INDIVIDUAL TARIFFS FOR MOBILE SERVICE BUNDLES
A Negotiation Calculation Tool
Louis François Pau, Hong Chen
Rotterdam School of Management, Burg. Oudlaan 50
3000 DR Rotterdam, The Netherlands
Keywords: Individual tariffs, mobile services, computational games, mobile music, negotiation, SLA.
Abstract: This paper aims to develop a bargaining model for calculation of individual tariffs for mobile service. In
almost all publmic communication services today , tariffs are set unilateraly by the suppliers for some
generic services ; the present and future emphasis on both unique content needs,and on service
personalization, restrict the old tariffing principle to those users who only need basic universal services .
Realizing this evolution , the ultimate goal is to provide a tool for computing individual tariffs. The paper
first looks at the intrinsic drivers of individual tariffs both from sociological and economic perspectives. The
paper proceeds further with a bargaining model for individual tariffs which is centered on user and supplier
behaviours. The user, instead of being fully rational, has “bounded rationality” and his behaviours are not
only subject to economic constraints but also influenced by social needs. The supplier can be either a firm
(typically a communications service operator) or community ; each supplier has his own goals which lead
to different behaviors. In the proposed solution, individual tariffs are decided through interactions between
the user and the supplier. Game theory is employed to provide structured analyses of the interactions and
tariff design. We developed a computational model based on the bargaining model. It can be used to
determine the individual tariff between a firm and an individual user. Preliminary results, which are based
on a music training service treated as a bundle of communications, content, and assistance , show that
individual tariffs can be beneficial to both the user and the supplier.
1 INTRODUCTION
Individual tariffs existed at the dawn of the telecom
history. Due to the limited supply and demand,
tariffs were negotiated between the individuals and
the telephone companies. Individual tariffs faded out
when telecom industry began to thrive in the early
20th century under economies of scale. Users started
to pay same prices for standard services.
Today, individual tariffs exist in industries such
as airline, travel and hotel, where prices are
associated with booking time, booking history,
restrictions the individual users willing to accept,
etc. In telecom industry, customer-specific tariffs
widely exist at enterprise/group level. The tariffs
mainly depend on aggregated amount that the
customers intend to buy. Looking back, most tariff
models in use today in telecom (fixed and mobile)
were derived from those of physical goods,
assuming limited capacity in either bandwidth or
transmission capacity. This worked well when there
were limited types of standardized services. The
rapid developments in technologies and social
environment have changed the scope of telecom
services (Chen & Pau,2004).
This paper aims to develop a bargaining model
for the development of computational models of
individual tariffs. The ultimate goal is to provide a
tool so that the determination processes of individual
tariffs are automated or semi automated and the
prohibitive service provisioning overhead is
avoided.
2 BASIC CONCEPTS
In order to define individual tariffs, the opposite is
defined first. Public tariffs in telecommunication
refer to the regulatory protected ability for an
identified user to obtain from a service provider, by
a bilateral contract, a set of standard prices for a set
of standardized services
Individual tariffs in telecommunications refer to
the regulatory protected ability for an identified user
31
François Pau L. and Chen H. (2006).
INDIVIDUAL TARIFFS FOR MOBILE SERVICE BUNDLES - A Negotiation Calculation Tool.
In Proceedings of the International Conference on e-Business, pages 31-37
DOI: 10.5220/0001424400310037
Copyright
c
 SciTePress

to obtain from a service provider, by a bilateral
specific contract, a set of service specific prices
corresponding to a request or a proposal from the
user specified with a service demand profile and
some duration.
The users of individual tariffs are the recipients
of services. The service provider/supplier is defined
in a broad sense as the entity that provides access,
content and applications, or a combination of them
to users. We identify four types of service providers:
firms; closed communities where membership is
required; open communities which do not require a
formal membership and ultimately, individuals.
In e-Commerce, some concepts exist akin
individual tariffs , e.g. in sales automation for e-
procurement .The main difference with the present
research is that those experiments do not include
content but only business process management or
information distribution aspects . The present
research also incorporates the case where the
supplier is an e-community driven by other
preferences then just profit or market share .
In this research, we focus on mobile services
first because the huge demands on the quantity and
diversity of mobile bundles. Second, mobile gadgets
are widely deemed integral and intimate in a
person’s daily life. The highly personal nature of
them allows mobile services to be personalized,
which is a prerequisite of individual tariffs.
3 DRIVERS AND BARGAINING
MODEL OF INDIVIDUAL
TARIFFS
3.1 Intrinsic Drivers of Individual
Tariffs
From a sociological perspective, a post-modern
society is characterized by its lack of dominant
ideology, culture or fashions. This is also reflected
in the diversity of personal values which give
meanings and directions to an individual’s
behaviours. Not all individual users are willing to
consider personalized services and tariffs. Some
prefer a pre-determined bundle with little
transparency and limited choices. But there are
values held by a growing population inviting
personalized services and individual tariffs. Here is
a non-exhausted list of drivers that we consider to be
fundamental.
Individualism. This paper follows the definition
of individualism defended by (Hayek , 1980).
Under this individualism, there are universally
accepted principles under which man makes his own
choices and take full responsibility; he is free to
follow his own will, to make full use of his
knowledge and skill, and he is guided by his
concerns for the particular things of which he knows
and he cares. Personalized mobile services and
tariffs are reflections of Hayek’s individualism;
where a person in a free society has the freedom of
choices of services, at anytime and anywhere. It is
also reflected in the freedom of service creation and
provision, either to a family, a community, or to the
whole society.
3.2 Economic Incentives of
Individual Tariffs
Price discrimination. The concept was coined by
(Pigou , 1920), who distinguished three types of
price discrimination. Different types of price
discrimination have different welfare effects in
terms of maximizing consumer plus supplier surplus.
Theoretically, first-degree price discrimination leads
to a Pareto efficient outcome. Early analyses of price
discrimination were done under monopolistic
settings and about physical goods; the supplier’s
technologies involve no economies of scope, and
usually possess constant or decreasing return to
scale. Other dimensions of price discrimination have
been studied by (Eden ,1990), (Levine, 2001),
(Varian, 1996) .
Current technologies already permit suppliers to
track and trace user behaviours and infer their
preferences so as to provide services accordingly.
Willingness-to-pay. WTP is the maximum amount
of money the user is prepared to pay for a
service/bundle , which is a measurement of value
that the user put to the service. WTP is higher when
attributes of a service meet precisely the user
demands, which is also one of the economic reasons
that call for personalized services and tariffs.
By involving the consumers in a service design
through interactions, users’ specific demands are
identified and integrated to the service. User’s WTP
is higher than the comparable standard services,
ceteris paribus (Franke, 2004).
Risks. The four possible ways to provide
individual tariffs for mobile services lead to different
risks, not only to service providers but sometime to
the end-users.
Individual tariffs introduce more risks to end-
users who bear little risks under public tariffs.
Specifically, the risks can be over-committing or
over-consumption which may lead to a service
disruption. As a consequence, the individual may be
denied access from others or access to the
information society. Suppliers, i.e. firms,
communities or even individuals, share a common
ICE-B 2006 - INTERNATIONAL CONFERENCE ON E-BUSINESS
32

goal when providing individual tariffs: to minimize
risks. The thinking of sure/certain profit from users
is currently dominant in mobile as well as other
telecom industry. Individual tariffs are calling for a
change to allow uncertainty in revenue from each
individual user. The guiding principle, which has
already been recognized in insurance industry for
hundreds of years, is to have a positive profit on
average. Insurance alleviates financial losses by
transferring risk of loss from one entity to another by
method such as pooling. There is no research
applying the “pooling” thinking to differentiated
telecom services and tariffs, where the focus will be
on pooling the user demands and willingness-to-pay
for a service. At individual level, each user’s
demands for a service may seem unique and serving
them may be costly. But for a supplier who serves
many users, the pooling of the demands offer market
potential. Furthermore, by pooling, the negative
profits from individual users are allowed as long as
the aggregate profit remains positive, which
generates an overall robust business.
3.3 User Behaviours
A user can be characterized as fully rational and
self-interested. The model is used broadly in
economic and other social sciences. However, many
researchers have found limits in this model.
3.3.1 Full and Bounded Rationality
The strict definition of full rationality states that, an
individual’s preference relation is rational if it
possesses the properties of completeness and
transitivity. It means the individual is able to
compare all the alternatives and the comparisons are
consistent. Furthermore, rationality implies that the
individual has complete information of all
alternatives and knows about the consequences of
his choices; he also has unlimited time and unlimited
computational power to pick his most preferred
option. In reality, such perfectly-rational person
never exists.
Herbert Simon has pointed out that the
individual’s preferences do not possess the rational
prosperities when comparing heterogeneous
alternatives. Simon characterized this as “bounded
rationality”. Model construction under bounded
rationality assumption can take two approaches.
First is to retain optimization, but to simplify
sufficiently so the optimum is computable. Second is
to construct satisficing model which provides
decisions good enough, with reasonable
computational cost (Simon, 1979). Neither approach
dominates the other. Related work is found in
(Tversky, 1974), (Kahneman, 2002) .
3.3.2 A Social Dimension
The self-interested property implies that economic
man is amoral and has no sense of right or wrong.
He ignores all social values unless adhering to them
gives him benefits; his preferences are exogenous
and not affected by societal environment at all.
However, it is never true. In choosing to act,
individuals commonly consider the consequences of
actions not only for themselves but others as well;
they have social preferences (Bowles, 2004). We
contend that the social preferences of mobile
services are decided by benefits that an individual
elicits from the interactions under different social
environments and with different people. Major
factors affecting social preferences are social
context, and content ,especially as content occurs to
confirm a relationship (Licoppe, 2003).
3.3.3 Modified Behaviour Model
The art of decision making is to obtain a complete
ranking of the alternatives that reflect the
preferences. Very often, this is done by assigning a
numerical value to each alternative. The number is
usually called utility. Specifically, we consider two
types of utilities of mobile communication services,
namely economic utility and social utility. Many
preferences, especially social preferences, are
partially rational or irrational. Therefore many
situations can not be described by utilities but only
by preferences. Here we assume that there are partial
preferences, which can be mapped out by types and
contexts. If a selection of a subset of preferences
leads to a locally monotonic function, then there
exists a utility function that can be used for
computational purposes.
A mobile service normally has multiple
attributes; the utility function is then constructed by
following the method from multiple attribute utility
theory. First, a utility function for each service
attribute is assessed. Then a multiple attribute utility
function determines how the level of one attribute
affects overall utility vis-à-vis a set of assessed
weights of relative importance. The individual tries
to optimize his utility. Due to his bounded
rationality, his optimizations are carried out in a
simple way. When making a decision, the individual
uses satisficing rules and tries to achieve an
acceptable level of utility before he stops.
INDIVIDUAL TARIFFS FOR MOBILE SERVICE BUNDLES - A Negotiation Calculation Tool
33

3.4 Supplier Behaviour
We also take a utilitarian approach when modelling
a supplier’s behaviour. When the supplier is a single
firm, economic utility is elicited from economic
benefits such as profit or market share, which is
generated by service offering. If we expand the
analysis further, a supplier also has social
preferences for his decisions (e.g. environmental
preferences). There may be conflicting goals over a
supplier’s economic utility and social preferences;
he will try to achieve equilibrium/equilibria between
them. However in this research, we assume that the
supplier derives only economic utility from service
offerings. A firm seeks to achieve maximum
economic benefit and at the same time minimum
risks.
The goals of a community, when offering mobile
services, are to achieve financial breakeven and
minimize service provisioning risks.
4 ANALYTICAL DESIGN
CALCULATION USING
COMPUTATIONAL GAME
THEORY
The main advantage of game theory is that it
provides structured analysis of decisions, which are
made as reactions to another player’s decisions.
Over years, game theory has evolved to incorporate
“bounded rationality” in its analyses (Aumann,
1997) . Further, the cooperation between disciplines
such as computer sciences, artificial intelligence and
economics gave birth to computational game theory
which enables richer ways of modelling complex
problems of interactions in an efficient way by
computers. One such example is the ability to
incorporate some aspects of the sales psychology
inherent to bargaining ,e.g. COSIM .
Individual tariffs are decided by the interactions
between the user and the supplier. The bilateral
contracting procedure between them can be
modelled by an imperfect information game, where
the payoffs are the utilities that both parties receive
from the service. In general, the negotiation process
is modelled by a recursive Stackelberg game, where
the first player has a dominant influence over the
followers. We empower the user by letting him
move first. Different decision rules and constraints
can be applied to investigate the equilibrium, if it
exits, when the individual sets his service and price
requirement to the supplier.
5 A COMPUTATIONAL MODEL
AND AN EXAMPLE
5.1 Service Design Space &
Perceptual Space
As mobile and computing technologies evolve,
technical specifications of a mobile service become
much more complex. From a supplier’s perspective,
it is common to define tens or even hundreds service
attributes in a single service. We characterize a
space that is constructed by these technical
attributes as a service design space (or an explicit
space). Each dimension in this space corresponds to
a technical attribute of the service, including tariff.
When reaching an agreement with a supplier, the
user wants the details to be specified in text or a
specification form. Service level agreements (SLAs),
which use to be a way to ensure quality of service
(QoS), are becoming increasingly common to set
commercial and business terms of service
provisioning (Pau, 2005). SLAs generally take the
form of a structured template, with specific QoS
metrics that are evaluated over a specific time
interval or to a set of defined objectives. Thus SLAs
are often written in technical language.
However, an ordinary user usually does not
understand most of the technical details of the
service specifications. Even given a complete literal
translation and additional explanations of the
attributes, it is unlikely that the user has the patience
to go through all the details. More importantly, user
needs to balance among the value of each attribute
and the constraints so as to optimize his payoffs.
Such perfectly-rational user never exists. Instead,
user demands are often expressed in plain (natural)
language which involves little technical details. His
perception of the service is usually much simpler.
We define a perceptual space as a space constructed
by the perceived attributes of a service (e.g. ‘a fast
connection’). The perceived attributes are actually
the results of a reduced mapping or an “attribute
substitution plus simplification”. The reduced
mapping is based on certain heuristics or as a result
of learning of the technical attributes into features
that the user in general can relate to. To reach a
concrete SLA, a translation or a mapping between
the explicit space and the perceptual space is
necessary.
5.2 The User
Suppose users can be divided into groups which
share similar preferences for a specific class of
services. We employ a statistical method called
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34

principle component analysis (PCA) to find out the
mapping between an explicit space and a perceptual
space for a specific group interested in the same
class of services. We assume the mapping is valid
for a new user, who can be placed in a same group.
Denote the explicit space as x space. PCA
generates new vectors which are linear combinations
of the x coordinates . Denote the PCA space as z
space, and the principle component coefficient
matrix as p (each column containing coefficients for
one principal component), we have z=x p. The PCA
method has two advantages: a). The first PCA
components often explain more variance than the
rest of the components, which can be left out
without losing much information. b). The generated
PCA components are orthogonal to each other (Latin
, 2003) .
Interpretation of the PCA components is service
specific. In reality z space has much smaller
dimensionality than x space due to user’s perceptual
capabilities. For a given service, we analyze the first
components which cover +/-80 % of variance.
The next step is the elicitation of a utility
function. User’s revealed preferences may not
possess the properties which are the necessary
conditions to find a utility function. On the other
hand, by working only in a perceptual space, it is
easy for the user to set where he would like to be,
and that is called a target point (actually a vector of
values), which mixes economic and social aspects of
the service. In this model, we assume the user’s
utility function is the inverse of the Euclidean
distance from a user’s best reachable points (because
of constraints) to his target point. A user maximizes
his utility by approaching as close as possible to his
target point. This is also a simplified decision
process.
5.3 The Supplier
The supplier, as a profit-oriented company, is
assumed to make decisions based only on his
economic utility. We define this utility, in the
context of the negotiation of an individual tariff, as
the expected marginal profit that the supplier
receives from serving a specific individual user. The
utility function is defined in terms of attributes in the
explicit space including price and service
provisioning costs. The supplier maximizes his
utility, under certain constraints. In the case the
supplier is a community with social prefeences as
well, a different utility is chosen similar to Section
5.2.
5.4 The Negotiation Process
During service personalization, a user and a supplier
negotiate on a set of service attributes and their
values, including tariffs/price in view of a SLA. The
negotiation process has a non-cooperative and
recursive nature. It is modelled as an n-stage user-
lead Stackelberg game. The individual user is the
leader as he sets forth first his wishes in the context
of individual tariffs, and not the supplier as it in
supplier driven public tariffs. During each stage,
each player tries sequentially to optimize his own
utility taking into account what the other has
proposed under his own constraints. Players update
their constraints based on what others proposed as
variable tolerance bounds as a learning process.
Payoffs & constraints: the players’ payoffs are
expressed in their utility functions. User’s utility
function is expressed in a perceptual (z) space while
the supplier’s in a technical (x) space. Optimization
of the user utility is carried out in z space and
optimization of the supplier utility in x space.
Players set their constraints separately in x space.
The final SLA is expressed in x space in view of
provisioning by the supplier. Since the user’s utility
function, constraints, optimization and SLA are
expressed in two different spaces, transformations
from one space to another is carried out when
necessary.
Equilibrium: A one-stage Stackelberg game can
be solved to find a Nash equilibrium, which is a
profile of actions with the property that no player
can deviate to achieve a better payoff, given the
actions of the other player. In the recursive
Stackelberg game used in our model, we define an
equilibrium point as a point where no player can
elicit a higher utility by deviation or entering a new
stage of the game; furthermore, the point should also
provide the supplier a non-negative payoff. As in
any constrained computational game , if user of
supplier is unwilling to admit tolerances represented
by constraints, the corresponding Lagrange
multiplier values go up increasing risk to the other
party,or the negotiation concludes by early
withdrawal and to a switch / churn to another more
flexible supplier .
Negotiation process: It has several steps.
Step 0: In the beginning, the supplier advertises
the offering of a class of mobile services. The
service attributes (including price) and their values
are expressed in x space (denoted as x_ offer
0
). The
service attributes are translated into perceptual
attributes, thanks to a pre-existing survey amongst
potential users of the service, serving as a learning
function. The individual user sets his target values
for the perceptual attributes based on his
preferences. The values of the attributes of the
INDIVIDUAL TARIFFS FOR MOBILE SERVICE BUNDLES - A Negotiation Calculation Tool
35

public offer from the supplier are also mapped into
the user’s perceptual space: it serves as an initial
reference point for the user (denoted as z_offer
0
).
Step 1: User optimizes his utility in z space,
under his own constraints and taking into
consideration the supplier’s offer. The result of
user’s optimization at stage i is denoted as
z_user_result
i
; it is then transformed into x space as
x_user_result
i
.
Step 2: User decides whether to stop or not,
based on his own decision rules. In case of the
former, he may opt out to take the public offer or to
negotiate with another supplier. If the user decides to
continue the present negotiation, he communicates
with the operator about his request, which is
x_user_result
i
. The user may at the same time signal
to the supplier a possible tolerance region in x space.
Step 3: The supplier updates his constraints
regarding the proposed value x_user_result
i
and the
possible tolerance region signalled by the user. He
then calculates his own optimum under the updated
constraints. Denote the supplier’s choice in x space
as x_operator, which is a vector. The result is
denoted as x_operator_result
i
. The supplier then
decides whether to accept the proposal, or to propose
back his last optimized values. He may stop the
game based on his own decision rules.
Recursion and Stopping rules: the procedure
repeats from Steps (1)--(3) until it satisfies one of
the following conditions: z_user_result
(m+1)
=
z_user_result
m
or x_operator_result
(m+1)
=
x_operator_result
m
. Either player can stop the game
when the results show a non-convergence trend,
which either appears as an oscillation (e.g.
||z_user_result
(m+1)
- z_user_result
m
|| = d, d ≠ 0) or an
amplification (e.g. ||z_user_result
m
- z_user_result
(m-
1)
|| < ||z_user_result
(m+1)
- z_user_result
m
||).
Furthermore, the supplier will stop the game when
the result of his optimization leads him to negative
profit.
5.5 Implementation and Preliminary
Results
We have developed a tool to automate the numerical
calculation of utilities and the negotiation process of
tariff and service personalization. One off-line part
calculates the PCA mapping between the explicit
space and the perceptual space from a group-survey
of potential users with latent interest in the service.
The other on-line part decides if equilibrium exists
based on the utility functions, constraints and
decision rules set by both players, and computes the
equilibrium if it exists.
We have created a mobile service bundle with
limited service attributes to illustrate the
computational model and to test the tool. The service
is inspired by the real practices by the operators in
mobile music area (e.g. the “Radio DJ” service
promoted by Vodafone: www.vodafone.de/music or
see (Manes, 2005) for other cases) and it is called
“mobile singing classroom” where the users can
improve their singing performance by following the
courses and getting instructions and content. Users
are supposed to be students from a music college;
the supplier is an operator assisted by teachers.
Table I shows the revealed preferences from three
users (A, B, C) and the negotiation results. Gains
and losses (when compared to the public offer) are
analysed for each player; the results can be a win-
win or win-loss situation. Users, as leaders of the
games, achieve gains. The differences in gains
across users stem from their different preferences
and constraints. The operator achieves better results
in two cases but a worse result in one case (utilities
not reported here) . Detailed descriptions of the
software implementation and full results of the
mobile singing classroom case are available in
(Chen & Pau , 2006).
Table I: User revealed preferences, operator’s public offer
and negotiation equilibrium results.
Name Initial points Public
offer
Final EQ Point
A B C A B C
Database size
Thousand song)
6 1 3 2
5.6
1.9
2.6
Instructions per
lesson
2 8 4 4 2.1
6.2 3.2
Coding rate of
songs (kbps)
12
8
14
4
1
4
4
114 13
0
11
9
12
2
SMS searches
per lesson
7 1 3 2
6.2
1.9
3.0
Distribution
method (1-10
from fixed to
mobile)
3 9 7 5 5.8 7.3
5.6
Nb of question
student asks
(full contract
period)
2 60 3
0
10 1 58.
3 1.3
Contract length
(month)
2 4 3 2
1.6
5.2
2.5
Nb of lesson per
month
20 8 1
0
5 19 6.1
8.4
User's bid for
the service (full
contract period
€)
10
0
10
0
7
0
30 63. 98 53.
6
ICE-B 2006 - INTERNATIONAL CONFERENCE ON E-BUSINESS
36

6 CONCLUSIONS
This paper tries to carve out a small piece of land out
of the uncharted area of individual tariffs for bundles
in mobile communication services ,paving the way
to tariffing of boadband bundles as well (such as IP
TV , alarm sevices , financial applications ) . Based
on user and supplier behaviours, our bargaining
model aims to provide guidance to build
computational models for implementation, where the
determination of individual tariffs can be automated
or semi automated so that the provisioning overhead
is not prohibitive. The preliminary results from the
computational model show that individual tariffs
can be beneficial in some cases to both the users and
the supplier. Our next steps of work involve
comparing different types of equilibria when the
user and the supplier use different strategies and
decision rules. Risk will also be incorporated in the
model by linking individual’s utility with random
distributed parameters so that the supplier can get a
quantified portofolio income with known risks .
It should be noted that the use of the perceptual
space has the added advantage to the supplier of
reducing the surveying costs amongst users and
allowing him still to offer catalog bundle tariffs
close to user groups expectations . More precisely ,
in marketing terms, the two main deployment
options are :
-supplier surveys a rather large population of
potential customers witgh personalized needs
(roughly 1000 users is enough for PCA stability)
,and determines by the tool set bundles with
different service charactristics fo users just to choose
from
-or a sophisticated user engages in a real time
negotiation with the supplier, using the tool,in which
case provisioning costs are reduced because they are
automated ,including opt-out decision
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