Download Managerial Economics

Managerial Economics
Economics of Strategy
and Games
Decoding Strategy
Patrick McNutt
Follow @tuncnunc
www.patrickmcnutt.com
Abridged ©
Lesson Plan I for Workshop Sessions
• Day 1 & 2: Introduce ‘the Manchester method’ as applied to
the game theory and TCE perspective in a search for a realtime understanding of management as player type, marketas-a-game and patterns and predictions.
Allocation of Case-Work: Cases Selected at Workshop for
Real-Time ‘Live’ Cases
• Day 2 and 3: Introduce relevant economics and game theory
material – from interdependence to Prisoners’ dilemma.
• Day 3: Group workshop presentation in the afternoon.
Why the game theory focus? Real companies
at the frontier of economic analysis…..
• Understand management as ‘they are’ not as theory
‘assumed them’ to be
• Management can be ranked (by type) and are faced with
indifference trade-offs => something must come ‘top of the
menu’: the 3rd variable or z. Trade off (x, y) to max z.
• Firms are conduits of information flows (vertical chain)
• Supply chain capacity constraints and technology-lag
• Reducing price does not necessarily lead to an increase in
revenues (elasticity)
• Prices are primarily signals (observed behaviour)
• Companies understand the competitive threat as
(recognised) interdependence (zero-sum and entropy)
• Predicting competitor reaction: price and entry strategies.
Workshop Lesson plan II
•
•
•
•
•
•
Learning Plan is to follow Besanko’s Economics of Strategy 6th
Edition ..selected Chapters and cases/examples.
Day 1 : Synopsis of E-Tutorials and Revision of Chapters 3 and 4
(Vertical Boundaries of the Firm, Agency and Co-ordination) and
Introduce Chapter 2 (Economies of Scale and Scope)
Day 1: Game Dimension and Introduce Workshop Case Analysis
Day 1: Introduction and setting the scene using McNutt’s
Decoding Strategy 2nd Edition Chapters 1 to 9 as required
Day 2 & 3: Focus on Besanko Part II: Chapters 2,3,5,6,7 and 8 and
link into Units 3 and 4.
From behavioural analysis to understanding and application of
extensive form and normal form games.
Planned Workshop Focus
• What is game theory about? Relevance to MBA learning.
• Management as a player with type and relevance of TCE:
Unit 1. Besanko Ch 3 & 4 and 5, McNutt Ch 1
• Cost leadership and economics of capacity: Unit 2. Besanko
Ch 2 and McNutt Ch 5
• Dynamic price games, entry deterrence, market structure,
oligopoly, signaling & Nash payoffs: Units 3 & 4. Besanko Ch
5,6,7 and 8 and McNutt Ch 6-9.
• Patterns and Real Time case Analysis…go to Appendix in
Decoding Strategy text.
What is game theory about?
Visit www.patrickmcnutt.com
• Observed behaviour (inductive) in a game, G.
• Identify the players in the game and the player’s type.
Finding the patterns in rival behaviour.
• Game => information on opponent type, recognised
interdependence, action-reaction, belief systems.
• Payoff depends on what each player believes about the
other.. Updating belief systems.
• What is a player’s true payoff? Independence v
interdependence; one-shot v repeated play.
• Consumers’ preferences as technology in a game.
Decoding Strategy & Pattern
Sequencing
Complete knowledge on the type and complete
information of the identity of a near rival:
Actionyou -> Reactionnear-rival ->…
..-> Reactions……NashReplyyou…..
Strategy defined in terms of an equilibrium: how
well either player does in a game depends on what
each player believes the other player will do.
Example A: What is type?
If you believe it to be true that Leo the Liar will never
tell the truth, how do you respond to his helping hand as
you cling for your life over the precipice of a cliff? Do
you ignore his helping? Do you rely instead on the many
apps on your smartphone, so tightly grasped in your other
hand, trying to make contact with your best friend to
come and rescue you?
Define Strategy
Cooperation arises in this instance if you and Leo as
players in a game can infer from past behaviour that
both of you are likely to be trustworthy. Leo may forgo
the short term gain of keeping to type for the long term
benefit of your friendship. He rescues you from the
cliff. You, however, will use the experience in order to
determine whether or not to believe Leo in the future.
Example B:
Player’s belief system
Your company’s strategy is s1: delayed launch of a new
innovative product for 2 years. Rumors do appear of an
impending launch date. You do not deny such rumors. In the
interim, an article appears a reputable trade journal
reporting that a not dissimilar product is about to be
launched by your competitor in the next few weeks.
Define Strategy
Do you stop and think about s1?
Do you reshape your strategy to s2: launch the product as
soon as possible?
Costs of not being a Player
• Agency costs can accrue across the shareholders (esp
institutional & activist shareholders)..changing CEOs.
• Bounded rationality and opportunity costs with trade-offs
• Make or Buy dilemma
• First Mover Advantage (FMA) v Second Mover Advantage
(SMA)
• Play to win v Play not to lose!
• Follower status ‘behind the curve’
• Technology lag and failure to differentiate ‘fast enough’ to
sustain a competitive advantage
• Near rival will try to minimise your gains by playing a
minimax strategy
Payoffs reflect preference order.
Guaranteed a 2 but there is an elusive 3
What if? Strategy I: cooperate
What if? Strategy II: compete.
Then if I is the consensus……..?
Strategy I
Strategy II
Strategy I
2,2
0,3
Strategy II
3,0
1,1
Unit 1: Why the emphasis on behaviour
(of players)?
• The Firm as a ‘nexus of contracts’
• Vertical chains and agency costs
• Make-buy dilemma & incomplete contracting
=> embedded patterns of behaviour
• Type of management & Bounded rationality
• Shareholders-as-principals and management-as-agent..
• The industry as a ‘market-as-a-game’
=> players with a playbook
TCE Co-ordination
•
Coase asked in ‘ The Nature of Firms’ in 1937:
Why are not all
economic
transactions
coordinated by
markets?
•
•
•
When transaction costs are
too high, exchange to be
coordinated by
organisations
Transaction costs: costs of negotiating, monitoring and enforcing contracts.
Behavioural assumptions: bounded rationality & opportunism.
The relative cost of organising transaction through different forms of governance
determined by:
• Extent to which complete contracts are possible. Where contract refers to
agreement between two parties which could be explicit or not.
• Extent to which there is a threat of opportunism by parties in the transaction.
• Asset specificity in the transaction & frequency with which the transaction is
repeated.
Storybook p.12
KPIs & Management Models
• What is the 3rd variable, Z? Any KPI = Z
by trial-and-error or player type reveals Z.
• Premise: knowledge on the type and
information of the identity of a near rival
translates into a Penrose effect with
Bounded Rationality
• Go to Table 1.2 pp21 McNutt Decoding
Strategy: Compare with Next Slide where
we add in Williamson/TCE.
Maximising Market Share:
Table 1.1 p9 McNutt
• Recognise zero sum constaint and entropy
(redistribution within market shares)
• Market Shares (before): 40+30+20+10
• Zero-sum (after): 30+40+20+10
• Entropy (after): 30+35+25+10
• Hypothesis: Iff {∆qi/∆Q} > 0 market
exhibits non-price competition:
• Check {∆qNOKIA/∆QSmartphones} < 0
Total Cost
£
Total Revenue
Min Profit Constraint
Output
Sales driven beyond the point
of max profit but within the
minimum profit constraint
Profit/Loss
Precis on a Marris model…
• McNutt Ch 4: Understand balanced
equation of ‘trade-offs’ to identify
parameters of profitability = next slide
• Supply of capital: debt v equity
• Demand for capital: R&D exp v dividends
• Instrumental variables influencing growth
– visit Diageo case in Kaelo v2.0
• KFIs: profits/output and output/capital
• Tobins q and Marris v ratio
Marris equations/dividends
paradox
• http://www.patrickmcnutt.com/news/activist-shareholderstobins-q-marris-v/
• Calculating share price by DCF formula
• P = eps/r : Static firm no growth opportunities
• P = eps/r + PV(GO): Dynamic firm with growth
opportunities…this is a Marris firm
• Common denominator is the plough-back ratio:
(PBR) = {1 – divs/eps}.This is a Marris equation
• More dividends can signal an absence of R&D growth
• Fig 4,2 pp58 - more R&D from G1 to G2 can accrue an
agency cost as Bayesian shareholders SELL as value falls V1
to V2.
Unit 2: Cost leadership [CL]
as a type (of player)
• Profitabiltiy v scale and (size and scope)
• Production as a Cost-volume constraint
• Understanding the economcis of
productivity as exemplar for incentives
• Normalisation equation
• Sources of Cost Efficiency [next slide]
• Cost leadership 5 Steps Checklist..McNutt p78
Sources of cost efficiency
• Measure of the level of resources needed to
create given level of value
Capacity utilisation
How much to produce given capital size?
Other
Economies of scale
X-inefficiencies, location, timing, external
environment, organisation discretionary policies
How big should the scale of the operation be?
Transaction costs
Production-cost
relationship
Economies of scope
Which are the vertical boundaries of the firm?
What product varieties to produce?
Learning and experience
factors
How long to produce for?
MES Point: Production - demand - production
to attain cost leadership
£
SAC1
SAC
2
Lower per unit
cost for more units sold
SAC
3
LAC
Av.Cost = marginal cost
0,0
q1
qt
Current plan of plant
closures to lower cost
base not completed
q
2
Q
Why? Capacity Constraints:
• Case A: Unexhausted economies of scale due to
product differentiation
• Case B: Firm-as-a-player does not produce large
enough output to reach MES
• Case C: Firm-as-a-player restraints production
(deliberate intent)..McNutt’s dilemma as
production drives demand…(Veblen monopoly type)
• Convergence of technology increases the firmspecific risk of Case C:
• Strategic Choice A or B or C?
Unit 3: Game type and
signalling
• Decisions are interpreted as signals
• Observed patterns and Critical Time Line
(CTLs). Go to Appendix in McNutt
• Recognition of market interdependence
(zero-sum and entropy)
• Price as a signal v Baumol model of TR max
• Scale and size: cost leadership
• Dividends as signals in a Marris model
Oligopoly,Games & T/3 Framework
•
•
Study of strategic interactions: how firms adopt alternative strategies
by taking into account rival behaviour
Structured and logical method of considering strategic situations. It
makes possible breaking down a competitive situation into its key
elements and analysing the dynamics between the players.
•
Key elements:
• Players. Company or manager.
• Strategies.
• Payoffs
•
Equilibrium. Every player plays her best strategy given the strategies
of the other players.
Objective. To explore oligopolistic industries from a game embedded
strategy (GEMS) perspective.
The use of T/3 framework, which considers 3 key dimensions (Type,
Technology & Time), will allow players to better predict the likely
strategic response of competitors when analysing rival competition.
•
•
Player Types I
Baumol type: player in a Bertrand game
who will reduce price if demand is
elastic.
CL type: in Cournot capacity game we
have a cost-leader type, CL, with reserve
capacity.
Incumbent and entrant: In the
geography the incumbent already exists
in the geography and the entrant is
intent on entering or presents a threat
of entry (contestable market).
Dominant incumbent is a player with at
least 40% of the market share. Often
linked with Stackelberg or ‘top-dog’ in
Besanko.
Bridging Unit 1 with Units 3
& 4: Game analysis
• Binary reaction;
Will Player B
react? Yes or No?
• If YES, decision
may be parked
• If NO, decision
proceeds on error
• Surprise
• Non-binary
reaction: Player B
will react.
Probability = x%
• Decision taking on
conjecture of likely
reaction
• No Surprise
The competitive threat!
• Traditional Analysis is focused on
answering this question for Company
X:
what market are we in and how can we
do better?
• Economics of strategy (T/3) asks:
what market should we be in?
Describe (prices as
signals) game dimension
• Players and type of players
• Prices interpreted as signals
• Understand (price) elasticity of demand and
cross-price elasticity
• Patterns of observed behaviour
• Leader-follower as knowledge
• Accommodation v entry deterrence
• Reaction, signalling and Nash equilibrium: ‘best you
can do, given reaction of competitor’
Perfect market: perfect
competition
• Defining a perfect market as follows:
If ΔPi increases, then the firm’s output = 0
or rivals follow the price increase.
• In a perfect market price differences cannot
persist across time
• Perfect competition = perfect market + near
rivals
So perfect market ≠> perfect competition but
perfect competition => perfect market
Player Types II
Extant incumbent: An incumbent that
has survived a negative event such as a
price war of a failed innovation or
technology-lag.
De novo entrant: An entrant intent on
entering – the incumbents can observe
plant building or product launch.
Potential entrant: An entrant that
presents a threat of entry into a game
through signalling with noise or
‘moonshot’ or planned capacity building
in another game [with economies of
scope).
Stackelberg type: A price leader in a
Bertrand game moving first in the belief
that others will follow or in the
knowledge that other are disciplined
(often linked to collusive behaviour).
•
•
•
•
•
•
Entry Deterrent Strategy &
Barriers to entry
Reputation of the incumbents
Capacity building
Entry function of the entrant
De novo and entry at time period t
Potential entrant - forces reaction at
time period t from incumbent
Coogan’s bluff strategy (classic poker
strategy) and enter the game.
Game Strategy
•
•
•
•
•
•
•
•
Nash premise: Action, Reaction and CV matrix
Non-cooperative sequential (dynamic) games
TR Test McNutt pp48..one-shot move
Limit price [to avoid entry] and predatory pricing
to force exit.
Near rival plays Minimax, so I play Maximin [focus
on my worst minimum payoff and try to maximise].
Segmentation strategy to obtain FMA
Relevance of ‘chain-store’ tumbling price paradox
Dark Strategy and 3 Mistakes in McNutt pp117118
Game Dimension
• Constructing an action-reaction sequence of
moves in search for a pattern.
• Non-cooperative sequential (dynamic) games
• Normal form game dimension with payoff
matrices, wherein payoffs reflect preference
order.
• Dominant strategy, Prisoners’ dilemma, Nash
equilibrium.
• Extensive form game dimension with decision tree
and backward induction
Limit Pricing Model in Besanko
pp207-211 and McNutt pp85-88
• Outline the game dimension: dominant
incumbents v camouflaged entrant type
• Define strategy set for incumbents:
commitment and punishment
• Allow entry and define the equilibrium
• Extensive form preference - entry
deterrent strategy v accommodation [next
slide]
CLASS EXERCISE
Strategy Profile - Fid the Nash Trap
Observations and Intelligence
In the decision tree narrative there is
no other firm to compete with in this
game – it is the incumbent v entrant.
But if the entrant does not enter, fight
and accommodate yield the same payoffs
to both players
Hypothesis 1
If the entrant does not enter, it does
not matter what the incumbent chooses
to do.
Hypothesis 2
The incumbent will not lower prices if
the entrant does not enter.
0,10
Do Not Enter
1
Agressive
-7,2
Enter
2
Accommodating
5,8
CLASS EXERCISE QUESTIONS
A. Convert the decision tree into a
normal form payoff matrix.
B. Find the Nash equilibria
C. Repeat A and B on the credible threat
of entry from a spherical competitor
[check pp173-175 in McNutt Decoding
Strategy]
D. Results in A+B+C written up as an Aide
Memoire for management.
E. Strategy Profile = Aide Memoire
Nash Equilibria & Prisoners’
Dilemma
•
•
•
•
Define the Nash equilibria [next slide]
Analyse the Payoff matrix
Apply The Thief of Nature Handout
Commitment and chat: one-shot and
repeated play
• Punishment ‘grim’ strategy
Payoffs reflect preference order.
Guaranteed a 2 but there is an elusive 3
What if? Strategy I: cooperate
What if? Strategy II: compete: price war.
Then if I is the consensus……credible?
Strategy I
Strategy II
Strategy I
2,2
0,3
Strategy II
3,0
1,1
Continuing with Unit 4:
Define a price war
• Determine the Bertrand reaction function:
• Besanko Fig 5.3 pp190 and McNutt Fig 9.4
p143
• Compute a Critical Time Line (CTL)from
observed signals..Examples of CTL in
McNutt in the Appendix
• Find a price point of intersection
• Case Analysis of Sony v Microsoft at
McNutt pp 141-144 and also in Kaelo v2.0
PATTERN – 2000-2006
PS2
launched
at $299
PS2 at
$199.99
PS2 at
$179.99
14 May 02 13 May 03
26 Oct 00
PS2 at
$149.99
11 May 04
100 million
PS2
shipped
Announcement
PS3 production
schedule to ship 6
million units by 31
Mar 07 at $499
PS2 at
$129.99
20 April 06
1 Nov 05
8 May 06
15 Nov 01
15 May 02 14 May 03
29 Mar 04
22 Nov 05
22 million
Xbox
shipped
Microsoft Xbox
launched at $299
Xbox at
$199
30 Oct 05
Xbox at
$179
Xbox at
$149
Xbox 360
launched
at $399
6 Feb 06
27 April 06
Xbox at
$179
Revised production
schedule for Xbox
360 to 5- 5.5 million
units by 30th June
2006
Strategic Pricing: Competitor Reaction
& Folk Theorem
READ the HBR articles
• Hypothesis: Bertrand Price Wars occur
due to a mismatch in price signals.
• Mismatch can occur due to (i) declining
volumes ∆qi/∆Q < 0; (ii) uncompetitive
cost structure; (iii) decreasing
productivity; (iv) management type
(predator); (v) calling-my-bluff; (vi)
bounded rational on player type.
The ‘signalling’ payoffs
& assurance
•
A & B Have common interest in coordinating strategies. Player A never choose
‘Bottom’ if rational, only ‘Top’, and Player B should play weakly dominant ‘Left’.
B
A
•
•
•
Left
Right
Top
3,3
1,2
Bottom
2,0
0,0
Problem of coordination where players have different preferences but common
interest in coordinating strategies.
Classroom discussion on Folk Theorem
Next slide for Assurance Game on coordination and trust: Payoff-dominant v
risk-dominant play.
Alliance
Alliance/JV
No
Alliance/No
JV
2,2
Payoffdominant
1,0
No
Alliance/No
JV
0,1
1,1
Risk-dominant
Minimax criteria.
If you look at examples in the book Decoding Strategy
pp148-151 we discuss this next slides for Near-rival
v Apple but it can be applied also in any market-as-agame
Strategy
Simply, identify the near rival [reacting first] and set
up the game tree assuming that near-rival plays
minimax, that is, confining you to the least of the
greatest market shares in the game - so then you play
maximin, to maximise the least loss.
n
Player B
S4
Player A:
S1
S2
S3
Column
maximum
Minimax
strategy
by B
S5
S6
S7
95
60
30
5
70
35
50
55
30
40
90
10
95
70
55
90
Row
Minimum
Maximin
strategy
by A
5
55
10
Apple’s maximin = NR minimax
Apple’s market shares
Near Rival
Strategy A
Near Rival
Strategy B
Row min
Apple
Strategy 1
20
60
20
Apple
Strategy 2
10
80
10
Column max
20
80
Minimax
Smartphones
games with
entropy
See next slide
for camouflage
deceiving play
20
Maximin
Apple’s ‘loading the dice’ strategy
Apple’s market shares adapted from pp152 in
McNutt..camouflage,deceiving mixed strategy
Near Rival
Strategy A
Near Rival
Strategy B
Apple
Strategy 1
20
60
Apple
Strategy 2
80
10
iPhone 7 in 2016
nano iPhone 2018
Post-Workshop: Visit Kaelo v2.0
and www.patrickmcnutt.com
• Check Examples of Critical Time Line in
the Appendix of McNutt’s text Decoding
Strategy.
• Play the PD game and investment game in
Kaelo v2.0 as outlined in class
• Selfish gene [one-shot], dominant strategy
to cheat. Schelling move, ‘loading the dice’.
• Near rival will play minimax – repeated
play/learning and mixed strategy.
• Watch for final E-tutorial announcement.
.