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Dr. Ir. Yudi Limbar Yasik., MSc

mail : yudiyasik@yahoo.com

hp : 0816- 420-8382

0818- 221-699

0812-218-20090

Introduction of Agent Based Modeling (ABM) for Customer Behavior Model

Concepts

oIlmu Ekonomi ?

oMarketing ?

oConsumer Behavior ?

oModeling?

oAgent Based Modeling?

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Ilmu Ekonomi?

oIlmu yang mempelajari tentang

kebutuhan manusia

oBarter

oKonsep uang

oKonsep Supply : Demand

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Marketing Concept?

o Kapan dibutuhkan

o Supply < Demand

o Supply = Demand

o Supply > Demand

o Ilmu yang pempelajari tentang perilaku

manusia dalam memenuhi kebutuhannya

o Dasar ilmu marketing adalah ilmu tentang

perilaku manusia (Consumer Behaviour)

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Marketing Strategy

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Consumer Research

And Analysis

Marketing Strategy

Development

Marketing Strategy

Implementation

Consumers:

Affect and Cognition

Behaviour

Envoironment

textMARKETING

STRATEGY

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CONSUMER

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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

Modeling

o Model Matematis

o Misal :

o S = V x t

o V= S / t

o t = S / V

o Model Statistik

o Misal :

o Linear regression

o y = α + βx + ε

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A model

• (from V.L. *modellus, dim. of L.

modulus "measure, standard," dim.

of modus "manner, measure" -

Online Etymology Dic.)

• is a pattern, plan, representation

(especially in miniature), or

description designed to show the

main object or workings of an

object, system, or concept.

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Why Agent Based Modeling??

o Model yang tidak bisa di dekati dengan

persamaan matematis atau statistik

o Sistem yang kompleks tidak linear seperti

perilaku manusia

o Ethical problem, Non Parametrik

o Bottom up aproach

o Simulasi sistem

o Skenario, prediksi

o Kemajuan di bidang ilmu simulasi dan komputer,

o artificial Intelegent

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ABM Teory

o Secara konsep ABM diturunkan dari gabungan antar disiplin ilmu yang dikenal dengan konsep “Science complexity” istilah yang diangkap oleh Levin 1999[i].

o 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.

o 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.

o [i] Lewin, R. (1999), Complexity: Life at the Edge of Chaos, University of Chicago Press, Chicago, IL.

DEFINISI

o ABM adalah :

o suatu metode yang digunakan untuk penelitian / eksperimen

o dengan melihat pendekatan dari bawah ke atas (bottom-up)

o bagaimana interaksi perilaku-perilaku individu dapat

mempengaruhi perilaku sistem,

o dengan simulasi berbasis komputer

o untuk memodelkan semua perilaku entitas (agen) yang terlibat

dalam dunia nyata

o dengan harapan interaksi antar entitas dapat menghasilkan atau

menggambarkan sifat utama

o yang dapat digunakan lagi sebagai alat bantu untuk

eksplanatori, eksploratori atau prediksi dalam mengambil

keputusan di dunia nyata.

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The key feature of agent-based modeling Twomey & Cadman, 2002, Agent-based modelling of customer behaviour in the telecoms and media markets

o The term ``agent’’ in the context of business or economic modeling refers to real world objects such as people or firms.

o In the agent-based approach the focus turns to the properties of the individual agents.

o 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.

o Involves a bottom-up approach to understanding a system’s behavior (e.g fish or bird group). o 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.

o 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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Agent Based Modeling Experiment Testfatsion, 2005, ACE Modeling Economies as Complex Adaptive Systems

o Modeler constructs a virtual world populated by various agent types (company, consumer, market, supplier, regulator)

o Modeler sets initial world conditions (consumer, market place)

o Modeler then steps back to observe how the world develops over time (no further intervention by the modeler is permitted)

o World events are driven by agent interactions

constructs a virtual world

sets initial world conditions

The world develops over time

Culture Disk

(agent interaction)

Emergent Behavior

(macro behavior)

Perbandingan ABM dengan Model Kuantitatif

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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)

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Strengths of agent-based modeling o System assumptions, The emergent non-equilibrium, dynamical behaviour of a

system is usually one of the most interesting outputs of agentbased models.

o Realism. This allows us to undertake qualitative scenario exploration to investigate the structure or morphology of the system independently of the details.

o 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.

o 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

o 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.

o 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.

o Object-orientated analysis, design and programming.

o 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

o + Ethical, parametric design.

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Weaknesses of agent-based modelling

o 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.

o 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.

o 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.

o 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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Designing an agent Hood, L. (1998), ``Agent based modelling’’, available at www.brs.gov.au/social_sciences/kyoto/hood2.html

o Low fidelity. o all the agents in the model have the same behaviour and intrinsic attributes.

o This situation would not even be categorised as an ABM by many practitioners.

o It is of interest for problems where the statistics of the collection of entities are of interest.

o This situation occurs in many physics and chemistry simulations (e.g. the molecular level simulation ofmaterial properties or drug design).

o 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).

o Medium fidelity. o Here an observed distribution of the agents’ behaviour is used to ``calibrate’’ the model.

o 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).

o 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’’).

o High fidelity. o 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.

o 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.

o At this level of fidelity we are thus capturing some notion of a mentalistic or cognitive agent.

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Platform ABM

o Swarn (berbasis bahasa C) o Bahan tentang Repast dapat didapat secara on line dari

http://www.swarm.org/wiki/Main_Page

o Repast (Recursive Porus Agent Simulation Toolkit:

berbasis Java) o Bahan tentang Repast dapat didapat secara on line dari

http://repast.sourceforge.net/repast_3/index.html

o Mason (Multi-Agent Simulator of Neighborhoods: untuk

kecepatan) o Bahan tentang Repast dapat didapat secara on line dari

http://cs.gmu.edu/~eclab/projects/mason/

o Netlogo (paling lengkap dokumentasi dan lebih praktis

digunakan) o Bahan tentang Repast dapat didapat secara on line dari

http://ccl.northwestern.edu/netlogo/

Proses Pembuatan ABM

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(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

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ABM Area of implementation (in Management)

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.

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CONTOH ABM

CONSUMER BEHAVIOUR MODEL

For VOICE MUSIC SMS (VMS)

VALUE ADDED SERVICE

AT GSM OPERATOR IN INDONESIA

http://abm.cantiknatural.com http://ccl.northwestern.edu/netlogo/models/community/customerBehavior

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Model Perilaku Konsumen VMS yudi limbar yasik, 2008

Pelanggan Telepon Selular GSM

Informasi Cara Penggunaan

Informasi Lagu

Informasi Harga

Informasi Cakupan

Komunikasi PemasaranAdvertising

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 RujukanKeluarga

Teman

Pemimpin Pendapat

Behavioral Attitude

Imitation

Conditioning

Inactive

Opportunity

Distrust

Opinion Decision

Need

Consumer Profile

- Kemampuan menggunakan

- Kesesuaian Lagu

- Sensitifitas Harga

- Daftar Phonebook

+ -

Virtual World

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Agents

o Konsumen VMS

o Pelanggan Telkomsel

o Pelanggan Indosat

o Pelanggan Excelcom

o Operator Telekomunikasi Selular GSM

o Kinerja Komunikasi Pemasaran

o Group Reference Influence

o Rumor

o Rekomendasi

o Diskualifikasi

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Agent’s Attribute o Pelanggan

o Tingkat kemampuan menggunakan

o Tingkat kesesuaian lagu

o Tingkat Kesesuaian Harga

o Behaviour Attitude

o Operator yang digunakan

o Kinerja Komunikasi Pemasaran

o Info cara menggunakan, info lagu, info harga,

info cakupan pelayanan

o Kelompok Rujukan

o Rumor, diskualifikasi dan rekomendasi 24

Agent’s Methods

o Behaviour Attitude

o Komunikasi Antar

pelanggan

o 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)

Implementasi Model Perilaku

Konsumen VMS Berbasis ABM

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TERIMA KASIH

yudily@yahoo.com

0816-420-8382