equilibrium? No
• Increasing returns without path dependent equilibrium
• Example:
• Gas / Electric
• Always goes to equilibrium at Gas even though there is increasing returns
• Path dependent equilibrium without increasing returns
• Example
• Symbiots

• Path Dependencies comes from a different process - Externalities
• Externalities:
• interdependence between choices can create path dependence
• Decisions create externalities
• Externalities that big projects create path dependence
• Choosing Project A first results in choosing Project C next
• Choosing Project B first results in choosing Project D next
• Yi J's decision to migrate to Australia affects parents & Dai J

Path Dependence or Tipping Point

• Path Dependent Equilibrium
• percentage of red balls in the long run depends on the path
• Tipping points
• direct tips
• Comparison
• Tipping points
• a single instance in time where the long term equilibrium
• a singular event that tips the event abruptly
• diversity index - count of equilibria
• diversity index (uncertainly) is reduced abruptly
• entropy - how much information in the system
• Path Dependent Equilibrium
• accumulative effect of moving along the path
• diversity index reduces gradually
• unlike tipping point where diversity index (uncertainly) is reduced abruptly

Key Lessons

• Externalities are the reason choices we make in the past will affect choices we make in the future
• Pay more attention to choices with externalities e.g.
• Where you choose to live
• What line of work you choose to do
• What language you choose to learn
• Where possible make decisions where externalities are all positive 
• including future externalities
• this will reduce path dependencies i.e. keep your options open
• History matters when it changes transition probabilities
• Make decisions that increase transition probabilities to states you desire - to goal states
• Pay attention to externalities when making decision especially negative externalities
• Keep your options open

The Power of Complements

The Power of Complements

In this week's lecture:

• Examples and definition of complementary products
• The economic definition for complementary products
• Cross-price elasticity of complementary products
• How substitute goods can also be surprising complements
• Interesting strategies in markets with complementary products
• Supporting the supplier of a complementary product
• Produce the complements - Advantages and disadvantages
• Strategies for firms that produce the complements
• Cross subsidize complementary products
• Bundle complementary products
• Increase Lock-In
• How complementarity can help achieve cooperation among firms who might otherwise compete
• Competitors as Complementors
• How Strategic Partnerships foster coordination between firms
• Shared decision making
• Organisation Integration
• Economic Integration

Complementary Products

Examples of complementary products:

• Computers and Software
• Skis & Skiing Sticks
• Smartphones & Apps

 

Economic Definition

The utility increase when the products are used together.

• Two products A and B
• These are complements if B increases users' utility from A, and vice versa
• U(A+B) > U(A) + U(B)

Cross-Price Elasticity

• Two products A and B
• These are complements if the demand for B increases when the price of A drops, and vice versa
• This phenomenon is referred to as negative cross price elasticity
• Cross price elasticity is how the price of one product depend on the price of another products

Surprising Complements

• Substitute good can also have complementary effects
• A price cut of the substitute good decreases market share but increases the size of the market, resulting in a positive net effect
• Example: Cloth shops in the same mall
• Shop A's price cut makes the whole mall more attractive
• Shop B's sales increase due to the additional customers

Supporting the Supplier

• Enable the supplier in ways that increase sales of complement e.g.:
• Better quality of complement
• Higher sales of complement
• Example: Apple
• Gave laptops to students writing software for Mac OS
• More compatible software titles made Apple's laptops more attractive
• Example: Game console manufacturer 3DO
• Problem
• Console manufacturers sold consoles for high prices to make profits
• This attracted a limited number of customers only
• Neither optimal for console manufacturers not for game publishers
• Solution
• Publishers paid a fee of 3 dollars to 3DO for every game copy sold
• 3DO could sell consoles much cheaper and attract more customers
• This way, publishers could sell more copies and increase profits

Producing Complements

• It may make sense to produce the complement yourself
• Example
• Sony produces game console and video games
• Hewlett Packard manufactures printers and printing ink
• Contra
• Market for complements may simply be unattractive
• Complement may require competencies the firm lacks (production, R&D, management, ...)
• Prospective customers might be put off by a firm's dominant position
• Pro
• Better tailoring of complement to own product
• Quality control for complement
• Internalization of the positive effects (externalities) of the complement on own product
• Cross-Subsidies
• Bundling
• Increasing Lock-in

Cross Subsidies

• Idea
• Product A is sold at small margins (even loss) to increase sales of Product B (high margins)
• Advantage
• Increase profits through intelligent pricing
• Risks
• Consumers do not buy product B at all
• Consumers buy product B from another manufacturer
• Examples: 
• Razors and razor blades
• Printers and printer cartridges
• Mobile phones & operator contracts

Bundling

• Idea
• Firm sells product A and complement B combined as a package
• Advantage
• Little or no competition in market for A
• Decreased competition in market for B
• Risks
• Potential buyers of "A only" or "B only" are lost
• Bundling becomes standard and advantages of unbundling are overlooked
• e.g. How to differentiate and how to set prices
• Examples
• Game console and games 
• e.g. Wii bundle with console, game, steering wheel
• Operating system and web browser
• Flight and baggage handling

Increasing Lock-In

• Idea
• Users have switching costs when switching from A to a substitute
• The more complements (B, C, ...) to A they buy, the higher the switching cost
• Advantage
• Higher switching costs imply a higher value of the customer to the firm
• You can charge customers higher prices
• Examples
• Microsoft Office and MS Windows
• Games and video consoles

Competitors as Competitors

• In certain situations, firms may be
• Competitors in one part of the market
• Complementors in another part of the market
• In such a constellation, they may not compete that harshly
• Example: Music Players & Music Content
• Sony and Apple
• Competitors
• Both sell portable music players
• Complementors
• Sony Music provides music content for Apple's iPod / iPhone
• Apple's iTunes store is important sales platform for Sony's music content
• Example: Mobile Phone Calls
• Vodafone and T-Mobile
• Competitors
• Both sell mobile phone contracts
• Complementors
• Their networks are interlinked
• Customers join Vodafone because they know that they can also call T-Mobile customers , and vice versa.
• Parts of Vodafone's / T-Mobile's revenues come from these cross-network calls

Strategic Partnerships

• Drivers of Strategic Partnerships
• Producers of complementary goods depend on each other
• Helping each other and coordinating each other's behaviour maximizes the positive effects of complementarity
• In many cases, integration into one single company not feasible or not desired by involved firms
• Forming a strategic partnership is a powerful way of institutionalizing coordination and formalising interests
• Definition of Strategic Partnerships
• Relationships between firms
• Typical characteristics
• Shared decision making
• Organizational linkages & coordination mechanisms(organisational integration)
• Joint equity ownership (economic integration)
• Organisational Integration
• Teams across firms
• Established reporting and decision routines across firms
• Heavy exchange of information
• Economic Integration
• Direct cross ownership of equity
• Firm A holds X% of Firm B's assets and vice versa
• Setting up a new legal entity (joint venture)
• Benefits
• Alignment of interests
• Retention of control and exclusivity
• Feasibility of inter-organisational coordination
• Complementarity and Strategic Partnerships
• If firms produce complementary products, already high incentives to work together cooperatively
• Less need for economic integration to align interests

Beyond Creative Programming Course

Next steps after Creative Programming Course:

1. Develop Mandelbrot Surfer
2. Read the Nature of Code
3. Develop Shoot the Apple Game
4. Develop a site like http://funprogramming.org/ (they key being graduated exercises)
5. Checkout Processing.js on Windows 8

Social Sharing Programmatically

Sharing on Social media

• Tumblr: http://stackoverflow.com/questions/6169603/tumblr-sharer-url-override

A good source of information on Tumblr

• http://www.cherrybam.com/tumblr-tutorials.php

Request Features for Mandelbrot Surfer

Mandelbrot Surfer

Mandelbrot Surfer lets you explore the Mandelbrot Set and discover wonderful fractal images share them with your friends.

New Planned Features

1. "Record" button -- allows you to record the images as you explore and "Play" button to playback.
2. "Random Explore" mode -- randomly move in and out of various spots making a nice screen saver.
3. Toggle sound/music -- goes well with random explore mode.
4. Share your favourite images with your friends on Facebook / Twitter. (Done for Twitter)
5. Add other features you'd like to see in comments below.

How to mix Javascript and Processing JS

Since Processing code gets converted to JavaScript and run like any other function, all Processing code has access to the global object. This means that if you create a variable or function in a global script block, they are automatically accessible to Processing

See http://processingjs.org/articles/jsQuickStart.html#mixingjsandprocessing

Mandelbrot Surfer

Mandelbrot Surfer

Mandelbrot Surfer lets users explore the Mandelbrot Set and discover wonderful fractal images. Give it a try - you may discover some graphical gems! See image below for an example.

Functionality

1. Zoom in and out of the image by pressing on the zoom buttons
2. Change the colors by pressing on the color flower button
3. Center the image at point by pressing on the point of interest

Potential features 

Let me know which one you like to see first!

1. "Record" button -- allows you to record the images as you explore and "Play" button to playback.
2. "Random Explore" mode -- randomly move in and out of various spots making a nice screen saver.
3. Toggle sound/music -- goes well with random explore mode.
4. Share a gem -- Share your favourite images on Facebook/Twitter with your friends.
5. What other features would you like to have?

Sample images from Mandelbrot Surfer:

Lyapunov Functions, Externalities and Langton's Lambda

Externalities

An externality in an exchange market is an action by one party that materially affects the happiness of someone who is not directly a party to the action.

Externalities make systems churn - less likely to reach equilibrium. Examples include:

1. Arms Trading
• Participants are happy but other countries are unhappy (externality)
2. Political Coalitions
• When Party A merges Party B, Party C may be upsed
3. Mergers
• When two firms merge, other firms are less profitable, less secure
4. Alliances
• When two countries make alliances, other countries are less happy, feeling less secure 

 Systems with externalities affect the Lyapunov Function of other parties which changes their behaviour which in turn may change our behaviour and it generally leads to churn.

Chris Langton's Lambda Parameter

1. Systems where behaviour isn't  influenced by others tend to go to equilibrium
2. Systems where behaviour and actions are influenced by others then to be complex

Lyapunov Functions in Exchange Markets

Exchange Markets

1. People bring stuff - some bring fish, some bring vegetables, some bring money
2. People exchange stuff (trade)

Assumptions

1. Each person brings a wagon of stuff
2. People trade with others but only if each gets an increase in happiness by some amt K 

Lyapunov Function

• Lyapunov Function
• Sum of total happiness
• Maximum
• There is a maximum happiness - when everyone gets what they want
• Each move increases value of Lyapunov Function
• Each trade increases total happiness

Four states of any system

The four states

• Equilibrium
• Cycle
• Random
• Complex

Equilibrium State

• Lyapunov Function: If a Lyapunov Function can be constructed for a system, the system will go to equilibrium 
• Markov Process: If it's a Markov Process, it will reach stochastic equilibrium

Cooperation

Why do companies cooperate?

• Sometimes competition is not in the companies' best interests
• i.e in a Prisoner's Dilemma
• Sometimes companies need each other

What are the mechanisms to achieve cooperation between individual profit maximizers?

• Repeated games
• Commitment

The Instaspam Week

Capabilities to develop

1. Accessing the camera on mobile phones
2. Access Facebook API from within Processing
3. Use App GUI interface 
4. Understand PHP and Javascript integration challenges

Gist of the Instaspam design

1. Take a picture: Provide a button to capture images from files or cameras
1. Add a Button to the sketch
2. Link the selectFile() function in insta.js to the button
2. Manipulate the image: Provide more buttons to manipulate the image
3. Push image to Facebook: Provide a button to push an image to Facebook in a 2-step process
1. Upload image in Processing canvas to a file in the cloud (using PHP or something similar)
2. Push uploaded file to Facebook

Create a Facebook App for Integration

1. Signup as a facebook developer
2. Go to https://developers.facebook.com/apps
3. Create a new app
• You need own a web site (a place you can upload images)
4. Get Facebook appId and store into insta.js
5. Save the channel.php file on your webspace

Challenges

1. Image manipulation on mobile phones can be slow for large images

 Image Effects

• Applying tint filter. Tints are useful for:
• turning a black and white photograph into an old style sepia image
• making all of the images in your app have a consistent color scheme
• Apply an overlay. Overlays are useful for:
• adding a picture frame to your image
• creating a mist or fog effect
• Use Buttons:
• Button b = new Button("name",x,y,w,h);
• b.setImage(img_active); 
• b.setImage(img_inactive); 
• b.display();   // in draw()
• b.mouseReleased();   // in mouseReleased()
• Use RadioButtons
• Use showGUI variable to display buttons only if showGUI is true
• increases screen space

Image Processing

• insta.js - allows Processing to access cameras on mobile phones
• edit index.html
• <body onload="setupFileListener()">
• <script src="insta.js"></script>
• <input type="file" name="file" id="file"/>
• assign the selectFile() function to a button
• the function will capture from the camera
• 
  
• 
  

Notes

• alpha = opacity (255: opaque, 0: transparent)

How to publish Processing sketches on iOS app store

Steps to release apps on iOS app store

1.  get an iOS developer account. 
2. use Phonegap to make your JavaScript-based processing sketches into full iOS apps
• check out this simple tutorial video on using phonegap.

Beyond Creative Programming

Next Steps

1. Study Oli Roberts Network Graph to figure out how it works
• Program | Code

Notable Works

Notable works

• David Moreno's Tank Game
• Oli Roberts Image upload app
• Especially like the way all his experiments are laid out
• Claire Teegardin's Monsterpaint
• http://www.youtube.com/watch?v=N2R8YDAJUbc
• http://www.youtube.com/watch?v=sw832Uhx6fY

Running SSH on Windows

Setting up SSH on Windows for GitHub

1. Create a ".ssh" folder in your home directory.
2. Use Puttygen.exe to create ssh-rsa keys.
3. Save the public key as %HOME%/.ssh/id_rsa.pub
4. Save the private key as %HOME%/.ssh/id_rsa
5. Edit the id_rsa.pub file by removing the comment and replacing with "ssh-rsa". Make sure it is one single line. It should look like:
• ssh-rsa AAABSLDFEISELslghkjsesoi ...... yourname@email.com
6. Login to GitHub and add the contents of id_rsa.pub as one of the security keys.
7. 
   

See this site on instructions on how to use Puttygen.exe to create keys.

Why use Twitter Bootstrap

Use Tools

1. Use Chrome's User-Agent Switcher to spoof your user agent
• Make the spoof permanent
2. Use Chrome's Resolution Test to quickly switch resolution of browser
• Add iPhone resolutions using options
3.  Use Twitter Bootstrap and CSS to quickly style a website to look good on mobile
1. See documentation on examples and components

Beyond Machine Learning

Next steps:

1. Competitive Data Mining with R and Python (Video 19:30)
2. Checkout ML toolkit tutorials here.

List of Courses

Courses In-Progress

1. Startup Engineering
2. Model Thinking 
3. Introduction to Finance
4. Maps and the Geospatial Revolution 
5. Competitive Strategy 
6. Creative Programming for Digital Media & Mobile Apps 
7. Introduction to Systematic Program Design - Part 1

Courses Completed

1. Machine Learning
2. An Introduction to Operations Management 
3. Introduction to Data Science

Lyapunov Functions - Scary Russian Function Not!

What's a Lyapunov function

The existence of a Lyapunov function for a model or system means that the system will hit equilibrium.

The elements of such a system include:

1. A maximum
2. A Lyapunov function
3. A property such that if something moves, it will always increase by k

How do cities self-organize such that queues are reasonable?

Answer: A Lyapunov function exists that explains how the city moves to equilibrium.

Applications of Lyapunov function

List of applications

1. Self-organizing cities
2. Exchange Economies
3. Arms trading

Online GIS tools

Closed source:

• ArcGIS by ESRI

Open source

• http://live.osgeo.org/

Why/How to do Market Sizing, Product Tier and Product Pricing

Why do market sizing?

The only thing that matters is the market:

In a great market -- a market with lots of real potential customers -- the market pulls product out of the startup. ~Marc Andreessen

How to do market sizing?

Top-down estimation

•  Use Fermi estimates
• The secrets of market sizing

Bottom-up estimation

• Use Google Finance or Yahoo Finance
• Use Google's Keyword Planner
• Use Facebook's Advertiser Tools

Product Tier and Pricing

1. Establish a market before building a minimum viable product
2. Develop a simple landing page
• Use Launchrock service
• Use photos from iStockphoto
• Use icons from iconfinder
• Use some basic SEO
3. Determine product tiers
1. Read Kickstarter product tiers
2. Read Kickstarter trends
3. Use the product tier selection process
4.  
4. Review what Joel Spolsky has to say

 

The Idea Maze and Execution Mindset

The idea state machine

Think of the idea state machine above as a recipe that tells you what to do next.

How to develop a good idea?

1. Develop a bird's eye view of the idea maze
• understand all the permutations of the idea and the branching of the decision tree
• gaming things out to the end of each scenario
2. Develop an obsession with the market / the idea maze
3. Study historical players that failed or succeeded in the maze
4. Think deeply about others who have gone before you and failed or succeeded 
5. Explain how you are different or you will navigate it better

The Execution Mindset

The execution mindset is in two parts:

1. do the next thing on the todo list
2. rewrite the list in response to progress

The list is essentially the plan - stay focused on what needs to happen and be flexible to "rewrite" the list in response to progress.

 References

• Startup Engineering Lecture 5

Startup Ideas

Startup Ideas

Context-aware applications - like Google Glass.
A personal collection of recipes, templates, models, examples and where to learn that are accessible:

1. Frequently Used Recipes, Templates, Practices
2. Financial models tells us what kind of information to collect
3. Code recipes or process recipes tells us what to do next
4. Templates gives us a quick structure
5. Model thinking models tells us what information to collect, which models to use
6. Examples to illustrate how 
7. Value - Productivity, better decision
8. Eventually available on Google Glass

Theoretical Underpinnings: Knowledge is constructed.

Three Steps to creating a truly valuable tech company

There are three steps to creating a truly valuable tech company. 

1. First, you want to find, create, or discover a new market. 
• start with a small, specific market 
• focus on getting to product/market fit
2. Second, you monopolize that market
•  through some combination of brand, scale cost advantages, network effects, or proprietary technology
3. Then you figure out how to expand that monopoly over time. 

Reference:

• http://blakemasters.com/post/21169325300/peter-thiels-cs183-startup-class-4-notes-essay
• http://www.ashmaurya.com/2009/11/achievingproductmarketfit/

Beyond Startup Engineering

Next Steps:

1. Review awesome essays in Peter Thiel's CS183: Startup Course.
2. Review the pmarca guide to startups
3. Follow the pmarca blog

Technical

1. Get a Javascript workout
2. Get examples of node.js | jquery.js
3. To to Startup School 

Beyond Systematic Program Design

Next steps beyond Systematic Program Design include:

1. How to Design Programs, HtDP
2. Structure and Interpretation of Computer Programs, SICP
3. Concepts, Techniques, and Models of Programming , CTM

References:

• How to Design Programs
• Structure and Interpretation of Computer Programs
• Concepts, Techniques, and Models of Programming

On Systematic Program Design

Key lessons from this course include:

1. Recipes are extremely useful
2. Templates are extremely useful
3. Knowing what needs to be done NOW is extremely valuable
• It increases productivity tremendously
• Recipes and templates are what makes this possible 
4. Data design drives function design drives implementation
5. Being systematic comes from using good recipes and templates

Key programming practices:

1. Understand the information domain clearly
1. Draw diagrams
2. Write out examples
2. Map the information domain to data domain
3. Use check-expects / unit tests
4. Use wishes to track work outstanding

Key tools:

1. Recipes
2. Templates

Data Definition for Tree Structures Using Mutual Recursion

A tree of arbitrary width and depth is defined using mutual recursion.
The figure below shows the mutual reference in the data definition and the corresponding function template.

Also contrast a tree with a list. A tree has arbitrary width and depth which a list has arbitrary length.

Decomposing Functions

When to decompose functions?

1. Function composition:  A function should be split into a function composition when it performs two or more distinct and complete operations on the consumed data.
• define arrange-images as a function composition of sort-images and layout-images helper functions.
2. Operating on a list: When an expression must operate on a list -- and go arbitrarily far into that list -- then it should call a helper function to do that.
• use the insert-images helper function so that the (first loi) expression can operate on the (rest loi) list, going arbitrarily far into that list as needed.
3. Domain knowledge shift: When the body of a function must shift to a new knowledge domain, it should call a helper function to do the work in the new domain.
• use the (larger? img1 img2) helper function when shifting from the domain knowledge of inserting images to domain knowledge of determining size of images.

Two Crucial Considerations for a Startup

Crucial Considerations

Two Crucial Considerations for a Startup:
1. Market Size
2. Time to Market

Market Size

Look at the market leaders for an estimate of market size.

• Apple for smartphones/tablet market
• ExxonMobil for Oil and Gas market
• Facebook for social media advertising
• Illumina for hardware sequencing
• Google for search advertising

Time to Market

A faster time to market beats all other considerations, other than market size.
Because of the effect of compounding.

Cash Flows: Important Principles

Estimate all cash flows on an incremental basis

Create a cash flow timeline, A, for the firm without the project.
Create a cash flow timeline, B, for the firm with the project.
The project value is the incremental cash flows generated i.e. cash flows of B-A.

Do not forget the importance of year 0 and the last year of the chosen timeline for the project

In year 0, Capital Expenditures and Working Capital will be incurred.
Working Capital typically includes Cash, Inventory, AR and AP
At the end of the project, there's terminal value or sale of inventory.

Account Issues are Important

Depreciation is the main issue because it's made up - And similar non-cash items.
Capital: a) Capex and b) Working Capital - Think about changes.

Do not mix financing with operations

Stay on the asset side. Value is generated on the asset side.
When you are doing project analysis, don't worry about financing for two reasons:

1. Money is generated by your ideas, not by financing
2. When you are discounting your cash flow, you are taking financing into account
3. Your ideas generate cash flows and financing just divides up the cash flows

Include effects of inflation/deflation

When projecting prices, take into account inflation or deflation.
Inflation is always in the discount rate, r.

Do not compare projects with unequal lives

Cash Flows from Project/Operations

Cash Flows from Project/Operations

  Revenues
- Costs of Goods Sold
- Selling, General & Admin. costs
- Depreciation
--------------------------------------
= Operating Profits
- Cash Taxes on Operating Profit
--------------------------------------
= Net Operating Profits After Tax
+ Depreciation
- Capital Expenditures
- Increases in Working Capital
--------------------------------------
= Cash Flows from Operations

Depreciation: Think of depreciation as the main non-cash item that has tax implications.
Working Capital = Cash + Inventory + Acct Receivable - Acct Payable

The dangers of using IRR

Value creation is most important when it impacts a lot of people and when it lasts a long time. And IRR has a bias for small, low-impact and short-term projects.

 IRR has a short-term bias

Be careful when using IRR. Do not compare IRR of one project with another project because they are internal rates.

All value is relative.
Instead of comparing IRR percentages to each other, compare the IRR percentages to a benchmark - the cost of capital outside.

IRR calculations favors projects with earlier cash flows - IRR has a short-term bias.
IRR is myopic in that it favours shorter term returns.

Be wary when IRR is used as the decision-making criteria - it indicates a myopic point of view.

Example Calculation

Consider Projects A and Project B:

• Project A Cash flows: [ -2000, 400, 2400 ]  -  IRR = 20%
• Project B Cash flows: [ -2000, 2000, 625 ]  -  IRR = 25%

Try computing the NPV of the two projects for discount rate of 5%, 11% and 20%.
Also, try drawing the graph of NPV vs r for the two projects.

IRR has a small-investment bias

Consider Project A and Project B:

• Project A Cash flows: [ -5000, 7500 ]  -  IRR = 50%
• Project B Cash flows: [ -50,000, 62500 ]  -  IRR = 25%