The document discusses the use of serious games and evolutionary game theory to navigate the complexities of market competition, emphasizing the inadequacies of traditional forecasting tools in complex environments. It outlines the development of a game-theoretical evolutionary simulation platform (GTES) to understand interactions in markets like smart grids and telephony, incorporating strategies for various players such as regulators, suppliers, and operators. The platform allows for scenario simulations that aid in strategic decision-making amid uncertainties and dynamic market conditions.

1. 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 1/42

2. 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 2/42

3. 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 !
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 3/42
Part I : Motivations

4. 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
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 4/42
Part I : Motivations

5. 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
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 5/42
Part I : Motivations
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.

6. Systemic Simulation of Smart Grid
« Regulator »
• Strategy: reduce CO2 emissions, preserve
economic throughput (enough energy at
« acceptable price »), keep a balanced
budget
• Tactical play: incentives to invest in green
energy, CO2 Tax, storage requirement for
intermittent 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 exposure
to demand peaks, maintain market share
• Tactical play: Variable pricing (higher price
when 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 on
additional capacity (green, storage, fossil)
« City »
• Strategy: maintain low energy average
price, avoid peak prices, preserve comfort
(limit “shaving”)
• Tactical play: choose local operator or
“classical” supplier, invest into energy
savings (megawatts)
CO2 Tax
(Regional
fossil/nuclear)
Supplier
Regulator
Green CO2 Tax
Incentives/
constraints
energy Wholesale
« classical »
distribution
of energy
CCitiytCyity
City
price
(Local
fossil/green)
Operator
Energy @ dynamic price
Variable demand
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 6/42
Part I : Motivations

7. Yearly
Investments
• Grow / reduce
nuclear assets
• Add fossil capacity
Regulator /
Environment
Environment parameters
• Demand growth
• Oil Price Trend
• Nuclear Growth / Reduction trend
• CO2 tax
Technology Parameters
• Cost of green tech (yearly
trend
• Cost of storage (yearly trend)
Systemic Parameters
• Demand variability
• NegaWatt generation (alpha)
• Peak shaving (beta)
• Market share sensitivity
(gamma)
Yearly
• Adjust market
Operator
City Supplier
Operator’s
customers
Supplier’s
Customers
MM price
supply
Open Market
The supplier buys electricity
on the open Market when
demand exceeds capacity, at
a very high price
demand
Buffer Reserve
Storage is divided into
Two separate units with
Different logics
supply
o.buy
o.sell
o.inReserve
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 7/42
shares
• Invest into
« negawatt »
energy saving
equipments
Yearly
Investments • Add « green »
capacity
• Add fossil capacity
• Add storage capacity
demand
o.inBuffer
o.fossilePower
o.sellReserve
o.greenPower
o.outBuffer
o.outReserve
Wholesale price
MM price
Simulation
over 15
Years
Systemic Simulation of Smart Grid (Model)

8. 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 8/42

9. 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”
Parameters which are
meaningful (e.g., oil
price future)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 9/42
(local
optimization)
Parameters which
describe the
player’s goals
eParameters
sParameters
Scenario-defined
Obscure &
unknown
randomize
Game Theoretical
Approach
Player’s degrees of
freedom
Wolter
Fabrycky:
DDP/DIP/DV
Part II : GTES (Game Theoretical Evolutionary Simulation)

10. Game-Theoretical Evolutionary Simulation
GTES
parametric analysis
Machine Learning
Local
optimization
Search for Nash
Equilibriums
Tactical
Strategic analysis of the
player’s goals
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 10/42
Part II : GTES (Game Theoretical Evolutionary Simulation)
 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
Sampling
Monte-Carlo
Global
Parameterized
Optimization
Problem
Parameters
Strategy
External
Non-cooperative
Repeated
Game
Classification
Taking the uncertainty of the
Actors model into account
Context

11. Evolutionary Algorithms & Machine Learning
From each actor’s viewpoint, everything being equal, a GTES model defines
a parametric optimization problem:
f x e e 
E p
max , ,
Multi-actor maximization
« game/problem » (fp  Rn)
i for each actor represents the « strategy»
x 
X
 The objective function fp
 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)
Bounded rationality 
 Local moves according to a neighborhood structure + dichotomy search
 Choosing the proper neighborhood structure is a key modeling choice
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 11/42
Part II : GTES (Game Theoretical Evolutionary Simulation)

12. The Search for Nash Equilibriums
 When each actor is maximally satisfied, w.r.t. each other actor’s tactic
, , ( , ) ( , ) i i i i i i i x T f x t f t t     
 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
( , ) min( ( , ( ), ) *
i i i f t t f t BR j t  
i i V i j
j 
i
 
 Hence producing the concept of « Forward looking Nash Equilibrium »
RAIRO - Operations
Research
Vol. 43 No. 4 (October-
December 2009)
 Nash Equilibrium (NE)
, , ( , ) ( , ) * *
i i i i i i x T f x t f t t    
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 12/42
Part II : GTES (Game Theoretical Evolutionary Simulation)

13. 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,
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
1
18
35
52
69
86
103
120
137
154
171
188
205
222
239
256
273
290
7000
6000
5000
4000
3000
2000
1000
0
1
35
69
103
137
171
205
239
273
307
341
375
409
443
477
511
545
579
7000
6000
5000
4000
3000
2000
1000
standard deviation ratios) 0
1
37
73
109
145
181
217
253
289
325
361
397
433
469
505
541
577
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 13/42
Part II : GTES (Game Theoretical Evolutionary Simulation)
stable
chaos
?

14. 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 KPIs
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 14/42
Part II : GTES (Game Theoretical Evolutionary Simulation)

15. 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 15/42

16. Energy demand Market Share
Demand generation
Operator Production
Max penetration rate
Price sensitivity
Dynamic Pricing
MW
Random
noise
pattern
Time (hourly/daily)
« NegaWatt » compensation (yearly)
Generates investments
Peak « shaving » (hourly)
Peak price → partial cutoff
Price
($)
Wholesale base price
Production cost × D
Production (GW)
D
Nuclear capacity
City Energy Demand (MW)
Extra
demand
Extra
capacity buffer
+
resell
• 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
Extra
demand
buffer
reserve
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 16/42
By
City
%
savings
Electricity sell price
a1
a2
b1
b2
%
cutoff
price
As price rise,
Cities invest in energy
saving
Self-motivated or
operator-controlled
g1
g2
Operator
market share
Price ratio
(supplier/operator)
Supplier (wholesale/ customer)
Operator
• local → production price + margin1
• supplier → wholesale price + margin2
+ customer
costs
Use local « green » power
• Green power is intermittent
• The operator controls & monitor all green
production from the city
Use local storage (buffer / reserve)
Use local « fossil » power
Wholesale purchase (Supplier)
high
• Fossil production is variable
• Fossil production generates CO2 taxes
• Unmet demand is bought wholesale
-
+/-
Reserve « threshold »
prices → tactical
parameters (policy)
S3G : A Collection of Simple Models
Difference between constrained /unconstrained demand
Part III : Smart Grids Systemic Simulation

17. 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)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 17/42
Part III : Smart Grids Systemic Simulation

18. S3G : Simulation & Game Protocol
S3G Work Plan
(1) Implement S3G Model
The model has successively been implemented (1000 lines of CLAIRE code)
“Rules of Play”
(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
Reflects one’s
vision of World
economy &
policies
Technology Parameters
• Cost of green tech
• Cost of storage
Reflects one’s
confidence in
technology
progress
Systemic Parameters
• Demand variability
• NegaWatt generation
• Peak shaving
• Market share
sensitivity
S3G tool
Regulator
Supplier
Operator
City
Reflects one’s
understanding
of energy
ecosystem
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 18/42
Part III : Smart Grids Systemic Simulation

19. Sensitivity to variability
 Testing the hypothesis that variability favors local operator
variability
Demand
response
shaving
Reaction to price
increase
NegaWatt
Investment
Local vs
centralized
fossile production
 The results show only non-significant improvements for SG operator
(small compared to the overall “local resell business” equilibrium !)
Wholesale
price
Operator
EBITDA
Market Share negaWatt Fossile
investment
Fear :
projected 5-
yr price
DR shaving
E1: regular 85,13€ 789M€ 22% 7,08TWh 2MW 14,04%
E2: more
variation
87,50€ 711M€ 22% 7,15TWh 4MW 14,87%
E3: local
variation
84,39 703M€ 22,56% 6,34 12MW 14,7%
 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
Very sensitive to
environment
parameters
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 19/42
Part III : Smart Grids Systemic Simulation

20. Carbon Tax, Green Power and Storage Cost
E1
Reference
S3:
CO2 tax
S3a CO2 tax
+ solar
S4 = S3a +cheap
storage
S6 cheap
Solar (100€/MWh)
 Carbon Tax
Wholesale
price
Operator
income
Market
Share
Solar
Investment
Storage
Investment
NegaWatt DR
84,12€ 862M€ 21,8% 0MW 0MW 7,3TWh 14,3% 35,8Mt
93,7€ 1106M€ 19,8% 0MW 0MW 9,37TWh 14,5% 24.7Mt
92,3€ 1006M€ 18,8% 1670MW 6,8MW 8,63TWh 14,8% 25.4Mt
87.8€ 992M€ 21% 416MW 50MW 7,7TWh 15,25% 29.3Mt
83,57€ 786M€ 21,7% 2461MW 0MW 6,9TWh 14,2% 35.7Mt
 Does not help: reinforces the advantage of nuclear energy
shaving
 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
Total
CO2
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 20/42
Part III : Smart Grids Systemic Simulation

21. Smart Grids : Strategy Matrix
S3G is a stable model (no war/ chaos) but ESS convergence is approximate
160
140
120
100
80
60
40
20
0
1 3 5 7 9 11 13 15 17 19
EBITDA (M€)
price (€/MWh)
Nash distance
(%)
160
140
120
100
80
60
40
20
0
1 3 5 7 9 11 13 15 17 19
The strategies of suppliers and operators may be aligned or conflicting
Operator:
Soft Strategy
Supplier :
Soft strategy
Supplier: 11635 M€ @ 79.7€
Operator: 1282 M€ : 19.9% MS
Supplier:
Hard strategy
Supplier: 7119 M€ @ 70.3€
Operator: 1253 M€ : 20.58% MS
The effect is regulation is very important 
EBITDA (M€)
price (€/MWh)
Nash distance
(%)
Operator:
Hard Strategy
Focus on
Marketshare
Supplier: 11775 M€ @ 80.7€
Operator: 667 M€ : 21.6% MS
Supplier: 7225 M€ @ 68.7€
Operator: 737 M€ : 19.9% MS
• wholesale boundaries
• fixed/variable price structure
Surprise ?
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 21/42
Part III : Smart Grids Systemic Simulation

22. Oil Price Sensitivity and « De-nuclearization »
Wholesale
price
Operator
income
Market
Share
Solar
Investment
Storage
Investment
NegaWatt DR
E1
Reference
84,12€ 862M€ 21,8% 0MW 0MW 7,3TWh 14,3% 35,8Mt
E4: oil price
increase
91,2€ 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
 Oil Price increase does not favor smart grids operators
 De-nuclearization is a more favorable scenario …
Shaving
 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 
Total
CO2
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 22/42
Part III : Smart Grids Systemic Simulation

23. 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)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 23/42
Part III : Smart Grids Systemic Simulation

24. 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 24/42

25. Example (1): Distribution Networks
 Simple model
 Two-steps phases which
are distinct from an
organization viewpoint 
Année 1
 Coupling with other operators
through distributors
 Serious Games at the excom
level … successful impact
R5:
agences
Parc SRO
Année 1
100 clients(forfaits) « statistiques »
R2:
WEBT
Parc BT
Année 1
SOCC
Ne fait rien
R4:
DCT
Churne (interne ou externe)
R1 R2 R3 R6
Parc SRO
Année 2
R3:
GSAT
renouvelle
Parc BT
Année 2
R1 :
RCBT
R6:
WEB
@SRO
Année 2
Calcul
PdM
BT/ SRO
Calcul
Renouvellement
Par réseau
Agrégation
Résultat :
- Bilan par
réseau
- Parc total
par opérateur
Calcul
Ventes
Par réseau
Base
100.0
« ajustée »
Répartition
Calcul
PdM
BT/ SRO
Courbe « en S »
d’appétence
Courbe « en S » de
renouvellement
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 25/42
Part IV : Telephony Simulation Examples

26. 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
C2 @ p2
Dist :
100%
C3 @ p3
Dist :
100%
Competition
Matrix
Par canal
C1 @
p1
Dist :
60%
C2 @ p2
Dist :
100%
Sensibility
Price -> Sales
Flux si p1 < p2
C[1,2] = 50%
C[2,1] =
50%
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 26/42
Part IV : Telephony Simulation Examples

27. 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 ! 
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 27/42
Part IV : Telephony Simulation Examples

28. 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
PU
FID
2
Opérateur
prix
3
MVNO inclus
renouvellement
1
acquisition
churn
Aggregation:
•MVNO
•Voice / data
• MM / Business
• Pre-/post-paid
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 28/42
Part IV : Telephony Simulation Examples

29. Coupling
1
2 3 1
Orange
1
2 3
Opérateur
 Acquisition/ price relationship →
2 3
SFR
1
2 3
Bouygues
Nouveaux
 S-curve to model sensitivity
 + competition model Cf. INRIA
 Change / price relationship [churn / renewal] → similar model with
2010 Talk
different parameters
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 29/42
Part IV : Telephony Simulation Examples

30. CGS – Simulation Architecture
 5-steps computational model
Nouveaux
clients
Résultats Volumes
5 4
Base
op 2
Calcul Churn,
Renouvellement,
migration
Canaux
PdM
Par canal
Calcul
Ventes
Par canal
3YP – tactique :
fid, acq, pricing
Base
op 1
f,f' : Courbes en S +
compétition (opérateur y
1
Renouvellement
(global –
non ventilé par
canal)
2
3
 Each iteration produces yearly results (model’s variables) for each
operator
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 30/42
Part IV : Telephony Simulation Examples

31. 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
migration
migration
…
Churn follows a
constant × priceb
distribution
CAVEAT 
- closed market
- retail channels ignored
- no segmentation
⇒ 50 lines of code,
easy to reproduce
migration
(new)
price
(old)
price
Customer
base 1
Unknown:
- a
- b
Customer
base N
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 31/42
Part IV : Telephony Simulation Examples

32. Mobile Operators (I) : Playing What-If Scenarios
4500
4000
3500
3000
2500
2000
1500
1000
500
0
100%
80%
60%
40%
20%
0%
B O S F M
100%
80%
60%
40%
20%
100%
80%
60%
40%
20%
4500
4000
3500
3000
2500
2000
1500
1000
500
0
100%
80%
60%
40%
20%
Stable
WAR
Chaos
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 32/42
Part IV : Telephony Simulation Examples
-500
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
Stable
WAR
Chaos
dev
sat%
result
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
0
2011 2012 2013 2014 2015
B
O
S
M
0%
B O S M
dev
sat%
result

33. Mobile Operators (II) : Strategy Analysis
Sensitivity to alpha (aggressive) Sensitivity to alpha (conservative)
100%
80%
60%
40%
20%
100%
80%
60%
40%
20%
4500
4000
3500
3000
2500
2000
1500
1000
500
0
If the fourth operator builds a network ? If the third operator flattens its costs ?
100%
80%
60%
40%
20%
4500
4000
3500
3000
2500
2000
1500
1000
500
0
100%
80%
60%
40%
20%
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 33/42
4500
4000
3500
3000
2500
2000
1500
1000
500
4500
4000
3500
3000
2500
2000
1500
1000
500
0
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
0
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
Stable
WAR
Chaos
0%
B O S F M
dev
sat%
result
-500
2011 2012 2013 2014 2015
B
O
S
F
M
Part IV : Telephony Simulation Examples

34. 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 34/42

35. 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
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 35/42
Part V : Future Directions & Conclusion

36. 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
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 36/42
Part V : Future Directions & Conclusion

37. Relations with System Dynamics
 System Dynamics
 Models based on interaction
networks between state
variables
(e.g., CGS example)
 Proximity
 Very close to the work
of J. Forrester or J. Sterman
 These networks are a
good first step towards
GTES modesl
 Differences
PU
ACQ
PU
FID
prix
3 2 1
Prix PU FID
Prix TA
Usage
Acquisition
Interco EBITDA
PU ACQ
FID
Base
Renouvellement
TCO
Offre
CA Entrant
ARPU
CA Sortant
ACQ
 What’s inside the model (detail = interaction formulas) is critical and
has 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).
Dépenses
Churn
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 37/42
Part V : Future Directions & Conclusion

38. Serious Games : Key Take-aways
What-if Scenarios,
Players’ strategies
Results
Game
Analysis
Models’ torture bench 
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 
Continuous
improvement
The more embarrassing, the more useful 
eg: Market Share , Technology introduction …
The model is right …
and shows a feedback
loop that we missed
Many successful instances over the years 
Sales Channel, Customer Lifecycle, LTE bid
The expert
disagrees
and the fun
starts 
Simple
Model + Key
Business
Variables
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 38/42
Part V : Future Directions & Conclusion

39. 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
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 39/42
Part V : Future Directions & Conclusion

40. 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, …
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 40/42
Part V : Future Directions & Conclusion

41. 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)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 41/42
Part V : Future Directions & Conclusion

42. Conclusion
« The difficulty lies, not in the
new ideas, but in escaping
from the old ones.”
J. M. Keynes
 “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)
Yves Caseau – 2014 – Serious Games as a Tool to Understand Complexity in Market Competition 42/42
Part V : Future Directions & Conclusion