A System Dynamics Model for Pricing Converged Telecommunication Services.
Paper Submitted to
CPRSouth 2016
May 2016
1
Table of Contents
Abstract ..................................................................................................................................................... 4
SECTION 1:-Introduction......................................................................................................................... 5
Background ........................................................................................................................................... 5
Problem Statement/Policy Relevance: .................................................................................................. 5
Principal Research Questions: .............................................................................................................. 6
SECTION 2: Literature Review ................................................................................................................ 7
Cost Based Pricing Methods ................................................................................................................. 7
Price & Cost Allocation type ............................................................................................................ 7
Limitation of cost-based pricing approaches. ....................................................................................... 7
Investment oriented pricing alternatives ........................................................................................... 7
Symbiotic & Dynamic Pricing relationships .................................................................................... 8
SECTION 3: Theoretical /Conceptual Framework ................................................................................. 10
Generic Telecomms Models ............................................................................................................... 10
System Dynamic Telecommunication Models ................................................................................... 11
Proposed Conceptual Framework ....................................................................................................... 12
Description of Proposed Model .......................................................................................................... 13
SECTION 4:METHODS & DATA SOURCES ..................................................................................... 15
System Dynamics Approach ............................................................................................................... 15
Data Sources ....................................................................................................................................... 17
Dynamic Hypothesis (Casual Loop Diagram) .................................................................................... 18
MODEL DEVELOPMENT ................................................................................................................ 19
Balancing Loops ............................................................................................................................. 19
Reinforcing Loops........................................................................................................................... 21
Model Testing ..................................................................................................................................... 22
SECTION 5: Results, Analysis & Discussions. ..................................................................................... 24
Maximizing Social & Operator Surplus (Social Surplus) ................................................................... 24
Resulting Price Level .......................................................................................................................... 26
Resulting Competition Level .............................................................................................................. 26
Resulting Market Indicators (Outcomes) ............................................................................................ 27
Discussions.......................................................................................................................................... 29
SECTION 6: -Conclusion & Recommendations .................................................................................... 30
References ............................................................................................................................................... 31
APPENDIX- SD Model & Equations ..................................................................................................... 33
2
Figures
Figure 1 Relationships in the new ICT Ecosystem ................................................................................... 8
Figure 2 Techno-Economic Model (Walrand 2008) ............................................................................... 10
Figure 5 Conceptual View -Regulatory Pricing Model (Author 2016) ................................................. 12
Figure 6 Proposed System Dynamics Price Model:-Regulatory View (Author 2016) ........................... 14
Figure 7 System Dynamics Steps (Sterman 2000).................................................................................. 15
Figure 8 Key Relationships between Price & Regulatory Variables (Author 2016) .............................. 18
Figure 9 Estimated Price-Competition Relationship .............................................................................. 19
Figure 10 Estimated Price-Market Penetration Relationship .................................................................. 20
Figure 11 Estimated Traffic Load-QoS Relationship ............................................................................. 21
Figure 13 Internet Market Trends, Kenya (Economic Survey 2016) ..................................................... 23
Figure 14 User Surplus - None & Optimized Comparison ..................................................................... 24
Figure 15 Operator Surplus - None & Optimized Comparison .............................................................. 25
Figure 16 Social Surplus- None Optimized & Optimized Comparison.................................................. 25
Figure 17 Price – None Optimized & Optimized Comparison ............................................................... 26
Figure 18 Competition – None Optimized & Optimized Comparison ................................................... 26
Figure 19 Market Penetration - None & Optimized Comparison ........................................................... 27
Figure 20 Network Performance - None & Optimized Comparison ...................................................... 27
Figure 21 Operational Income – None Optimized & Optimized Comparison ....................................... 28
Figure 22 Market Penetration, QoS & OpIncome - Combined View ..................................................... 28
Tables
Table 1 Key Relationships & Theories ................................................................................................... 18
Table 2 Summary Results - Initialized vs Optimized Values ................................................................. 29
3
Abstract
Purpose:
The purpose of this paper is to propose a new model for determining the optimal prices for converged
(internet) services. A new framework is considered necessary because existing telecommunication
pricing models fail to capture the multiplatform and multisided nature of contemporary Telco services
(Bauer, 2014)
Design/Methodology
Noting that the converged communications sector is complex and dynamic, this study uses the System
Dynamics approach (Sterman, 2000) to map out key regulatory indicators (variables) and how they
relate and interact with (feedback into) the Price variable. The optimal price will be one that delivers
the best regulatory output - that is the price that delivers the best revenues (profit margins) subject to
the constraints of market penetration and quality of services.
Results/Findings
After optimizing and executing the model, the maximized regulatory objective was achieved with 25%
drop in the price value and a 50% increase in the competition level. The corresponding market outcome
reported a 23% increase in market penetration, an 18% drop in the Operational Income and a 53% drop
in the quality of service.
Originality/Value
A System Dynamics analytical model for pricing services for the converged telecommunication (ICT)
sector is presented. Through optimization, it addresses the challenges of establishing prices for
converged telco services that have multi-sided and multiplatform properties.
Key Words: ICT Policy, Regulation, Networks, Broadband, Telecommunications, Indicators, System
Dynamics
4
SECTION 1:-Introduction
Background
Broadband internet prices remain relatively high and unaffordable, particularly in the developing
countries of Africa. ITU 2015 specifically reports that the monthly broadband rates in Kenya for 1Mbs
of internet access per month is at 35% of average incomes. Comparatively, it is 15%, 32% & 41% of
average incomes in Ghana, Ethiopia and Senegal respectively.
These figures are far off the global targets of 5% average incomes that was set by the UN, Broadband
Commission for affordable internet.
(Waema et. al. 2010) had earlier noted that despite the liberalization of the communication sector,
improved regulatory policies and the landing of multiple of submarine cables to enhance connectivity
to the rest of the world, the price of retail internet access remains prohibitive.
This suggests that new insights and approaches are required to tackle the question of pricing internet
service in developing countries.
Problem Statement/Policy Relevance:
From a regulatory point of view, the pricing of telco services has tended to focus on the whole-sale
pricing that incumbent operators would charge prospective retailers to connect to their networks. The
aim of the regulator has been to find formulea to estimate the cost of services for purposes of setting
interconnection prices.
However, the telecommunication market has over the last years moved away from incumbent market
structures and now regulators have to find new ways to establish optimal pricing formulae for telco
services that exist today, particularly those that are internet based.
Several methodologies have been proposed such as the modified Long Run Incremental Cost (Casier et
al., 2009), Profitability Methodology, (Frederiksen, 2011a) to try and address this emerging challenge.
Whereas these new pricing methodologies can be used to inform regulators of the cost and appropriate
price elements of the new generation telecommunication services, they still suffer from the fact that
they do not capture the multiplatform and multisided nature of contemporary Telco services (Bauer,
2014a)
Multisided platform refers to the property of a communication service to generate revenues from third
parties rather than from the specific user of the service. Examples include free online videos where
content providers or advertisers meet the cost of provision while users access the free service. This
means that the price charged is decoupled from the underlying costs of service provision. It is simply a
negotiated price contract between operators and the advertiser that is then partly taken up by the user.
This study used the system dynamics approach to develop a model that allows regulators to view the
impact that various price values have on the overall telco-market performance. Regulators can then set
targets and retrospectively derive the optimal price values expected to deliver on the same. In this
manner, one can resolve the challenge of pricing services that are agnostic to underlying cost factors.
5
Principal Research Questions:
The key research objective is to develop a regulatory pricing model that is appropriate for the
contemporary, multisided and converged (internet based) telecoms sector. In so doing, the non-linear
relationships that inform the model behavior (output) was examined.
Specifically, the relationship that pricing has with selected regulatory indicators of Market Penetration,
Operational Incomes (Profit) and Quality of Service (QoS) was explored and estimated under the
following questions:
1. How does Internet Price relate to Market Penetration? As an example, how would a 10% price
increase or price drop influence market penetration?
2. How does Internet Price relate to Revenues, Incomes (Profit)”? As an example, would does a 10%
price increase or price drop influence revenues or incomes?
3. How does Internet Price relate to Quality of Service? As an example, how would a 10% price
increase or price drop influence network performance (QoS)
4. What are the Tipping Points in the above relationships? Given a scale of 0-1, where
0=Lowest,1=Highest Price, which Price point gives: (i) Highest and Lowest acceptable Market Share
(ii) Minimum and Max Revenues (iii) Lowest and Highest QoS.
The rest of the paper is organized as follows: Section 2:- reviews literature covering prevailing pricing
mechanisms and their limitations; Section 3:- gives underpinning theoretical foundations and proposes
a new regulatory pricing model; Section 4:- describes the methodology and data sources used; Section
5:- presents and discusses the results and Section 6:- provides conclusions and future work.
6
SECTION 2: Literature Review
Cost Based Pricing Methods
The prevailing methods for pricing communication services are cost based. This essentially means that
in determining what price to charge consumers, the operator’s objective is to ensure that the cost of
providing the services is covered while making acceptable returns on the investment for the
shareholders.
Price & Cost Allocation type
The final price charged is therefore the cost of provisioning the service plus ‘x’, where ‘x’ is the
expected profit margin. The value of possible tariffs or prices charged would range between the BreakEven Point, through the Incremental Cost (IC), Fully Allocated Costs (FAC) and the Stand Alone Cost
(SAC) as described by (Casier et al., 2009)).
This is supported by (Harno, 2010) (de Aguiar, Pinheiro, Neto, Cunha, & Pinheiro, 2009) who used
these cost based pricing approaches to estimate the price operators may charge to offer 3G services
while (Bjorkdahl, Bohlin, & Lindmark, 2004) & (Bohline 2005) did the same to estimate prices for 4G
mobile networks. (He & Walrand, 2006) did a similar analysis for the case of an Internet Service
Provider.
These cost-driven price models are the same ones regulators (ITU 2009) rely on when determining
appropriate wholesale interconnection prices they would enforce on incumbent operators in order to
open up competition.
Limitation of cost-based pricing approaches.
However, the new (Internet-based) communication networks have disrupted the classical economic
theory as the de-facto framework for setting prices and brings in other perspectives for cost recovery.
Newer approaches such as investment oriented and symbiotic pricing approaches have been proposed.
Investment oriented pricing alternatives
(Frederiksen, 2011a) looked at costing broadband services in the new regulatory environment. He
adopted the view that pricing of services should be based on encouraging investments rather than just
establishing the interconnection charges designed to determine how much incumbent or dominant
operators should charge retailers.
(Frederiksen, 2014) further noted that regulatory policy in the original regime was very much about
deciding prices for wholesale products for the incumbent, based on either retail minus or cost plus with
Long Run Incremental Cost (LRIC) as the most used cost concept. He says that this approach no
longer applies since the main problem now is based on how to give the right incentives to all the
players. This means incentivizing the incumbent, the dominant, the new entrants amongst others, so
that they can make the huge investments in the next generation networks.
7
Symbiotic & Dynamic Pricing relationships
(Jonason, 2002) earlier observed that the traditional micro-economics methods (Supply-Demand) for
establishing price levels may not entirely apply in the new digital economy. Internet Services could be
priced based on spill over effects rather than cost-recovery basis. The traditional pricing approach
considers the Product, Owner and Pricing as well known and distinct entities but in the digital
economy, the distinction is blurred and dynamic.
He noted that pricing mechanisms in digital services are part of the product and the owners of the
digital product maybe different from the owners of the Pricing mechanism. The total price of the
product therefore is not based on the cost of the product alone, but includes the cost of the charging
mechanism. Furthermore, the dichotomy between Seller and Buyer keeps changing, such that at one
point the buyers are the sellers and vice versa.
These complex relationships that complicate the pricing of services is also observed by (Fransman,
2009)) who described them as symbiotic and documented them as shown below:
4
CONSUMERS
Relationship
Relationship
6
3
Relat.
4
3
2
NETWORK
OPERATORS
Relat.
PLATFORM,
CONTENT &
APPLICATIONS
PROVIDERS
2
Relationship
Relationship
1
1
5
NETWORKED
ELEMENT
PROVIDERS
Figure 1 Relationships in the new ICT Ecosystem
Essentially, he outlines how innovation and value is created through interactions between various
autonomous stakeholders. He identifies six symbiotic and dynamic relationships that connect together
diverse stakeholders that include: (1) Hardware providers (2) Network Operators (3) Content &
Application providers (4) Consumers. Each of these players may have a role to play in deciding the
price of the communication service.
This is supported by (Bauer, 2014a) who observed the rise of Multi-Sided Platform (MSP) markets,
whereby Content providers, Application providers and Network operators can be in fluid relationships.
In such circumstances, the issue of pricing of services has to move beyond the traditional cost-based
approaches. This is particularly the case when the service can actually be offered at no charge through
Zero Rating, given the understanding that revenues will be recouped from other sources such as
8
advertisements.
The implication is that the final price set for a service is not within the functional control of one
individual player within the ICT ecosystem. It also therefore means that any regulatory intervention
that focuses solely on one or two of stakeholders without thoroughly working through the impact this
may have on rest of the relationships is likely to be more harmful than beneficial. This calls for
dynamic regulation as described by (Bauer & Bohlin, 2008).
This research work aims to contribute to this body of knowledge by designing a regulatory pricing tool
that explores the use of other parameters, over and above the traditional cost-based ones, in dealing
with the question of pricing contemporary communication services. In addition, given the observed
dynamic nature of the new communication services, the System Dynamics paradigm presents a suitable
methodology for exploring issue.
It is expected that the tool would provide the regulator and operator a better understanding of the
telecommunications market dynamics and subsequently trigger more effective pricing policies for the
sector.
9
SECTION 3: Theoretical /Conceptual Framework
Generic Telecomms Models
(Walrand, 2008) captured quite clearly the closed looped interactions and relationships between Users,
Operators and the Network (Capacity). Additionally, she demonstrated how Prices, Revenues,
Investments, Demand and Quality of Service were interrelated as outlined below:
Figure 2 Techno-Economic Model (Walrand 2008)
She went further to categorize these relationships under the Economic, Regulatory and Technological
layers and argued that the performance of communication networks (QoS) has often been studied
extensively but always in isolation. This meant that there was little attention as to how QoS links into
the Economic and the Regulatory layers. She subsequently proposed the Techno-Economic model
above as a framework for holistically studying the telecommunication network.
(Rouskas and Lv 2009) study followed the Techno-economic model but focused on deriving the
optimal number of internet service tiers or levels for a given population of users. They used
mathematical optimization techniques to calculate the number of service classes that would return the
highest profit from a given population of users..
Figure 3 User, Operator, Social Surplus (Rouskas 2009)
10
They used game-theoretic approaches in determining the optimal pricing for the given service
class/tier. Typically, the Operators aim to increase or maximize their “Supplier Surplus” that is their
Profit – subject to their Network Capacities.
Supplier Surplus = Price (x) – Cost (x) where………………………….(Eqtn 1)
 Price (x) = Unit Price of (x) amount of Bandwidth offered
 Cost(x) of Supply = Cost of Supplying (x) amount of Services.
The Users aim to increase or maximize their “Consumer Surplus” that is their Net Benefit – subject to
their budgetary constraints.
Consumer Surplus = Utility(x) –Price(x) where…………………………..(Eqtn2)
 Utility(x) = The value or amount User is willing to pay for x amount of Bandwidth
 Price (x) = Unit Price of (x) amount of Bandwidth offered
The Regulator aims to increase or maximize the “Social Surplus” that is the total benefit for both the
consumers and suppliers in the market.
Social Surplus = Supplier Surplus + Consumer Surplus………………..(Eqtn3)

The estimated or expected Price of Service is a negotiated price and lies between the Supplier
Surplus and the User Surplus
The comprehensive use of the regulatory concepts of Utility, Price, Cost, User Surplus, Operator
Surplus and Social Surplus is adopted in the proposed System Dynamic Regulatory view of the
Telecomms sector
System Dynamic Telecommunication Models
Several System Dynamics (SD) studies such as (Jain & Sridhar, 2003) Bushan et al 2011;(Mayoka,
Musa, Rwashana, & Rwashana, 2012) (Vaishnav, 2010)) amongst others have been done around
telecommunication services, regulation and pricing. However, (Dutta 2001) model was selected given
its ability to capture most of the regulatory indicators behind this study.
11
Figure 4 System Dynamics Telecommunication View (Dutta 2001)
It looks at the Telco business from an Operators point of view and incorporates the major components
such as the Customer, Finance and Network views. Dutta developed, tested and validated this
comprehensive model which was extended and adopted in this study. The System Dynamic
interpretation of (Rouskas et al 2009) model was proposed and linked into the Dutta (2001) Model as
the Regulatory engine for managing the telecoms sector.
Proposed Conceptual Framework
Having noted from literature that pricing of communication services is heavily reliant on cost-based
approaches, we endeavor to built on that by presenting a model that acknowledges the cost aspects of
operating a telecommunication network. However, we extend the model to take into consideration new
realities brought about by the Internet medium.
This includes the fact that new relationships between different actors within contemporary
communications network imply that prices charged can be based on other parameters – as long as the
operator is able to make reasonable returns to sustain and expand their network.
The proposed model therefore shifts the focus away from the low-level, often complex costing of
telecommunication variables and looks at pricing at a higher, macro level. The macro level approach
analyzes the industry in terms of regulatory variables of Quality of Service (QoS), Investment potential
(Profits), Market penetration, Capacity expansion and makes inferences to the pricing behind the
prevailing market indicators.
The top level, conceptual view of the proposed model is given below:
Figure 5 Conceptual View -Regulatory Pricing Model (Author 2016)
12
(Rouskas et al 2009) model was recast in System Dynamics terms and considered a good basis for
replicating regulatory behavior and its output became input into an extended Dutta’s 2001
telecommunications model. These two models are interlinked to form the core component of the
proposed regulatory model (Author, 2016)
Acknowledging that the regulatory objective is to maximize both the consumer and the telecomm
provider surplus by ensuring fair competition and universal access (ITU,2010), the pricing problem is
therefore redefined into one of optimization; whereby we seek the optimal price possible, subject to
selected regulatory objectives and parameters as given below:
i)
Maximum Market Penetration:- regulatory universal access obligation that aims to reach
widest possible number of users.
ii)
Maximum Operational Income:-regulatory obligation that ensures operators can sustain
operations while making reasonable returns to attract investments.
iii)
Minimum Quality of Service:-regulatory obligation to ensure an acceptable network
quality(performance) in order to protect users rights to reliable services.
Description of Proposed Model
The proposed model considers a telecommunication service provider operating in a potential market
size of 1,200 customers for a period of ten years. The service provider is assumed to start off with 100
customers, 100GB/s Network capacity and is offering internet services of 0.5GB/s per customer. The
price of the service depends on a scale ranging from (0-1) and depends on the level of competition.
Competition was modeled on a varying scale of (0-1), with Low (0) competition leading to Higher
prices (0.75), and moderate competition (0.5) leading to moderate or Base prices of (0.5) and high
competition (1) leading to Lower prices (0.35).
13
Figure 6 Proposed System Dynamics Price Model:-Regulatory View (Author 2016)
The objective was to provide the regulator with a policy interface that links into (Dutta 2001)
telecommunication sector model that was extended to contain variables of regulatory interest such as
the Internet Market Penetration. (See Appendix for full model)
14
SECTION 4:METHODS & DATA SOURCES
System Dynamics Approach
(Forrester, 2009), considered the father of System Dynamics (SD) defined SD as the study of
information-feedback characteristics of industry activity to show how organizational structure,
amplification (in policies), and time delays (in decisions and actions) interact to influence the success
of the enterprise.
System Dynamics is an approach developed to understand how the interaction between policies and
structure of an entity determines its behavior. Dynamic models are those that try to reflect changes in
real or simulated time and take into account that the model components are constantly evolving as a
result of previous actions (feedback effects).
There are several variations of implementing SD ((Forrester 1994); (Lane & Oliva, 1998) (Williams,
2000) but all are based on the standard system dynamic steps as summarized by (Sterman 2000)).
Figure 7 System Dynamics Steps (Sterman 2000)
15
Sterman (2000) recommends the following detailed and disciplined approach that was adopted towards
building the proposed model.
1. Problem Articulation (Boundary Selection)
• Theme selection: What is the problem and why is it a problem?
• Key variables: What are the key variables and concepts we must consider?
• Time horizon: How far in the future and how far back should we consider the roots of the
problem?
• Dynamic problem definition (reference modes): What is the historical behavior of the key
concepts and variables and what might their behavior be in the future?
2. Formulation of Dynamic Hypothesis
• Initial hypothesis generation: What are current theories of the problematic behavior?
• Endogenous focus: Formulate a dynamic hypothesis that explains the dynamics as endogenous
consequences of the feedback structure.
• Mapping: Develop maps of causal structure based on initial hypotheses, key variables, reference
modes, and other available data, using tools such as: Subsystem diagrams, Causal loop diagrams
(CLD), Stock Flow Diagrams (SFD)
3. Formulation of a Simulation Model
• Provide a specification for structure & decision rules.
• Provide estimation of key parameters, behavioral relationships, and initial conditions.
• Test for consistency with the purpose and boundary.
4. Model Testing
• Comparison to reference modes: Does the model reproduce the problem behavior adequately
for purpose?
• Robustness under extreme conditions: Does the model behave realistically when stressed by
extreme conditions?
• Sensitivity: How does the model behave given uncertainty in parameters, initial conditions,
model boundary, and aggregation?
5. Policy Design and Evaluation
• Scenario specification: What environmental conditions might arise within the sector?
• Policy design: What new decision rules, strategies, and structures might be tried in the real
world and how can they be represented in the model?
• “What if. . .” analysis: What are the effects of the policies?
• Sensitivity analysis: How robust are the policy recommendations under extreme input
variations?
• Interactions of policies: Do the policies interact? Are there synergies or compensatory
responses?
16
Data Sources
The data collected is centered on answering the dynamic hypothesis questions regarding how Internet
prices are related to the following variables:
a) Internet price relates to or influences market penetration
b) Internet price relates to or influences profit margins (Return on Investments,ROI)
c) Internet price relates to or influences quality of services (QoS)
d) How competition influences internet pricing
Using literature reviews and interviews from industry experts, we estimated the following functional
relationships and decision points that eventually became the decision equations that drive the model
output.
1
2
3
4
Decision
Point,
Key
Decision Policy/Rule
Relationship,
How does
Internet
Price
influence
Market
Penetration
Internet Price
& Market
Penetration
How does
Internet
Price
influence
Profit
Margins
(ROI)
Internet Price
& Profit
Margin
Higher prices lead to
better profit
margins(ROI).
Economic Theory of
Supply/Demand
How does
Internet
Price
influence
QoS
Internet Price
& Quality of
Service
Lower prices lead to
increased subscribers
which subsequently
lead to lower QoS
Erlang Theory of Teletraffic
Engineering
How does
Competition
influence
internet
Competition
& Internet
Price
Higher competition
drives down the prices
of internet services
Economic Theory of
Supply/Demand
Lower prices increase
uptake and
consequently imply
higher market
penetration
The converse would
apply, higher prices
lead to lower market
penetration
17
Supporting Theory/Data
Sources
Diffusion Models (Bass,
Krishnan, & Jain, 1994),
Logistic Growth Models
(Sterman, 2000)(Dutta &
Roy, 2004)
(Goel, Hsieh, Nelson, & Ram,
2006)(Rouskas & Lv,
2009)(Casier et al.,
2009)(Walrand, 2008)(Dutta,
2001)
(DaSilva, 2000)(Roberts,
2004)(Courcoubetis &
Weber, 2003)(Davies, Hardt,
& Kelly, 2004)
ITU (Jain & Sridhar, 2003)
prices
5
How does
Regulation
influence
internet
prices
Regulation &
Internet Price
Quality regulation
drives down the prices
of internet services.
Economic Theory of
Supply/Demand
(Bauer, 2014b)(Bauer &
Shim, 2012)(Frederiksen,
2011b)(Bauer & Bohlin,
2008)(Casey & Töyli, 2012)
Increased Competition
is the common
regulatory approach
Table 1 Key Relationships & Theories
Dynamic Hypothesis (Casual Loop Diagram)
A Dynamic Hypothesis explains the dynamics of a system in terms of the endogenous consequences of
the feedback structure (Sterman 2000). It also demonstrates how decision policies and structure interact
to generate system behavior. The proposed model is based on the dynamic hypothesis described below
using Casual Loop Diagrams (CLDs).
+
Supply(Competition)
B4
Profit (Operation +
Income)
B5
+
+
B2
Demand(Market
Penetration)
B3
Ideal Price
+
- Price Gap
R1
+
Regulation
R2
+
Current Price X
+
Net Performance(QoS)
B1
Price Adjustment
+
+
Adjustment Rate
Figure 8 Key Relationships between Price & Regulatory Variables (Author 2016)
There are two types of loops at play. The positive (reinforcing, R) and negative (balancing, B) feedback
loops. A feedback is said to be positive or reinforcing if an increase in a variable, after a certain delay,
leads to a further increase in the same variable. Reinforcing loops often generate exponential growth
(positive reinforcement) or collapse (negative reinforcement)
18
On the other hand it is negative or balancing if an increase in a variable leads to a decrease of the same
variable after a certain delay. Negative feedback often drives balancing or stabilization of systems that
produce oscillatory behaviour.
Key dynamics of how these variables impact on pricing are captured by the six loops described below.
MODEL DEVELOPMENT
Balancing Loops
We review four of the Balancing loops (B1, B2, B3 & B4) below:
i) The Investment loop (Balancing loop B1), becomes active when competitive pressure mounts on
the operator to reduce prices (price wars). The operator however cannot reduce prices beyond a given
price point that reflects the cost of production. Below this price, the operator begins to make loses and
the market is no longer attractive for investments.
ii) The Competition loop (Balancing loop B2), describes the supply side, whereby high prices
eventually attract more players into the market, who in turn put more pressure on the prices, forcing
them down in an attempt to gain customer share. The proposed model estimates this relationship as
shown below.
Figure 9 Estimated Price-Competition Relationship
Increased competition puts pressure on prices. X-axis shows the modeled competition value as a ratio
the maximum competition (0-1). When the competition (X-Axis) is at moderate level (0.5), the effect is
that the price is NOT impacted given the multiplier effect factor of 1.0 (on Y-Axis). At the lowest
competition level (0), the given price is modeled to increase by a factor of 25% while decreasing by a
similar amount when competition is highest (1).
19
iii) The Market Penetration/Demand loop (Balancing Loop B3) describes the demand side, whereby
increased prices depresses demand while reduced prices increase demand. The proposed model
estimates this relationship as shown below.
Figure 10 Estimated Price-Market Penetration Relationship
Lower prices increase demand. X-axis shows the modeled price value as a ratio the maximum price (01). When the price (X-Axis) is at moderate level (0.5), the effect is that 50% of the market can afford
the service given multiplier effect factor of 0.5 (on Y-Axis). At the lowest price level (0), 100% of the
total market is assumed to afford the services, while the at highest price level of (1), only 10% (0.1) of
the market affords the services.
Once subscribers have been acquired, they are however not static and are further modeled to join and
leave the network depending on acquisition, natural attrition and prevailing quality of service levels.
iv) The Profit loop (Balancing loop B4&B5) describes the behavior of price with respect to profits.
An increase in prices increases profits for the operator. However, if the high prices are sustained, more
competitors enter the market and subsequently prices drop leading to lower profits as competition
intensifies.
Operational Income = Revenue – Expenses………………………… (Eqtn 4)
Revenue =Price * Total Number of Customers……………………… (Eqtn 5)
Expenses = Fixed Costs * Variable Costs……………………………. (Eqtn 6)
The impact on Operational Income is dictated by total number of subscribers and the corresponding
concave effect this number has on the variable costs.
20
Reinforcing Loops
The two Reinforcing loops (R1 & R2) are reviewed below:
i) The Technology loop (Reinforcing loop, R1), reflects a constant reduction on price based on some
adjustment rate. During this period, the operator may chose to reduce prices based on efficiency gains
arising from technological developments. This could be due to availability of newer or cheaper
equipment that can deliver twice the performance of its earlier predecessor.
ii) The Congestion loop (Reinforcing loop, R2), describes the behavior of price with respect to
quality of service (QoS). Reducing prices attracts more subscribers who in turn put more traffic load on
the network. Increased traffic subsequently reduces the quality of service, leading to customer attrition
as they seek better or alternative services. The proposed model estimates this relationship as shown
below.
Figure 11 Estimated Traffic Load-QoS Relationship
Lower prices increase traffic load. X-axis shows the modeled traffic load as a ratio of overall Network
Capacity (0-1). When the network is fully occupied at level (1), the multiplier effect is that the Network
Performance (Y-Axis) is degraded to 0%. At half load level (0.5), or 50% occupancy, the network
performance is considered to be operating at (0.75) 75% performance levels, while a small load of 25%
or below, gives a high performance level of 100
21
Model Testing
(Martis, 2006) defines model testing as a validation process of determining that the model on which the
simulation is based on is an acceptable and adequate representation of reality. Further he observed three
key steps in enhancing confidence in the model.
These are namely the Validation, Verification and Credibility steps as shown below:
Figure 12 Validation, Verification & Credibility Process
(Martis 2006)
Conceptual Model Validation: was done whereby the theories and assumptions underlying the
conceptual model were shared with selected industry & regulatory experts for interrogation. Their
feedback was used to fine-tune the proposed conceptual model and ensure it matched the problem
entity.
Computerised Model Verification: was done with the data & feedback structure tests being the focus
and link between the conceptual model and the computerized model. The model development section
captured this verification process that ensured that the data necessary for model building, evaluation
and model simulations was adequate and realistic.
Operational Validation: was done whereby the data patterns of the simulation was interrogated and
compared to documented real-life data patterns (Fig 13) from regulatory reports. Additionally, selected
industry & regulatory experts were tasked to critique the simulation output by comparing it against
their expert experience.
22
18
16
14
12
10
8
6
4
2
0
4.62
6.00
16.4
5.215.00
4.00
3.95
9.9
2.53
2.25
2.00
4.7
5.9
3.00
2.00
Ratio
Percentage
Internet Makert Trends (2010-2015)
Broadband Mkt Pen. (%)
(Rev/Invest)
1.00
0.5
0.7
0.00
2010 2011 2012 2013 2014 2015
Year
Figure 13 Internet Market Trends, Kenya (Economic Survey 2016)
The figure shows the live data for Broadband Market Penetration and the Revenue/Investment Ratio in
Kenya for the year 2010-2015. The upward trends for both variables are in line with the simulated data
for Market Penetration and the Operational Income. The Quality of Service (QoS) data is not available
since it yet to be collected by the regulator.
23
SECTION 5: Results, Analysis & Discussions.
The telecommunication provider is initialized to provide services for 10years (120 Months) without
investing in network capacity expansion. The performance of regulatory indicators of interest, namely
the Market Penetration, Network Performance (QoS) and Operational Income are maximized. This is
by way of setting the optimization objective of maximizing both User and Operator Surplus. A
moderate competition and service level of 0.5 is selected as the initial values.
After searching through twenty-six (26) simulations runs, the optimization algorithm made the
following returns:
Initial point of search
AvgTraffic = 0.5
Competition Level = 0.5
Simulations = 1
Pass = 0
Payoff = 15411.8
---------------------------------
Maximum payoff found at:
AvgTraffic = 0.5
*Competition Level = 1
Simulations = 26
Pass = 3
Payoff = 18074.9
---------------------------------
This means that a higher competition level of (1), combined with a service category level of (0.5)
delivered the best social & regulatory outcome. The results for the various parameters are detailed
below:
Maximizing Social & Operator Surplus (Social Surplus)
The Steady-state value for User Surplus occurred around month 96 and was initially at 90.7. Upon
optimization, a higher User Surplus value of 148 was deduced; which was an increase of 38.7%.
Figure 14 User Surplus - None & Optimized Comparison
24
The Steady-state value for Operator Surplus occurred around month 96 and was initially at 64. Upon
optimization, a lower Operator Surplus value of 39 was deduced; which is a drop of 39%.
Figure 15 Operator Surplus - None Optimized & Optimized Comparison
The Steady-state value for over Social Surplus occurred around month 96 and was initially at 155.
Upon optimization, a higher Social Surplus value of 187 was deduced; which was an increase of 39%.
Figure 16 Social Surplus- None Optimized & Optimized Comparison
25
Resulting Price Level
Flight Sim_Base_Run
Flight Sim_Optimized_Run
PriceX @ 120
.5
.45
.4
.35
.3
PriceX
Figure 17 Price – None Optimized & Optimized Comparison
Price was initially set at 0.5, with the optimization algorithm suggesting a lower price value of 0.375;
which was a price reduction of 25%. Irrespective of the reduction value, what is more important is that
Price has been deduced for a given set of initialized market conditions.
Resulting Competition Level
Flight Sim_Base_Run
Flight Sim_Optimized_Run
Competition Level @ 120
1
.85
.7
.55
.4
Competition Level
Figure 18 Competition – None Optimized & Optimized Comparison
Competition was initially set at 0.5, with the optimization algorithm suggesting a maximum
competition value of 1, which was an increase of 50%.
26
Resulting Market Indicators (Outcomes)
The graphs for the optimized variables of Market Penetration, Network Performance & Operational
Incomes are now discussed below:
Figure 19 Market Penetration - None & Optimized Comparison
The Steady-state value for Internet Market penetration occurred around month 96 and was initially at
0.21. Upon optimization, a higher penetration value of 0.25 was deduced; which was an increase of
23%.
Figure 20 Network Performance - None & Optimized Comparison
The Steady-state value for Network Performance (QoS) occurred around month 96 and was initially at
0.45. Upon optimization, a lower performance value of 0.21 was deduced; which was a performance
reduction of 53%.
27
Figure 21 Operational Income – None Optimized & Optimized Comparison
The Steady-state value for Operational Income occurred around month 100 and was initially at 104.
Upon optimization, a lower Operational Income of 85 was deduced; which is a drop of 18%. Despite
the drop, the operational annual incomes still gives a positive Net Present Value (NPV)1.
NPV calculations assume a typical discount rate of 15% and a project span of 10 years. Using, the
simulated annual cash-flows (Operational Income), the projected cash flows came to a net present
value 302.92 units, which is greater than the initial investment amount of 100 units. This gives a
positive NPV value of (302.92 -100)=202.92, indicating the project remains profitable even under the
optimized regulatory circumstances.
Figure 22 Market Penetration, QoS & OpIncome - Combined View
The graph shows the combined behavior pattern for the regulatory variables. As the Market penetration
increases, Operational Incomes also increase but Network performance drops significantly.
1
𝐶𝑡
𝑁𝑃𝑉 = −𝐶𝑜 + ∑𝑇𝑡=1 (1+𝑟)𝑡
where Ct=Cashflow during period t, Co=Total Investment Cost, r=Discount Rate, T=no of time periods
28
Discussions
The optimization algorithm executed twenty six runs as it searched through multiple parameter
combinations that would maximize the regulatory objective of increasing the Social (User & Operator)
surplus. It increased the Social surplus by 20%, despite dropping the Operator surplus by 39% while
increasing the User surplus by 38.7%.
The optimization algorithm settled on an optimal price level of 0.375, which was a reduction of 25%
over the initial price. It chose a competition level of 1, which was a 50% increase in competition levels
for the given service (traffic) level of 0.5 per subscriber.
For the given optimized price, the search algorithm gave an improved market penetration by a value
0.26, which was a 23% increment over the initial market penetration. This was however at the expense
of a 53% drop in network performance (QoS). The operational incomes also dropped by 18%; even
though the return on investment (NPV) was still positive.
This suggests that in searching for affordable or optimal prices that would increase communication
services uptake, regulators may perhaps need to allow for a certain level of degraded service.
Alternatively, strict quality of service may still be achieved but at a cost of higher prices with
potentially lower market penetrations.
Finally, whereas the observed drop in the price is insightful, what needs more emphasis is the fact that
a framework for deducing optimal prices for contemporary communication services has been proposed
and demonstrated.
a)
b)
c)
d)
e)
f)
a)
b)
c)
Telco
Initial
Optimized
Change
%Change &
Variable
Value
Value/Outcome
Direction
Regulatory Objective Function: Maximize User & Operator Surplus (Social
Surplus)
User Surplus
90.7
148
53.3
Up 38.7%
Operator
64
39
-25
Down 39%
Surplus
Social Surplus
155
187
32
Up 20%
Subject to the following Parameters
Price
0.5
0.375
-0.125
Down 25%
Competition
0.5
1.0
0.5
Up 50 %
Service
0.5
0.5
0
Static
/Traffic Level
Market Indicators (Outcomes)
Market
0.21
0.26
0.05
Up 23%
Penetration
Network
0.45
0.21
-0.24
Down 53%
Performance
Operational
104
85
-19
Down 18%
Income
Table 2 Summary Results - Initialized vs. Optimized Values
29
SECTION 6: -Conclusion & Recommendations
This study has proposed a model and demonstrated how it can be used to determine optimal prices of
converged telecommunication service. The model also enables regulators to understand and interrogate
the dynamic relationships of the various market indicators within the ICT sector.
The model can be improved further by calibrating it for a specific telecommunication network such as
an existing mobile operator network or a fixed line (cable) operator. Additionally, the average price
perspective can be further broken down to establish which proportions of the various income group
categories can afford the services.
This will mean initializing the model with actual functional relationship values that uniquely reflect the
realities of that operator and the corresponding economic environment.
Finally, emerging issues that may influence pricing such as Innovations, local content, Internet
eXchange Points (IXPs) amongst others can also be explored and their corresponding constructs
developed and interfaced into the model.
One arguable limitation is that regulators do not routinely regulate or fix prices at retail levels,
preferring to let competition determine prices. However, the new model enables regulators to precisely
do that by influencing the optimal retail prices through higher-level regulatory objectives.
By knowing which optimal price gives a particular regulatory outcome, regulators can authoritatively
dictate market penetration and quality of service performance indicators for the operators to achieve
within a given time frame.
30
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APPENDIX- SD Model & Equations
Appendix I:-System Dynamics Model for Telecomms Market ..................................................................
Appendix II:-Equations for System Dynamics Model for Telecomms Market ..........................................
33