Agent Based Modelling for Customer behaviour

1. 1
Dr. Ir. Yudi Limbar Yasik., MSc
mail : yudi.yasik@inti.co.id
hp : 0812-218-20090
Introduction of
Agent Based Modeling (ABM) for
Customer Behavior Model

2. Concepts
ü Ilmu Ekonomi ?
ü Marketing ?
ü Consumer Behavior ?
ü Modeling?
ü Agent Based Modeling?
2

3. Ilmu Ekonomi?
▪ Ilmu yang mempelajari tentang
kebutuhan manusia
▪ Barter
▪ Konsep uang
▪ Konsep Supply : Demand
3

4. Marketing Concept?
▪ Kapan dibutuhkan
▪ Supply < Demand
▪ Supply = Demand
▪ Supply > Demand
▪ Ilmu yang pempelajari tentang perilaku
manusia/konsumen dalam memenuhi kebutuhannya
▪ Dasar ilmu marketing adalah ilmu tentang perilaku
konsumen (Consumer Behaviour)
4

5. Marketing Strategy
5
Consumer Research
And Analysis
Marketing Strategy
Development
Marketing Strategy
Implementation
Consumers:
Affect and Cognition
Behaviour
Envoironment
text
MARKETING
STRATEGY
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CONSUMER
ENVIRONMENT
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6. Consumer Behaviour
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Usaha Pemasaran
- Produk
- Promosi
- Harga
- Saluran distribusi
Lingkungan Sosio-Budaya
- Keluarga
- Sumber informasi
- Reff Group
- kelas sosial
- sub budaya & budaya
Psikology
- Motivasi
- persepsi
- Pengetahuan
- Kepribadian
- Sikap
Pengenalan kebutuhan
Penyelidikan sebelum
pembelian
Evaluasi Alternatif
Pengalaman/
Pembelajaran
Pembelian
- Percobaan Pembelian
- Pembelian ulang
Evaluasi Setelah
Pembelian
MASUKAN / PENGARUH EXTERNAL
PROSES PENGAMBILAN KEPUTUSAN (INTERNAL)
ACTION
Sumber: Schiffman & Kanuk. 2000: 8

7. What is a Model?
An abstracted description of a process, object, or event
Exaggerates certain aspects at the expense of others
“Essentially, all models are wrong, but some are useful”
(George Box, 1987)

8. Modeling
▪ Model Matematis
▪ Misal :
▪ S = V x t
▪ V= S / t
▪ t = S / V
▪ Model Statistik
▪ Misal :
▪ Linear regression
▪ Y = α + βX + ε
▪ Model Visual
8

10. What is an Agent-Based Model?
An agent is anautonomous
individual element with
properties and actions in a
computer simulation
Agent-Based Modeling (ABM) is
the idea that the world can be
modeled using agents, an
environment, and adescription
of agent-agent and agent-
environment interactions

11. Why ABM??
▪ Model yang tidak bisa di dekati dengan persamaan matematis
atau statistik
▪ Sistem yang kompleks tidak linear seperti perilaku manusia
▪ Ethical problem, Non Parametrik
▪ Bottom up aproach
▪ Simulasi sistem
▪ Skenario, prediksi
▪ Kemajuan di bidang ilmu simulasi dan komputer,
▪ artificial Intelegent
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12. Contoh Complex model : Policy Analysis
Part 2: ABM
Part 1: GIS
landscape
Transit
network
social-
economic
data
Fuel Price
Create initial
environment
Initialize
households
Households relocate
Households choose
travel modes
Part 3: TDM
Households
make trips on
highway network
Households decide to
own a car or not
Income
Car
Ownership
CarUse
LowDensity
LandUse
Auto-dominant
Transportation
System
Highway/Transit System
Public Policies Investment
Ownership
Tax
Fuel tax
Zoning
Affordable car
Auto financing
Employmentsprawl
Residential sprawl
Private
sectors
Other
socioeconomic
factors
Household formation
Female workforce
Transit
agency
1995 TAZ
Rail
network
Six
counties
Environment in
ABM
Year
Transit
Share
L2
L3
Point of
noreturn
T1
T2
T3
L1
Points of
government
intervention
Yandan Lu, 2009

13. ABM Teory
▪ Secara konsep ABM diturunkan dari gabungan antar disiplin ilmu yang
dikenal dengan konsep “Science complexity” istilah yang diangkap
oleh Levin 1999[i].
▪ Secara alamiah konsep biologi dan ilmu sosial digabungkan sehingga
menghasilkan gabungan yang kompleks yang dapat mengantisipasi
sistem yang tidak linear, bisa mengatur diri sendiri, heterogen, bisa
beradaptasi, ada feedback, dan dapat memunculkan perilaku.
▪ Ke semua gabungan ilmu tadi di implementasikan ke dalam suatu
teknik computer dan software yang membuat kerangka kerja
permodelan berbasis agen, yang merupakan hasil perkembangan teori
komputer mulai dari artificial intelegent, neural network, dan
pemrograman computer yang dapat berevolusi.
▪ [i] Lewin, R. (1999), Complexity: Life at the Edge of Chaos, University of Chicago Press, Chicago, IL.
13

14. Recommended Book
• An Introduction to
Agent-Based Modeling
• Uri Wilensky and
William Rand
• Available at MIT
Press and Amazon
https://mitpress.mit.edu/books/introduction-agent-based-modeling
http://www.intro-to-abm.com/

15. DEFINISI
▪ ABM adalah :
▪ suatu metode yang digunakan untuk penelitian / eksperimen
▪ dengan melihat pendekatan dari bawah ke atas (bottom-up)
▪ bagaimana interaksi perilaku-perilaku individu dapat mempengaruhi
perilaku sistem,
▪ dengan simulasi berbasis komputer (Virtual World)
▪ untuk memodelkan semua perilaku entitas (agen) yang terlibat dalam
dunia nyata
▪ dengan harapan interaksi antar entitas dapat menghasilkan atau
menggambarkan sifat utama
▪ yang dapat digunakan lagi sebagai alat bantu untuk eksplanatori,
eksploratori atau prediksi dalam mengambil keputusan di dunia nyata.
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16. The key feature of agent-based modeling
Twomey & Cadman, 2002, Agent-based modelling of customer behaviour in the telecoms and media markets
▪ The term ``agent’’ in the context of business or economic modeling refers to real world
objects such as people or firms.
▪ In the agent-based approach the focus turns to the properties of the individual
agents.
▪ These agents are capable of displaying autonomous behavior such as reacting to
external events as well as initiating activities. Of equal importance is the interaction of
these agents with other agents.
▪ Involves a bottom-up approach to understanding a system’s behavior (e.g fish or bird
group).
▪ Traditional modeling usually takes a top-down approach in which certain key aggregated
variables are observed in the real world and then reconstructed in a model.
▪ Under this approach a modeler would observe the effects of say a price change on the
number of consumers who purchased a product at an aggregated level. This would provide
the basis for quantifying the strength of interaction in the model.
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17. Agent Based Modeling Experiment
Testfatsion, 2005, ACE Modeling Economies as Complex Adaptive Systems
▪ Modeler constructs a
virtual world populated by
various agent types
(company, consumer,
market, supplier, regulator)
▪ Modeler sets initial world
conditions (consumer,
market place)
▪ Modeler then steps back to
observe how the world
develops over time (no
further intervention by the
modeler is permitted)
▪ World events are driven by
agent interactions
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constructs a virtual world
sets initial world conditions
The world develops over time
Culture Disk
(agent interaction)
Emergent Behavior
(macro behavior)

18. Perbandingan ABM dengan Model Kuantitatif
18
Pemodelan Ekonomi
Secara Kuantitatif
Pemodelan dengan Agent Based (ABM)
Model dibangun untuk
menyederhanakan
permasalahan
Model dibangun untuk mengungkapkan
permasalahan dengan pendekatan dari bawah ke
atas (bottom up approach), Twomey dan Cadman
(2002:56)
Model dihasilkan dari
pengolahan data empirik
(seperti data hasil survey)
Model adalah langkah awal untuk menghasilkan
data empirik, simulasi yang dijalankan dengan
model akan menghasilkan data empirik ,
Axelroad dan Tesfatsion (2005:4)
Model yang dibuat untuk
memecahkan masalah
yang dihadapi
Bukan model yang menyelesaikan masalah tetapi
agen-agen dalam model yang akan memecahkan
masalah yang dihadapi, Bonabeau (2002:7280)
Model yang dibuat adalah
hasil akhir dari penelitian
Model yang dibuat adalah langkah awal dari
penelitian, Bryson ett. all (2005:1)

19. Strengths of agent-based modeling
▪ System assumptions, The emergent non-equilibrium, dynamical behaviour of a system is usually one of
the most interesting outputs of agentbased models.
▪ Realism. This allows us to undertake qualitative scenario exploration to investigate the structure or
morphology of the system independently of the details.
▪ Natural representations. relatively easy to understand as they have a simple, structural correspondence
between the ``target system’’ and the model representation. They are more intuitive and easier to
understand than, say, a system of differential equations.
▪ Heterogeneity. ABMs also allow us to introduce a very high degree of heterogeneity (diversity) into our
populations of agents. Traditional models ± to permit mathematical solutions
▪ Bounded rationality . Both limited information and limited abilities to process information may be explicitly
incorporated into the model. Habit and social imitation may also be included.
▪ Communication and social networking. ability explicitly to incorporate communication among agents.
Agents can, for example, ``talk’’, share information or imitate other agents in the population. This level of
subtlety is usually outside the reach of traditional mathematical models, since social networks quickly make
equation-based models so complex as to be insoluble.
▪ Object-orientated analysis, design and programming.
▪ Maintenance and refinement. It is reasonably easy to add new types of agents or new attributes or
behaviours of agents without destroying earlier knowledge incorporated into the model
▪ + Ethical, parametric design.
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20. Weaknesses of agent-based modelling
▪ Data problems. the potential lack of adequate data. This is not surprising since, as mentioned
in the introduction, most quantitative research until now has concentrated on ``variable and
correlation’’ models that do not cohere well with process-based simulation that is inherent in
ABMs. This means that not only is it likely that new types of data are needed to be collected but
even theories may need to be recast effectively to take account of the potentialities of agent-
based simulation.
▪ Identifying rules of behaviors. Trying to capture the appropriate processes or mechanisms
underlying the agents’ behavior may not be an easy task. However, as Hood (1998) points out,
the flip side of this is that it forces us to be explicit about our assumptions and forces us to think
about extracting the ``essence’’ of the problem.
▪ Programming skills. Any sophisticated, agent-based model requires programming in an
object-orientated language such as Java. That is, it requires a level of computing skill beyond
simple spreadsheet programming.
▪ Computational time. ABMs are computationally intensive, and although it is precisely because
of the advances in computing power that we now have the possibility of desk-top agent-based
modelling, there are still limits to the level of detail and number of agents that can be run in a
simulation in a reasonable amount of time.
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21. Designing an agent
Hood, L. (1998), ``Agent based modelling’’, available at www.brs.gov.au/social_sciences/kyoto/hood2.html
▪ Low fidelity.
▪ all the agents in the model have the same behaviour and intrinsic attributes.
▪ This situation would not even be categorised as an ABM by many practitioners.
▪ It is of interest for problems where the statistics of the collection of entities are of interest.
▪ This situation occurs in many physics and chemistry simulations (e.g. the molecular level
simulation ofmaterial properties or drug design).
▪ because of their simpler agent details, usually much larger numbers of agents are employed in
the simulations than in a typical ABM. For example, one of the largest astrophysics simulations
ever performedconsisted of 150 million agents (stellar entities).
▪ Medium fidelity.
▪ Here an observed distribution of the agents’ behaviour is used to ``calibrate’’ the model.
▪ This is a very useful middle ground to target for many applications where the tails of a
distribution are of interest (e.g. the poorest 10 per cent, the richest 10 percent).
▪ An advantage of working at this level of detail is that it allows ups to capture some of the
observed properties of the individual agents without having to resolve the internal workings of
the agents (i.e. ``what makes them tick’’).
▪ High fidelity.
▪ a proper attempt is made to capture the internal workings of the agents. This may include trying
to model, among other things, the beliefs, desires and intentions of the agent.
▪ At this level of fidelity we may also include an ability of the agent to adapt and learn, such that
the agent’s behaviours and properties evolve over time as they learn about their environment
and what actions lead to success or failure.
▪ At this level of fidelity we are thus capturing some notion of a mentalistic or cognitive agent.
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22. Platform ABM
▪ Swarn (berbasis bahasa C)
▪ Bahan tentang Repast dapat didapat secara on line dari http://www.swarm.org/wiki/Main_Page
▪ Repast (Recursive Porus Agent Simulation Toolkit: berbasis Java)
▪ Bahan tentang Repast dapat didapat secara on line dari
http://repast.sourceforge.net/repast_3/index.html
▪ Mason (Multi-Agent Simulator of Neighborhoods: untuk
kecepatan)
▪ Bahan tentang Repast dapat didapat secara on line dari
http://cs.gmu.edu/~eclab/projects/mason/
▪ Netlogo (paling lengkap dokumentasi dan lebih praktis digunakan)
▪ Bahan tentang Repast dapat didapat secara on line dari http://ccl.northwestern.edu/netlogo/
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24. Proses Pembuatan ABM
24
(1)
Studi Pustaka
Observasi
wawancara
(2)
Desain
Model
Berbasis
ABM
(4)
Uji Validitas &
Reliabilitas
Model
Valid ABM
Model
(5)
Eksperiment
dengan
ABM
Data Hasil
Eksperiment
Model
Berbasis
ABM
(6)
Uji Statistik
& Observasi
Mdoel
Emergent
Behavior
Model
Kuantitatif
Spesifikasi:
Virtual World
Agents
Properti
Method
Output Proses
Keterangan :
Dokumen
Desain
sistem ABM
(3)
Pembuatan
Model
Berbasis ABM

25. ABM Area of implementation (in Management)
25
Learning and the
embodied mind
There exists a broad range of algorithms which represent the learning process of computational agents, e.g.
genetic algorithms.
Evolution of behavioural
norms
Norms are generated by interaction and in social settings. AXELROD (1997, 47) uses the following definition “A
norm exists in a given social setting to the extend that individuals usually act in a certain way and are often
punished when seen not to be acting in this way”.
Bottom-up modeling of
market processes
The major point in markets is the ability to perform self organisation. Some markets follow a path dependency
while others behave differently. Nearly every market can be investigated by using agent-based simulations.
Formation of economic
networks
Economic networks play a crucial role in social and economic science. The formation of transaction networks by
strategically interacting agents takes the centre stage.
Modeling of
organisations
An organisation consists of a number of people which have an objective or performance criterion that transcends
the objectives of the individuals within the group (V. ZANDT, 1998). In this sense organisations can be modelled
by implementing agent-based models.
Automated markets This area is related to the Internet and to virtual markets. There is a number of profit oriented research on the way
with continuously growing implementations in products.
Parallel experiments with
real and computational
agents
There are two main differences regarding experiments with real and computational agents: The behaviour of
computational agents is determined and known in advance while it is not possible to know explicitly why real
agents respectively human beings make a particular choice. Performing both experiments in parallel could support
the finding of insights.
Building ACE
computational
laboratories
Work with agent-based models needs computer and programming skills. There are environments developed and
still under construction which support application for non-skilled researchers. These computational laboratories
permit the study of systems of multiple interacting agents by means of controlled and replicable experiments, e.g.
Swarm or RePast.

26. CONTOH ABM
CONSUMER BEHAVIOUR MODEL
For VOICE MUSIC SMS (VMS) VALUE ADDED SERVICE AT GSM OPERATOR IN INDONESIA
http://ccl.northwestern.edu/netlogo/models/community/customerBehavior
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27. Model Perilaku Konsumen VMS
yudi limbar yasik, 2008
27
Pelanggan Telepon Selular GSM
Informasi Cara Penggunaan
Informasi Lagu
Informasi Harga
Informasi Cakupan
Komunikasi Pemasaran
Advertising
Sales Promotion
Publicity
Personal Selling
Direct Marketing
ACCEPTANCE
RATE OF
VMS SERVICES
Rumor Cara Menggunakan
Rumor Lagu
Rekomendasi Cara
Rekomendasi Lagu
Diskualifikasi Cara
Diskualifikasi Lagu
Kelompok Rujukan
Keluarga
Teman
Pemimpin Pendapat
Behavioral Attitude
Imitation
Conditioning
Inactive
Opportunity
Distrust
Opinion Decision
Need
Consumer Profile
- Kemampuan menggunakan
- Kesesuaian Lagu
- Sensitifitas Harga
- Daftar Phonebook
+
-

28. Virtual World
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29. Agents
▪ Konsumen VMS
▪ Pelanggan Telkomsel
▪ Pelanggan Indosat
▪ Pelanggan Excelcom
▪ Operator Telekomunikasi Selular GSM
▪ Kinerja Komunikasi Pemasaran
▪ Group Reference Influence
▪ Rumor
▪ Rekomendasi
▪ Diskualifikasi
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30. Agent’s Attribute
▪ Pelanggan
▪ Tingkat kemampuan menggunakan
▪ Tingkat kesesuaian lagu
▪ Tingkat Kesesuaian Harga
▪ Behaviour Attitude
▪ Operator yang digunakan
▪ Kinerja Komunikasi Pemasaran
▪ Info cara menggunakan, info lagu, info harga, info cakupan pelayanan
▪ Kelompok Rujukan
▪ Rumor, diskualifikasi dan rekomendasi
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31. Agent’s Methods
▪ Behaviour Attitude
▪ Komunikasi Antar pelanggan
▪ Keputusan pelanggan
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Inactive BA
(Min_threshold,
Maks_threshold,
state=0)
Active BA
(conditioning)
Siap menerima
pengaruh positif
State=1
Active BA
(imitating)
menerima pengaruh
positif
State=2
Active BA
(opportunis)
Siap menerima
pengaruh negatif
State=-1
Active BA
(distrust)
menerima pengaruh
negatif
State=-2
Positif Stimulus > Positif Threshold
Negatif_Threshold < Negatif Stimulus
Positif Stimulus > Positif Threshold
Positif Stimulus > Positif Threshold
Positif Stimulus > Positif Threshold
Negatif_Threshold < Negatif Stimulus
Negatif_Threshold < Negatif Stimulus
Negatif_Threshold < Negatif Stimulus
Inactive BA
(Min_threshold,
Maks_threshold,
state=0)

32. Implementasi Model Perilaku Konsumen VMS Berbasis ABM
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33. Terima Kasih
2022
PT INDUSTRI TELEKOMUNIKASI INDONESIA (PERSERO)

34. https://www.npr.org/sections/13.7/2017/01/04/506400719/video-swooping-starlings-in-murmuration