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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 1/42
Serious Games as a Tool to Understand
Complexity in Market Competition: An
Evolutionary Game Theory Simulation Platform
November 28th, 2014 – v0.3
UTC – Labex MS2T
Yves Caseau
National Academy of Technologies –
AXA
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 2/42
Outline
Part 1: Motivations – Making sense in a complex world
Is there a better tool
than Excel™ ?
Part 2: GTES (Game-Theoretical Evolutionary Simulation)
Part 3: Smart Grid Systemic Simulation Example
Part 4: Mass Market Telephony Simulation Examples
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 3/42
Complexity is everywhere in our companies
Complexity is everywhere
Multiple elements and multiple relations,
emergent behavior (ecosystems)
Feedback loops and delays
Uncertainty
Planning / Forecasting is still a major corporate activity
Budget, marketing, business plans, …
Excel™ is still the preferred tool
Complicated spreadsheets …
at best, a few scenarios and sensitivity analysis
Today’s business practices are suited to a complicated world,
not a complex world
Taking competitors & markets into account (adaptation)
Taking uncertainty into account
Enough of linear extrapolations !
Part
I :
Moti
vati
ons
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 4/42
From Strategic Planning to Serious Games
The solution is not better forecasting
With stochastic approaches towards uncertainty …
With multi-variable optimization …
That’s what experience and complexity theory say
We need to develop skills to better prepare for whatever the
future is bringing (situation potential)
Cf. the Art of military war games
Practice of multiple simulated situations develop
reactive skills (reflexes) and systemic understanding
Lessons from multiple strategic thinkers (Julien, Taleb, …)
Serious Games
Play against “smart” opponents
Experience feedback loops
Each scenario (game) is plausible, even if not likely
Part
I :
Moti
vati
ons
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 5/42
History of GTES Development
2000: UMTS Bid
2004 – 2006 : Distribution Channels Optimization
2006 – 2009 : Mobile Operator Competition Model
2009 – 2010 : Extension to Free
2010 – 2012 : Smart Grid Model
Reconcile three geographic visions of Smart Grids
Reconcile two corporate visions of Smart Grid
Part
I :
Moti
vati
ons
The « Utility »
view
The “Utility view” defines a smart grid as
adapting the power network to:
• local sources (as opposed to a one-way
distribution network),
• intermittent production sources (though
storage and favoring flexible production
units)
• using price incentives to “shave”
demand peaks.
The « Google »
view
The “Google view” defines a smart grid as:
• change from a tree structure to
a network structure (centralized to de-
centralized),
• the use of market forces to create
a dynamic and more efficient
equilibrium between supply and demand,
• the use of IT to provide information to
all actors, including end consumers.
The « Japanese »
view
The “Japanese view” is human-centered
instead of being techno-centered. The
goal is to change human behavior to
adapt to new challenges (lack of
resources, global warming, …). Smart
grids are the backbone of a multi-scale
architecture (smart home,
neighborhood, city, region, country)
where each level has its own resources
and autonomy.
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 6/42
Systemic Simulation of Smart Grid
« Regulator »
• Strategy: reduce CO2 emissions, preserveeconomic throughput (enough energy at« acceptable price »), keep a balancedbudget
• Tactical play: incentives to invest in greenenergy, CO2 Tax, storage requirement forintermittent sources of energy Open Questions
• What part does local
storage play?
• What CO2 price would
change significantly the
cost/benefits analysis?
• What is the systemic
benefit of local
management?
• What could be the large-
scale effect of dynamic
pricing on self-
optimization of customer
demand?
• Does Smart Grids provide
better resilience ?
• Is the relationship
between supplier and
operators a “coopetition”
or a competition ?
« Supplier »
• Strategy: maintain EBIDTA, reduce exposureto demand peaks, maintain market share
• Tactical play: Variable pricing (higher pricewhen demand & production costs are high),power plant investments
« Operator »
• Strategy: grow turnover, grow EBITDA,increase market share
• Tactical play: Storage utilization policy,Dynamic pricing, when to invest onadditional capacity (green, storage, fossil)
« City »
• Strategy: maintain low energy averageprice, avoid peak prices, preserve comfort(limit “shaving”)
• Tactical play: choose local operator or“classical” supplier, invest into energysavings (megawatts)
(Regional
fossil/nuclear)
Supplier
Regulator
City
(Local
fossil/green)
Operator
CityCityCity
CO2 Tax
CO2 TaxGreen
Incentives/
constraints
energy Wholesale
price
« classical »
distribution
of energy
Energy @ dynamic price
Variable demand
Part
I :
Moti
vati
ons
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 7/42
Regulator /
Environment
Environment parameters
• Demand growth
• Oil Price Trend
• Nuclear Growth / Reduction trend
• CO2 tax
Technology Parameters
Systemic Parameters
• Cost of green tech (yearly
trend
• Cost of storage (yearly trend)
• Demand variability
• NegaWatt generation (alpha)
• Peak shaving (beta)
• Market share sensitivity
(gamma)
Operator
City Supplier
Operator’s
customers
Supplier’s
Customers
Open Market
The supplier buys electricity
on the open Market when
demand exceeds capacity, at
a very high price
Yearly
Investments
• Grow / reduce
nuclear assets
• Add fossil capacity
Yearly
• Adjust market
shares• Invest into
« negawatt »
energy saving
equipments
Yearly
Investments • Add « green »
capacity
• Add fossil capacity
• Add storage capacity
ReserveBuffer
Storage is divided into
Two separate units with
Different logics
demand
supply
MM price
o.inBuffer
o.fossilePower
supplydemand
o.sellReserve
o.inReserve
o.greenPower
o.buy
o.sell
o.outBuffer
o.outReserve
Wholesale price
MM price
Simulation
over 15
Years
Systemic Simulation of Smart Grid (Model)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 8/42
Part II
Part 1: Motivations – Making sense in a complex world
Part 2: GTES (Game-Theoretical Evolutionary Simulation)
Part 3: Smart Grid Systemic Simulation Example
Part 4: Mass Market Telephony Simulation Example
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 9/42
Game Theoretical Evolutionary Simulation (GTES)
GTES is a tool for looking at a complex
model with too many unknowns
Problem(fuzzy,
complex,
…)
Abstrac-
tions
Model:Set of
equations with
too many
unknown
parameters !
Split
parameters
« Players »
Environment
(DIP)
Strategy (DDP)
Tactical (DV)
“tactical” may
be derived from
“strategy”
(local
optimization)
Parameters which
describe the
player’s goals
eParameters
sParameters
Parameters which are
meaningful (e.g., oil
price future)
Scenario-defined
Obscure &
unknownrandomize
Game Theoretical
Approach
Player’s degrees of
freedom
Wolter
Fabrycky:
DDP/DIP/DV
Part
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 10/42
Game-Theoretical Evolutionary SimulationPart
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
Two ways to look at GTES
Solving a complex undefined optimization problem
Game theory in a complex environment
An approach inspired by Robert Axelrod pioneering work on Agent-based models of cooperation & competition
E.g.; experimental/evolutionary validation of TIT-for-TAT strategy in a repeated prisoner dilemma game
SamplingMonte-Carlo
Search for NashEquilibriums
Machine LearningLocal optimization
GTES parametric analysis
GlobalParameterizedOptimizationProblem
Parameters
Strategy
External
TacticalNon-cooperativeRepeatedGame
Strategic analysis of the player’s goals
Classification
Taking the uncertainty of the model into accountActors
Context
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 11/42
Evolutionary Algorithms & Machine Learning
From each actor’s viewpoint, everything being equal, a GTES model defines a parametric optimization problem:
The objective function fpi for each actor represents the « strategy»
A set of state variables and associated target values (e.g., EBITDA, share, …)
Linear combination + concave valuation of difference
The set x of free variables represent the « tactic »
Finding the best solution (called BR: Best Response) requires to solve an optimization problem
Machine learning means that finding the best tactic is automated
An approximate model requires a heuristic solution
Hill-climbing / meta-heuristics (SA or genetic algorithms)
Local moves according to a neighborhood structure + dichotomy search
Choosing the proper neighborhood structure is a key modeling choice
Eeexf pXx
,,maxMulti-actor maximization
« game/problem » (fp Rn)
Bounded rationality
Part
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 12/42
The Search for Nash Equilibriums
RAIRO - Operations
Research
Vol. 43 No. 4 (October-
December 2009)
Nash Equilibrium (NE)
When each actor is maximally satisfied, w.r.t. each other actor’s tactic
The simplest way to find a NE is to iterate the computation of the « Best Response » function
An iterative loop that may be nested with the local optimization loop
A heuristic version may be derived according to a neighborhood structure V
There does not necessarily exist a « pure » Nash Equilibrium
The loop may not converge ( “destructive war” or “chaos”)
The convergence rate increases with a « maxmin » approach
The valuation function is extended to take one level of feedback
Hence producing the concept of « Forward looking Nash Equilibrium »
),(),(,, iiiiii ttftxfTxi
),(),(,, **
iiiii ttftxfTxi
)),(,((min),(*
jiViiij
iii tjBRtfttf
Part
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 13/42
Sampling
Monte-Carlo
The uncertainty regarding the environment parameters
e is handled through randomization
Each parameter from E is drawn between an min/max
Example: a [1.0, 3.0]
Scenarios
Are used to implement « what-if » analysis (though e)
Boundaries for Monte-Carlo sampling
Experiences
Sample Size x Scenario x Strategies
For each sample, we search for a NE through
a fixed number of iterations
Result is a triplet
Classification (% of stable, war, chaos)
Typical values of key “business” status variable
(mean + confidence intervals)
Stability metric (rate of convergence,
standard deviation ratios) 0
1000
2000
3000
4000
5000
6000
7000
1
37
73
109
145
181
217
253
289
325
361
397
433
469
505
541
577
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
1
18
35
52
69
86
103
120
137
154
171
188
205
222
239
256
273
290
0
1000
2000
3000
4000
5000
6000
7000
135
69
103
137
171
205
239
273
307
341
375
409
443
477
511
545
579
Part
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
stable
chaos
?
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 14/42
Lessons from Practice
Defining the satisfaction (w.r.t strategy = set of goals) is critical
Additive versus multiplicative formulas
Use multiple strategy objects to avoid the fine tuning of sensitivity
One way to resolve the relative weight issue
Local optimization = Neighborhood + meta-heuristics
Simple local-climbing seems enough …
… but the need for “multiple simultaneous changes” (e.g., 3-opt) is an
indication of the game’s interest
Experiences with meta-heuristics (Tabu, genetic, random walks) are
interesting but do not change the nature of the result
Sensitivity to initial values for “tactics” is a quality indicator of
local search strategy
Meta-principle : increase the “opt power” until stability is reached
Measuring “Nash convergence” is tricky (unless infinite time)
Easy : define a N-uple distance over tactics
Harder : evaluate if a small distance is acceptable
Linear regression on major Business KPIsPart
II :
GT
ES (
Gam
e T
heore
tical
Evolu
tionary
Sim
ula
tion)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 15/42
Part III
Part 1: Motivations – Making sense in a complex world
Part 2: GTES (Game-Theoretical Evolutionary Simulation)
Part 3: Smart Grid Systemic Simulation Example
Part 4: Mass Market Telephony Simulation Example
Part 5: Conclusion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 16/42
Energy demand Market Share
Demand generation
Operator Production
Dynamic Pricing« NegaWatt » compensation (yearly)
Peak « shaving » (hourly)
MW
Time (hourly/daily)
pattern
Random
noise
By
City
%
savings
Electricity sell price
a1
a2
b1
b2
%
cutoff
price
Generates investments
As price rise,
Cities invest in energy
saving
Self-motivated or
operator-controlled
Peak price → partial cutoff
g1
g2
Opera
tor
mark
et
share
Price ratio
(supplier/operator)
Max penetration rate
Price sensitivity
Supplier (wholesale/ customer)
Operator
Price
($)
• local → production price + margin1
• supplier → wholesale price + margin2
Production cost × D
Wholesale base price
Production (GW)
D
Nuclear capacity
+ customer
costs
Use local « green » power
• Green power is intermittent
• The operator controls & monitor all green
production from the city
City Energy Demand (MW)
Use local storage (buffer / reserve)
Use local « fossil » power
Wholesale purchase (Supplier)
Extra
demand
Extra
capacity buffer
resell
Extra
demand
+
• use buffer if full
• Use reserve if (buy) price is high
• Fill reserve is (buy) price is low
• Sell from reserve if (sell) price is
high
• Fossil production is variable
• Fossil production generates CO2 taxes
• Unmet demand is bought wholesale
buffer
reserve
-
+/-
Reserve « threshold »
prices → tactical
parameters (policy)
S3G : A Collection of Simple Models
Difference between constrained /unconstrained demand
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 17/42
S3G : Players’ objectives (optimization functions)
Each players tries to optimize three “KPI” (performance indicators)
Using a linear combination
Measuring the difference between current and target value (defines a strategy)
Regulator
To maintain total output (economy = electricity consumed + negaWatt)
To reduce CO2
To keep a balanced budget (subsidies < taxes)
City
To keep average electricity bill as low as possible
To keep the current level of demand-response shaving
To reduce the « feared worst price » = peak price + 5 x anual growth rate.
Supplier (global)
To keep income at current level
To protect market share (80% when simulation starts)
To keep the number of hours when foreign supply is needed to a minimum
Operator (local)
To grow market share (from 20%)
To grow turn-over
To increase income (sales – expenses)
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 18/42
(1) Implement S3G Model
The model has successively been implemented (1000 lines of CLAIRE code)
“Rules of Play”
S3G Work Plan
(2) What-if analysis and validation
S3G has been checked though a number of what-if scenarios (both as a
debugging method and a first output)
(3) Machine Learning : « Tactic optimization »
A crude version of “hill climbing / local search” optimization is operational.
More complex methods are required because of pricing structure
(4) Search for Nash equilibrium
This is how we address the question of competition vs. cooperation.
(5) Randomize unknown environment parameters
Full-blown GTES simulation includes a Monte-Carlo sampling of unknown systemic
parameters to asses the robustness of phase (4) : classification. Randomization is
extended to demand generation to study the impact of variability
(6) Search for robust strategies and robust equilibriums
The search for best tactics is extended to take robustness into account.
The analysis of the competition landscape is revised accordingly
(7) Scenario Analysis
The last phase is to decompose the parametric space into relevant scenarios
to address the questions/issues from slide # 1.
Environment parameters
• Demand growth
• Oil Price Trend
• Nuclear Growth
• CO2 tax
Technology Parameters
Systemic Parameters
• Cost of green tech
• Cost of storage
• Demand variability
• NegaWatt generation
• Peak shaving
• Market share
sensitivity
S3G tool
RegulatorSupplier
Operator
City
Reflects one’s
vision of World
economy &
policies
Reflects one’s
confidence in
technology
progress
Reflects one’s
understanding
of energy
ecosystem
S3G : Simulation & Game ProtocolPart
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 19/42
Sensitivity to variability
Testing the hypothesis that variability favors local operator
The results show only non-significant improvements for SG operator(small compared to the overall “local resell business” equilibrium !)
Variability “pushes” the system in the “right direction” (favorable to smart grids), but
is a “small scale” change and most of it seems absorbed in the complex loop
interactions
variability
Demand
response
shaving
NegaWatt
Investment
Local vs
centralized
fossile production
Wholesale
price
Operator
EBITDA
Market Share negaWatt Fossile
investment
DR shaving
E1: regular 85,13€ 789M€ 22% 7,08TWh 2MW 14,04%
E2: more
variation87,50€ 711M€ 22% 7,15TWh 4MW 14,87%
E3: local
variation84,39 703M€ 22,56% 6,34 12MW 14,7%
Very sensitive to
environment
parameters
Reaction to price
increase
Fear :
projected 5-
yr price
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 20/42
Carbon Tax, Green Power and Storage Cost
Carbon Tax
Does not help: reinforces the advantage of nuclear energy
Carbon tax to Solar subsidy: positive (PV industry ) but marginal
Green Power : still too expensive … (all economic parameters drawn from Web search – orders of magnitude)
Storage Cost Local optimization finds the optimal buffer/reserve ratio & when to buy/sell
Efficiency = average difference (buy/sell) price -> depend on price structure !
Yields a price threshold at [50% to 100%] of wholesale price
Resilience (e.g., Japan) or co-usage (electric car) is not factored in
Wholesale
price
Operator
income
Market
Share
Solar
Investment
Storage
Investment
NegaWatt DR
shaving
Total
CO2
E1
Reference84,12€ 862M€ 21,8% 0MW 0MW 7,3TWh 14,3% 35,8Mt
S3:
CO2 tax93,7€ 1106M€ 19,8% 0MW 0MW 9,37TWh 14,5% 24.7Mt
S3a CO2 tax
+ solar92,3€ 1006M€ 18,8% 1670MW 6,8MW 8,63TWh 14,8% 25.4Mt
S4 = S3a +cheap
storage87.8€ 992M€ 21% 416MW 50MW 7,7TWh 15,25% 29.3Mt
S6 cheap
Solar (100€/MWh)83,57€ 786M€ 21,7% 2461MW 0MW 6,9TWh 14,2% 35.7Mt
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 21/42
Smart Grids : Strategy Matrix
S3G is a stable model (no war/ chaos) but ESS convergence is approximate
The strategies of suppliers and operators may be aligned or conflicting
The effect is regulation is very important
0
20
40
60
80
100
120
140
160
1 3 5 7 9 11 13 15 17 19
EBITDA (M€)
price (€/MWh)
Nash distance(%)
0
20
40
60
80
100
120
140
160
1 3 5 7 9 11 13 15 17 19
EBITDA (M€)
price (€/MWh)
Nash distance(%)
• wholesale boundaries
• fixed/variable price structure
Operator:
Soft Strategy
Operator:
Hard Strategy
Supplier :
Soft strategy
Supplier: 11635 M€ @ 79.7€
Operator: 1282 M€ : 19.9% MS
Supplier: 11775 M€ @ 80.7€
Operator: 667 M€ : 21.6% MS
Supplier:
Hard strategy
Supplier: 7119 M€ @ 70.3€
Operator: 1253 M€ : 20.58% MS
Supplier: 7225 M€ @ 68.7€
Operator: 737 M€ : 19.9% MS
Focus on
Marketshare
Surprise ?
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 22/42
Oil Price Sensitivity and « De-nuclearization »
Oil Price increase does not favor smart grids operators
De-nuclearization is a more favorable scenario …
When storage cost is lowered (1% market share gain at 100$/W)
Studying scale-sensitivity would require more time/computers/faster
machines
One game with approximate results (10 samples) : 1 day of CPU
Based on other GTES application, typical sample size should be 1000
Wholesale
price
Operator
income
Market
Share
Solar
Investment
Storage
Investment
NegaWatt DR
Shaving
Total
CO2
E1
Reference84,12€ 862M€ 21,8% 0MW 0MW 7,3TWh 14,3% 35,8Mt
E4: oil price
increase91,2€ 647M€ 21,4% 0MW 5MW 8,3TWh 15,5% 28Mt
S2: Government
« de-nuclearizes »88€ 785M€ 21,7% 0M 4MW 8TWh 14,6% 45Mt
H1: 3 cities (vs 10) 82.05€ 866M€ 21.7% 0MW 0MW 6.9TWh 12.67% 36,1Mt
H2: 20 cities 83.6€ 568M€ 23.6% 0MW 0MW 7TWh 13,56% 37Mt
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 23/42
S3G Temporary Conclusions
Systemic Simulation of Smart Grids
A very simple model …
… yet which captures a number of interaction loops between players
Demonstrates an interesting level of complexity …
… shown by the “relative difficulty” to get stable Nash Equilibrium
Serious Gaming as a learning tool (not a forecasting tool !)
Takes the various stakeholders viewpoint into account
Build systemic knowledge (understand the environment as a system with feedback loops and delays)
GTES is an interesting approach for serious gaming
A Few Lessons Learned
Importance of regulation
Competition regulation (dynamic & wholesale pricing)
Technology / CO2 incentives
Smart behavior starts with storage
When it becomes affordable at local scale (vs. STEP)
Part
III :
Sm
art
Gri
ds
Syst
em
ic S
imula
tion
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 24/42
Part IV
Part 1: Motivations – Making sense in a complex world
Part 2: GTES (Game-Theoretical Evolutionary Simulation)
Part 3: Smart Grid Systemic Simulation Example
Part 4: Mass Market Telephony Simulation Examples
Conclusions
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 25/42
Example (1): Distribution Networks
Simple model
Two-steps phases which
are distinct from an
organization viewpoint
Coupling with other operators
through distributors
Serious Games at the excom
level … successful impact
Parc SRO
Année 1Parc BT
Année 1
SOCC
Ne fait rien
Parc SRO
Année 2Parc BT
Année 2
Churne (interne ou externe)
renouvelle
R1 :
RCBT
R2:
WEBTR3:
GSATR4:
DCT
R5:
agences
R6:
WEB
@SRO
100 clients(forfaits) « statistiques »
Année 2
Année 1
R1 R2 R3 R6
Calcul
Renouvellement
Par réseau
Agrégation
Résultat :
- Bilan par
réseau
- Parc total
par opérateurCalcul
Ventes
Par réseau
Base
100.0
« ajustée »Répartition
Courbe « en S »
d’appétence
Courbe « en S » de
renouvellement
Calcul
PdM
BT/ SRO
Calcul
PdM
BT/ SRO
Part
IV
: T
ele
phony S
imula
tion E
xam
ple
s
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 26/42
Example (2) : Commercial Costs Optimization
Problem: resource allocation between different channels …
… while taking competition between distribution channels into
account (hard to evaluate)
Goal (met) : start discussion between channels
method: what-if scenarios
First round : calibration
Second round : global simulation
Adjustment
Optimization
delta
OPEX
2006 data
sales, fixed/
variable costs
delta
CAPEX
result
Euros
Competition
Matrix
Sensibility
Price -> Sales
Par
canal C1 @
p1
Dist :
60%
C2 @ p2
Dist :
100%
C2 @ p2
Dist :
100%
C3 @ p3
Dist :
100%
C[1,2] = 50%
C[2,1] =
50%
Flux si p1 < p2
Part
IV
: T
ele
phony S
imula
tion E
xam
ple
s
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 27/42
Example (3) : CGS – Cellular Game Simulation
Simulation of competition between n mass-market telephony
operators
Mobile telephony, internet access provider, most operator business
Follow-up of UMTS 2000 simulation
While taking distribution channels into account
Make use of only published data (Annual reports)
… which required successive simplification iterations
Time unit = year (3/5 year => 3/5 iterations)
Model that has been played with 3 and 4 players …
First to evaluate 3 years plans (3YP) robustness
Then, to simulate the arrival of Free on the French market
(2009-2010)
Independently, we run a “war game” organized by
McKinsey, with a more sophisticated model but no
automated reaction
Serious game … are
serious : cannot
comment !
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 28/42
CGS – Operator Model from CEO’s insight
Input
ACQ: average acquisition cost per customer
FID: loyalty cost
“average service basket” price
(PPM: typical average package price)
Internal Variables
Customer Base
# acquisitions, # renewals
Consumption (faction of expected average)
ARPU (summation of PPM x usage)
Simple Financial Model
Operations expenses (including annual trend)
Inbound / Outbound Turnover, interco (TA)
Ebitda = CA – DO – FID – ACQ - Interco
PU
ACQ
renouvellement
1
acquisition
Opérateur
2
churn
3
PU
FIDprix
MVNO inclus
Aggregation:
•MVNO
•Voice / data
• MM / Business
• Pre-/post-paid
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 29/42
Coupling
Acquisition/ price relationship →
Change / price relationship [churn / renewal] → similar model with
different parameters
1
Orange
2 3 1
SFR
2 3
1
Bouygues
2 3
Nouveaux
S-curve to model sensitivity
+ competition model Cf. INRIA
2010 Talk
1
Opérateur
2 3
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 30/42
CGS – Simulation Architecture
5-steps computational model
Each iteration produces yearly results (model’s variables) for each
operator
Nouveaux
clients
Calcul Churn,
Renouvellement,
migration
3YP – tactique :
fid, acq, pricing
f,f' : Courbes en S +
compétition (opérateur y
1
Renouvellement
(global –
non ventilé par
canal)
45VolumesRésultats
Canaux
Calcul
Ventes
Par canal
PdM
Par canal
2
3
Base
op 1
Base
op 2
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 31/42
Open Mass-Market Naive Competition
(Example 4)
Each actor is a company with
an ARPU (price)
an attractiveness (premium)
a customer base
fixed costs + variables costs
migration fluidity
a structural churn
Crude
estimates !
Market share follows a
(price + premium)a distribution
(new)
price
(old)
price
mig
ratio
n
Churn follows a
constant × priceb
distribution
CAVEAT
- closed market
- retail channels ignored
- no segmentation
⇒ 50 lines of code,
easy to reproducem
igra
tion
mig
ratio
n
…
(new)
price
(old)
price
Unknown:
- a
- b
Customer
base 1
Customer
base N
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Mobile Operators (I) : Playing What-If ScenariosPart
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-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
3 mobile operators 4 mobile operators
4 mobile operators, loose strategies 4 mobile operators, tight strategies
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
Stable
WAR
Chaos
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
2011 2012 2013 2014 2015
B
O
S
M
0%
20%
40%
60%
80%
100%
B O S M
dev
sat%
result
Stable
WAR
Chaos
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 33/42
Mobile Operators (II) : Strategy Analysis
Sensitivity to alpha (aggressive) Sensitivity to alpha (conservative)
If the fourth operator builds a network ? If the third operator flattens its costs ?
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
Stable
WAR
Chaos
0%
20%
40%
60%
80%
100%
B O S F M
dev
sat%
result
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2011 2012 2013 2014 2015
B
O
S
F
M
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 34/42
Part V
Part 1: Motivations – Making sense in a complex world
Part 2: GTES (Game-Theoretical Evolutionary Simulation)
Part 3: Smart Grid Systemic Simulation Example
Part 4: Mass Market Telephony Simulation Example
Future Directions & Conclusions
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 35/42
Performance Issues
Models that have been tested with GTES are computationally simple, still running once simulation ranges from tens of milliseconds (most of them) to one second.
Tactics’ optimization requires from 100 to 1000 simulation cycles
The search for Nash equilibrium requires many hundreds of optimization cycles (interlacing between the two loops helps by a factor of 10)
Monte-Carlo Sampling requires a few hundreds to a few thousands of Nash equilibriums searches
Consequently,
Computation time quickly becomes a problem(from a day to a year)
Parallel computation is straightforward
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 36/42
Quality of Model Issues
Modeling « Torture Bench »
Machine learning => zooms on logic faults
No mercy for linear approximation that are “locally right”
« Model tuning » takes time !
A good practice is to limit oneself to status variable for which an value history is available
« stability » requirement
Limit / boundary behaviors
Ex: S-curve versus linear formulas
Concavity/convexity –
Neighborhood structure and exploration strategy
Tradeoff between efficiency and relevance (should mimic actors)
Scale –sensitive : need to work on the real size problem (even if abstracted)
Monte-Carlo sampling must be introduced early on to avoid over-engineering
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 37/42
Relations with System Dynamics
System Dynamics
Models based on interactionnetworks between statevariables (e.g., CGS example)
Proximity
Very close to the workof J. Forrester or J. Sterman
These networks are a good first step towardsGTES modesl
Differences
What’s inside the model (detail = interaction formulas) is critical andhas a deep impact on results …
… especially when the system is coupled with a stochastic input flow
Polarities (+/- ) between state variables are not enough, not even the values of local derivatives (elasticity => linear model).
Prix Prix TA
Usage
PU FID
Acquisition
Interco EBITDA
Dépenses
Churn
Renouvellement
PUACQ
123
PUFID
Base
TCOOffre
ARPU
CA Entrant
CA Sortant
PU ACQ
FID
ACQ
prix
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 38/42
Serious Games : Key Take-aways
Results
Game
Analysis
The model is wrong …
or too complex
(e.g. Social Networks)
The model is wrong …
but may be fixed
(e.g. CGS)
The model is right …
our thinking was wrong
Systemic education
The model is right …
and shows a feedback
loop that we missed
The more embarrassing, the more useful
eg: Market Share , Technology introduction …
Many successful instances over the years
Sales Channel, Customer Lifecycle, LTE bid
The expert
disagrees
and the fun
starts
Simple
Model + Key
Business
Variables
What-if Scenarios,
Players’ strategies
Contin
uous
impro
vem
ent
Models’ torture bench
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 39/42
Failed SNS Experiment
I applied GTES to study the “Google+ versus
Facebook” fight two years ago.
Value of SNS experience =
quality of social content X quality of Edge ranking
The model is interesting, it shows the recursive value equation
(strong reinforcement)
Quality of content = f(size of network x time spent on network)
Time spent on SNS = f(Quality of content / effort)
The model was easy to implement (using Duncan Watts
principles for social network growth)
Showcases the difference between adoption and usage !
However, the results are immensely sensitive to frequencies (of visit)
and delays
Bottom-line : GTES is not universal
Start with a few scenarios, then heavy sampling with Monte-Carlo …
If unstable, you need more real-life measures to narrow the
incertitude about systemic parameters
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 40/42
RTMS : Repeated Tender Market Share
Problem: find a model to reproduce the behavior of buyers /
sellers in a closed repeated tender market
For instance, IT division buying software development man days
See if there is a systemic justification for observed practices
First Model
Bid price is a combination (either fixed or randomized) of
Balanced price (economic optimization)
Dynamic price (based on previous bidding history)
Two sets of coefficient according the previous
Selection is based on price (cheapest wins) with a bias towards
diversity (cf. the two-sourcing rules)
Very similar to repeated Prisoner's dilemma game (Axelrod) but more
complex (than TIT-for-TAT)
Preliminary results
Interesting : Nash equilibriums are found … not always (and required
a lot of tuning).
“Forward Nash Equilibrium” – interesting but expensive
Meaningful simulations : collusions, bluffing, …
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 41/42
Future : Connected Health Trust Game
The problem : data privacy issues with connected devices
Would you share your health data with your insurance company to
get a better price ?
Would you react socially - as a group – to selective price increases ?
Insurance issues :
Anti-selection (if another insurer gets the “lower risk” group)
Asymmetry of information
The model:
Segments of population with different health risk behaviors, which
are revealed (or not) by connected devices
Group of insurance companies with different policies (fixed or
variable prices according to behavior)
Macro parameters
Precision of determination - link between behavior & risk
Stability of determination – evolution in time
Importance of social behavior
No results yet … stay tuned (open for collaboration)
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Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 42/42
Conclusion
“Serious Games” approach will become mainstream in the future
Forecasting does not work any longer
Need to develop reflexes and skills
Build systemic knowledge (understand the environment as a system with feedback loops and delays)
GTES is an interesting platform for serious gaming
Combination of “classical techniques”
Evolutionary game theory … will become popular with faster computers
A workbench for model tuning –CAVEAT – not all models adapt to GTES
Not a panacea, but proven utility over the last 10 years
Still, a lot of work is required
Parallelization (good MapReduce candidate)
Making “Forward Nash” (look-ahead) practical
Leveraging evolutionary meta-heuristics (e.g. genetic algorithms)
« The difficulty lies, not in the
new ideas, but in escaping
from the old ones.”
J. M. Keynes
Part
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