Simulating Digital Businesses Using an
Agent Based Modeling Approach
Aneesh Zutshi
1
, Antonio Grilo
1
and Ricardo Jardim-Gonçalves
2
1
UNIDEMI, Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
2
UNINOVA, Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
Keywords: Digital Business Models, Agent Based Modeling, Business Forecasting, Business Simulation.
Abstract: This paper proposes a complex systems approach to understand the growth and decline of Digital
Businesses. Digital Businesses are characterised by unique factors such as a highly networked online
customer base, where word of mouth spreads very fast, and minimal cost of incremental users due to
economies of scale. Thus online businesses peak and plummet rapidly making it difficult to fathom their
success. Through an Agent Based Modeling approach we propose that online businesses can be represented
as simulation models which can enable forecasting, what-if analysis and optimization of business
parameters.
1 INTRODUCTION
Digital businesses encompass the entire gamut of
ventures, from online sale of products and services
to social collaboration platforms, and have become a
vital engine for the new economy. Understanding
this new economy has been a challenge for
companies that were tuned to the traditional ways of
doing business and were armed with traditional
product development and marketing philosophies.
Most companies that rushed into the dot-com bubble
in the early part of the last decade perished
(Goodnight & Green 2010). However, we also saw
the emergence of new business giants such as
Google and Amazon, who did the right things at the
right time to emerge as winners.
Today Digital Business managers often rely on
experience and intuition to set up business models
and pricing strategies. Though marketing surveys,
have been traditionally used in gauging consumer
willingness to pay, a big challenge is to predict
business growth, customer adoption and customer
response to specific business and pricing models.
Online Businesses are complex interacting systems
where online users interact and share opinions and
experiences at a rate far greater than traditional brick
and mortar businesses. These user opinions are
shared through offline and online word of mouth
(WOM) channels, in addition to various marketing
channels. Customer Satisfaction is a key to spread of
positive or negative WOM, which influences new
customer adoption rate. System Dynamics have
often been used to model consumer adoption
(Stermann 2000). However System Dynamics
require the rules of the behavior to be written at a
higher level, such as how the whole population of
consumers will respond to a marketing activity
rather than how a particular individual will respond
(Rand & Rust 2011).
Online businesses are examples of a complex
system, where the behavior of individual users can
be used to model the growth or decline of a business
proposition (Zutshi et al. 2014a). This could provide
an analytical approach to develop models that can be
used by businesses as a decision support system.
Such models can perform a range of objectives, such
as making business forecasts, calculating the
implications of a change in product pricing,
optimization of different price plans, and simulating
the impact of a change in the Business Model.
Agent Based Modeling is the most appropriate
tool to implement this complex systems approach.
Agent Based Modeling is a new computational
method through which macro-level consequences
are explained through simplified representation of
micro level interactions between agents that
represent real life entities (Zutshi et al. 2013). These
autonomous agents represent online users with
165
Zutshi A., Grilo A. and Jardim-Gonçalves R..
Simulating Digital Businesses using an Agent Based Modeling Approach.
DOI: 10.5220/0005121301650171
In Proceedings of the 11th International Conference on e-Business (ICE-B-2014), pages 165-171
ISBN: 978-989-758-043-7
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

individual characteristics as well as independent
internal decision making capabilities.
In this paper, we propose a Dynamic Agent
Based Modeling Framework (DYNAMOD), that
incorporates Agent Based Modeling (ABM)
techniques to develop and test digital business
models that can be potentially applied to a variety of
online market scenarios. We also make a discussion
on how to manage probabilities from the simulation
results using probability management techniques.
New tools and techniques are necessary to help
model the complex nature of online products and
services (Zutshi et al. 2012; Grilo et al. 2012).
Hence we need to develop a customizable simulation
environment that can capture the dynamics of an
online market, and provide Business Managers with
tools to simulate and forecast, thus aiding to perfect
their Business Model. Online markets can be
represented as a network of interconnected online
users which share positive and negative feedbacks
and respond to different online products and
services. If the behavior of individual agents can be
sufficiently well modeled, then a large range of
application possibilities open up such as forecasting,
what-if analysis and business model simulation.
2 ABM IN BUSINESS
ABM is build on proven, very successful techniques
such as discrete event simulation and object oriented
programming (North & Macal 2007). Discrete-event
simulation provides a mechanism for coordinating
the interactions of individual components or
“agents” within a simulation. Object-oriented
programming provides well-tested frameworks for
organizing agents based on their behaviours.
Simulation enables converting detailed process
experience into knowledge about complete systems.
ABM enables agents who represent actors, or
objects, or processes in a system to behave based on
the rules of interaction with the modelled system as
defined based on detailed process experience.
Advances in computer technology and modelling
techniques make simulation of millions of such
agents possible, which can be analysed to make
analytical conclusions.
The literature review reveals that applications of
ABM have been made to model specific areas of
Business (Zutshi et al. 2014b). These include
prediction of financial distress (Cao & Chen 2012),
product adoption [(S. Kim et al. 2011), (Diao et al.
2011)], consumer behaviour [(Vanhaverbeke &
Macharis 2011)], market share (Kuhn et al. 2010),
Urban Management (Gao et al. 2012) and demand
forecasting (Ikeda et al. 2004). (Kuhn et al. 2010)
demonstrated the possibilities of predicting market
share based on certain BM attributes of Frontier
Airlines. (Bellman et al. 2013) addresses the issue of
capturing Internet behaviour to deliver relevant
advertisements. ABM approaches can also be used
for modeling user response to different sources of
advertising. It can also be used to model response to
identify the most critical target groups,
complementing traditional approaches for the same.
(Roozmand et al. 2011) propose an Agent Based
Model to simulate consumer decision making based
on culture, personality and human needs and relates
them to car purchase decisions. Tesfatsion
introduced Agent-Based Computational Economics
(ACE) as the computational study of dynamic
economic systems modeled as virtual worlds of
interacting agents. (Somani & Tesfatsion 2008)
have applied ACE to retail and wholesale energy
tradings in the Power Markets. In this paper we
extend the concept of Agent-Based Computational
Economics, to develop DYNAMOD- An Agent
Based Modeling Framework for online Digital
Business Models.
3 OTHER RELEVANT AREAS OF
RESEARCH
3.1 Diffusion of Innovations
Diffusion of Innovations has been an active research
area and reflects adoption decisions made by
individual consumers. These decisions are made in a
complex, adaptive system and result from the
interactions among an individual's personal
characteristics, perceived characteristics of the
innovation, and social influence (Schramm et al.
2010). There are two major approaches to modeling
diffusion: econometric and explanatory. The concept
of Econometric Modeling was first introduced by
(Bass 1969). Econometric approaches forecast
market uptake by modeling the timing of first-
purchases of the innovation by consumers and are
more applicable when market growth rate and
market size are of primary interest. Explanatory
approaches, as first proposed by (Gatignon 1985)
establish that the diffusion of a product in a defined
market is equivalent to the aggregation of individual
consumer adoption decisions. The adoption
decisions are dependent on: Personal characteristics,
ICE-B2014-InternationalConferenceone-Business
166

perceived product characteristics, and social
influence.
In earlier works, diffusion of innovation has been
approached with mathematical modeling
(Goldenberg 2001; Goldenberg et al. 2010;
Goldenberg et al. 2007). However as computational
powers increased, relatively recent attempts have
been made to complement these classic approaches
with Agent Based Modeling tools. (Delre et al.
2007; Stonedahl et al. 2008; Diao et al. 2011). We
have used these works as the basis for developing
the DYNAMOD model with the application of
specific characteristics that relate to online
businesses.
3.2 Word of Mouth
Literature has assumed word of mouth (WOM) to be
the influence of neighbors over an individual (Feng
& Papatla 2011). This is a relevant assumption for
offline word of mouth since such communication is
mostly limited by geographical location. (Keller
2006) estimates that 90% of WOM conversations for
traditional goods and services takes place offline.
Word of mouth communication is more effective
when the transmitter and recipient of information
share a relationship based on homophily (tendency
to associate with similar persons), trust and
credibility.
(Feng & Papatla 2011) state that the incentive to
spread word of mouth reduces if a product gets well
known. They go further to deduce that elevated
awareness is created by a high advertising budget,
reduces the word of mouth propagation. They also
deduce that highly satisfied or highly dissatisfied
customers are likely to engage in more word of
mouth than other customers. The DYNAMOD
model incorporates this by ensuring that agents with
a high or low satisfaction score have a greater
influence on their neighbours.
(Goldenberg et al. 2007) state that for a
traditional product, negative word of mouth spreads
up to 2 levels of agent chains but a positive word of
mouth can go on spreading much further. They also
predict that the net effect of advertising at early
stages when the product is still not stable could
enable a larger creation of negative influence, which
could possibly reduce the subsequent market uptake
of a product. Thus at an early stage digital products
can be negatively impacted by a high degree of
advertisement and subsequent adoption, especially
when the product is still at a rudimentary state.
While the extent may vary, there is general
agreement in the literature that a dissatisfied
customer influences others more than a satisfied one
(Herr & Kardes 1991). This consensus is built both
on evidence that dissatisfied customers communicate
with others more than satisfied ones and that
recipients of this communication place more weight
on negative information.
Thus the DYNAMOD model incorporates these
findings by incorporating them into the way Word of
Mouth propagates amongst the user Agents.
3.3 Network Structure
Cellular Automata, is a form of lattice network and
has been used by numerous authors to
mathematically model word of mouth. They
represent users as cells in a cellular grid like
network, with each a cell getting influenced by static
neighbouring cells surrounding them. (Goldenberg
2001) used cellular automata and introduce the
concept of strong ties and weak ties while discussing
word of mouth. However the static nature of the
network makes it unsuitable to represent the
dynamic nature of online user networks. Another
form of a network is a random network where the
cell distance is randomly distributed. Another
common network methodology is the small-world
network which starts with a random network
randomly rewiring some of the edges (Stonedahl &
Rand 2010).
In the case of DYNAMOD, we shall be using a
dynamic random network where user agents start
being randomly distributed over a flat world, and get
influenced by agents in a fixed radius. However the
agents themselves slowly make random walks, and
thus the agents within their sphere of influence keep
changing. This represents an online world where
users constantly meet the opinion of new users
through online posts and forums.
4 THE DYNAMOD
FRAMEWORK
4.1 DYNAMOD Components
The DYNAMOD Framework has been developed
based on the academic literature collected regarding
the unique aspects of an online business. Its purpose
is to provide researchers and companies engaged in
online businesses with a tool for quickly developing
Computational Modeling Systems that can represent
their Business Models and their Business
Environment, in order to perform advanced
simulations for predicting business growth
SimulatingDigitalBusinessesusinganAgentBasedModelingApproach
167

dynami
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and sim
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ake a varie
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onceptual re
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e
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or implemen
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n
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o
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alysis
NetworkEf
f
/Viral
Marketi
n
Effects
on Agent B
a
representati
o
n
e user that c
o
d
uct or servi
c
M
OD (See Fi
g
h
other and s
n
d services. A
t
y
Advertising
these influe
n
r
to predict f
u
a
nd extendibl
e
Business M
t
y of simulat
i
l
ationship o
f
M
OD Frame
w
i
nteracting a
g
includes stan
d
t
ing influence
g
ent. This
n
and interac
t
e
eded to initi
a
o
f The DYNA
M
w
ork Comp
o
f
ects
g
Market
Segmentation/
Regionbased
modeling
ased
o
n of
ould
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e is
gure
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hare
t
the
and
n
ces,
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ture
e to
M
odel
i
ons.
f
the
w
ork.
g
ents
dard
and
core
t
ion,
a
lize
M
OD
o
nent
Ot
h
mo
d
sce
n
add
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Co
m
Mo
d
co
m
co
n
b
e
h
int
r
im
p
int
r
mo
d
co
m
ne
e
tha
t
on
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t
M
a
M
o
mo
d
ag
e
cus
rep
r
ma
j
Ra
n
thr
o
imi
use
nei
g
4.
2
Th
e
D
Y
an
A
b
e
a
tha
t
inf
l
Th
e
sca
l
p
ro
de
fi
as
k
sa
m
wh
i
ad
o
wil
l
h
er features are
a
d
ules, as and w
h
n
arios. In the c
u
i
tional module
s
m
petitor Analy
s
d
eling.
Competitor
m
petitors wh
o
n
sumers, an
d
h
avior of con
r
oduction of
p
acts on cons
u
r
oduction of
F
d
el, and sim
u
m
ponents of
t
e
ded in case o
t
have an in
h
Viral Mark
e
t
change th
e
a
rket Based
o
delin
g
chan
g
d
el space,
t
e
nts. This c
a
tomers with
r
esent custo
m
Diffusion of
j
or forms o
f
n
d 2010). In t
h
o
ugh indivi
d
t
ation(Bass 1
r
adopts onl
y
g
hbors have
a
2
The Ad
o
e
adoption fu
n
Y
NAMOD Fr
a
A
gent will b
e
a
client. We
s
t
shall acco
u
l
uences as det
a
e
Influence s
c
l
e of 0 to 1.
duct,
A
i
Influence
0
.
This adopti
o
fi
ned for th
e
ing users for
m
ple survey,
i
le developin
g
Also, if the
o
ption, the pr
i
l
ingness to p
a
a
dded to the m
o
h
en necessary,
u
rrent scope of
t
s
have been en
v
s
is, Effects, an
d
Anal
y
sis in
v
o
can have co
d
then moni
s
umers. Pric
i
various cha
r
u
mer adoptio
F
reemium Bu
s
u
lates the ado
p
t
he Business
e
f Free Busin
e
erent Netwo
r
e
tin
g
need to
e
rate of p
r
Se
g
mentati
o
g
es the dispe
r
o represent
a
n represent
v
arying purc
h
m
ers on differe
n
Innovation l
i
adoption fu
n
h
e Bass like
m
d
ual innovati
o
969). In the
t
y
when a c
e
a
dopted(Gatig
n
o
ption Fu
n
n
ction is a cri
t
a
mework, sin
c
e
come a clien
t
s
hall use a h
y
u
nt for all t
h
a
iled below:
c
ore of i
th
A
g
For an agen
t
25 (Adoptio
n
o
n threshold
e
DYNAMO
D
satisfaction/i
n
this value h
a
g
the scoring
s
online servic
i
ce offered
m
a
y.
o
del in the for
m
for different c
a
the model, fou
r
v
isaged, namel
y
d
Market Base
d
v
olves introd
u
o
mpeting infl
u
i
toring the
s
i
n
g
Anal
y
sis
r
ging units,
a
o
n. It also in
v
siness Model
p
tion of Free
e
s. This mod
u
e
ss Models.
B
r
k Effect or
a
o
add additio
n
r
oduct adop
t
o
n or Re
g
i
o
r
sion of age
n
different cl
u
different c
l
h
asing powe
r
e
nt continents
.
i
terature has
u
nctions (Sto
n
m
odel, adopti
o
i
on or thro
u
threshold m
o
e
rtain thresh
o
n
on 1985).
n
ction
i
tical compon
e
ce it determi
n
t
and when it
y
brid adoptio
n
h
e various s
o
gent A
i
shal
l
t
to adopt a
p
n
Threshold).
has been s
p
D
model, a
n
i
nfluence sco
r
a
s been kept
s
cale.
c
e is not free
m
ust be lowe
r
m
of
a
se
r
y
d
u
ction of
u
ences on
s
witching
involves
a
nd their
v
olves the
s
into the
and Paid
u
le is not
B
usinesses
a
re based
n
al logics
ion. The
n Based
n
ts in the
u
sters of
l
asses of
r
s, or can
used two
n
edahl &
o
n occurs
u
gh peer
o
del, each
o
ld of its
e
nt of the
n
es when
ceases to
n
function
o
urces of
l
be on a
p
articular
p
ecifically
n
d while
r
es in the
in mind
then for
r
than his
ICE-B2014-InternationalConferenceone-Business
168

The adoption influence is updated on each
iteration by averaging it with a new computed value
of the A
i
Influence
(Eq.1)
The
New
A
i
Influence
is computed through a combination
of the following components: Offline word of
mouth, Brand influence and Ad Influence, Network
Effect and Viral Marketing:
New
A
i
In
fl
uence =

i
OfflineWOM-Friends-Influence
. 
∑


j
Influence
)
+ 
i
Brand-Influence
. 
∑

j
Influence
)
+ 
i
Ad-Influence
. Cost
Ad
+N-CONST . A
i
Network-Effect
+C
Viral Marketin
g
(Eq.2)
The degree of Influence from Neighbours,
Global or Advertisements is determined by the 3
coefficients 
i
OfflineWOM-Friends-Influence
,

i
Brand-Influence
,
and 
i
Ad-Influence
. Each Agent responds differently to
these different sources of influence. Through a
sample survey we gather the value of these 3
coefficients for every respondent and then compute
the mean and standard deviation values. These
values are then used to assign these coefficients to
these agents.
Network Effect Coefficient: In order to model the
network effects, we have introduced a network
effect coefficient (N-COF) in our model (See Figure
3). When the percentage of users who are clients
within the neighbourhood of an agent is below the
critical membership lower limit, the value of the
network coefficient is -0.5. This causes a slowdown
in the adoption requirement, owing to a chilling
effect of network externalities (Goldenberg et al.
2010). Within the critical membership range, the
coefficient moves from -0.5 to +0.5, and we begin to
see a higher growth rate. Network effects tend to
cause a hockey stick growth pattern to be more
skewed. In the case of double sided network effect.
C
Viral Marketing
is added to increase the influence
rate for such products that have a high viral
marketing. The Viral Marketing coefficient is
triggered if a large percentage of respondents state to
having become clients based on automated mails
from acquaintances.
Once an Agent becomes a Client, then onwards,
the key parameter will be the Satisfaction and not
Influence. The satisfaction level will not be
influenced by neighbours or advertisements but
rather be a function of the product’s utility. In this
model we have used the satisfaction scores that have
been collected from sample surveys. Hence:
If A
i
= Client, A
i
Influence
= A
i
Satisfaction
(1.)
And A
i
continues to remain a client unless
A
i
Satisfaction
< 0.25. In this current model, once the
user stops being a client, he doesn’t become a client
a second time. However this is specific to the
context of the business being modeled.
5 STEPS FOR DEVELOPMENT
OF AN ABM MODEL
The following steps outline a generic approach to
developing an Agent Based Model for an online
business.
1. Identification of the Scope and Objectives of
the Model like forecasting future market
penetration, forecasting revenue based on
changes in pricing, optimization of pricing
bundles, predicting customer adoption based
on optimizing marketing mix, etc.
2. Identification of Business Model Components
& associated variables that define the
business case.
eg. Pricing, Product offering, Product
characteristics - like Network Effects, Viral
Marketing.
3. Identification of Agents and Agent
Parameters.
eg. Buyers, Sellers, Facilitators
4. Model initialization through data obtained
sample surveys.
eg. Market size, Average willingness to pay,
product satisfaction, WOM influence.
5. Develop the model and validate it using the
DYNAMOD Framework.
6. Obtain historical records of the growth data.
Divide the data into initialization and
validation phase.
7. Initialise the model coefficients. Run the
simulation model to ensure the simulation
output is as close to the validation phase
historical data as possible by adjusting the
model coefficients.
8. Compare the simulated data with the
Validation data. Compare the accuracy of
this forecast with other methods like ARIMA.
If the forecast is acceptable them the model is
set to be complete and ready for use.
SimulatingDigitalBusinessesusinganAgentBasedModelingApproach
169

9. Use the Model as required, by changed the
relevant business model parameters and
measuring the impact.
6 CONCLUSION
This paper discusses a novel approach of studying a
digital business as a complex system, and using an
Agent Based Modeling approach to develop specific
purpose simulators that can be used for a wide
variety of what-if scenarios and as a tool for
optimization of Business Parameters. This approach
has already been implemented by our research group
for some online case studies such including
facebook.com, custojusto.pt and vortal.biz.
ACKNOWLEDGEMENTS
We acknowledge custojusto.pt and vortal.biz for
providing us case studies to validate the model.
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