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