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


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Agent Based Modeling (ABM) for Customer Behavior Model

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

  1. 1. Introduction ofAgent Based Modeling (ABM)for Customer Behavior Model Dr. Ir. Yudi Limbar Yasik., MSc mail : 1
  2. 2. ConceptsoIlmu Ekonomi ?oMarketing ?oConsumer Behavior ?oModeling?oAgent Based Modeling? 2
  3. 3. Ilmu Ekonomi?oIlmu yang mempelajari tentang kebutuhan manusiaoBarteroKonsep uangoKonsep Supply : Demand 3
  4. 4. Marketing Concept?o Kapan dibutuhkan o Supply < Demand o Supply = Demand o Supply > Demando Ilmu yang pempelajari tentang perilaku manusia dalam memenuhi kebutuhannyao Dasar ilmu marketing adalah ilmu tentang perilaku manusia (Consumer Behaviour) 4
  5. 5. ON T ITI EC C O H AV GN FFMarketing Strategy BE C O ER A NS IO UM R AN SU M ER D N CO MARKETING text STRATEGY CONSUMER Consumer Research ENVIRONMENT And Analysis Consumers:Affect and Cognition Marketing Strategy Behaviour Development Envoironment Marketing Strategy Implementation 5
  6. 6. Consumer Behaviour MASUKAN / PENGARUH EXTERNAL Lingkungan Sosio-Budaya Usaha Pemasaran - Keluarga - Produk - Sumber informasi - Promosi - Reff Group - Harga - kelas sosial - Saluran distribusi - sub budaya & budaya PROSES PENGAMBILAN KEPUTUSAN (INTERNAL) Pengenalan kebutuhan Psikology - Motivasi Penyelidikan sebelum - persepsi pembelian - Pengetahuan - Kepribadian Evaluasi Alternatif - Sikap Pengalaman/ Pembelajaran ACTION Pembelian - Percobaan Pembelian - Pembelian ulang Evaluasi Setelah 6 PembelianSumber: Schiffman & Kanuk. 2000: 8
  7. 7. Modelingo Model Matematis o Misal : oS=Vxt A model o V= S / t • (from V.L. *modellus, dim. of L. ot=S/V modulus "measure, standard," dim.o Model Statistik of modus "manner, measure" - Online Etymology Dic.) o Misal : • is a pattern, plan, representation o Linear regression (especially in miniature), or o y = α + βx + ε description designed to show the main object or workings of an object, system, or concept. 7
  8. 8. Why Agent Based Modeling??o Model yang tidak bisa di dekati dengan persamaan matematis atau statistiko Sistem yang kompleks tidak linear seperti perilaku manusiao Ethical problem, Non Parametriko Bottom up aproacho Simulasi sistemo Skenario, prediksio Kemajuan di bidang ilmu simulasi dan komputer,o artificial Intelegent 8
  9. 9. ABM Teoryo 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 . 9
  10. 10. DEFINISIo 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. 10
  11. 11. The key feature of agent-based modelingTwomey & Cadman, 2002, Agent-based modelling of customer behaviour in the telecoms and media marketso 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. 11
  12. 12. Agent Based Modeling Experiment Testfatsion, 2005, ACE Modeling Economies as Complex Adaptive Systems o Modeler constructs a virtual world populatedconstructs a virtual world by various agent types sets initial world conditions (company, consumer, market, supplier, regulator) o Modeler sets initial world conditions (consumer, market The world develops over time place) Culture Disk o Modeler then steps back (agent interaction) to observe how the world develops over time (no further intervention by the modeler is permitted) o World events are driven Emergent Behavior by agent interactions (macro behavior) 12
  13. 13. Perbandingan ABM dengan Model Kuantitatif Pemodelan Ekonomi Pemodelan dengan Agent Based (ABM) Secara Kuantitatif Model dibangun untuk mengungkapkanModel dibangun untuk permasalahan dengan pendekatan dari bawah kemenyederhanakan atas (bottom up approach), Twomey dan Cadmanpermasalahan (2002:56) Model adalah langkah awal untuk menghasilkanModel dihasilkan dari data empirik, simulasi yang dijalankan denganpengolahan data empirik model akan menghasilkan data empirik ,(seperti data hasil survey) Axelroad dan Tesfatsion (2005:4)Model yang dibuat untuk Bukan model yang menyelesaikan masalah tetapimemecahkan masalah agen-agen dalam model yang akan memecahkanyang dihadapi masalah yang dihadapi, Bonabeau (2002:7280)Model yang dibuat adalah Model yang dibuat adalah langkah awal darihasil akhir dari penelitian penelitian, Bryson ett. all (2005:1) 13
  14. 14. Strengths of agent-based modelingo 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 solutionso 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 modelo + Ethical, parametric design. 14
  15. 15. Weaknesses of agent-based modellingo 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. 15
  16. 16. Designing an agentHood, L. (1998), ``Agent based modelling’’, available at 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. 16
  17. 17. Platform ABMo Swarn (berbasis bahasa C) o Bahan tentang Repast dapat didapat secara on line dari Repast (Recursive Porus Agent Simulation Toolkit: berbasis Java) o Bahan tentang Repast dapat didapat secara on line dari Mason (Multi-Agent Simulator of Neighborhoods: untuk kecepatan) o Bahan tentang Repast dapat didapat secara on line dari Netlogo (paling lengkap dokumentasi dan lebih praktis digunakan) o Bahan tentang Repast dapat didapat secara on line dari 17
  18. 18. Proses Pembuatan ABM (1)Studi Pustaka Observasi wawancaraSpesifikasi: (2)Virtual World Desain Agents Model Properti Berbasis Method ABM Dokumen (3) Desain Pembuatan sistem ABM Model Berbasis ABM Model (4) Berbasis Uji Validitas & ABM Reliabilitas Model (5) Valid ABM Eksperiment Model dengan ABM (6) Data Hasil Uji Statistik Eksperiment & Observasi Keterangan : Mdoel Output Proses Emergent Behavior Model Kuantitatif 18
  19. 19. ABM Area of implementation (in Management)Learning and the There exists a broad range of algorithms which represent the learning process ofembodied mind computational agents, e.g. genetic algorithms.Evolution of Norms are generated by interaction and in social settings. AXELROD (1997, 47) uses thebehavioural norms 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 The major point in markets is the ability to perform self organisation. Some markets follow aof market processes path dependency while others behave differently. Nearly every market can be investigated by using agent-based simulations.Formation of Economic networks play a crucial role in social and economic science. The formation ofeconomic networks transaction networks by strategically interacting agents takes the centre stage.Modeling of An organisation consists of a number of people which have an objective or performanceorganisations 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 There are two main differences regarding experiments with real and computational agents:with real and The behaviour of computational agents is determined and known in advance while it is notcomputational possible to know explicitly why real agents respectively human beings make a particularagents choice. Performing both experiments in parallel could support the finding of insights.Building ACE Work with agent-based models needs computer and programming skills. There arecomputational environments developed and still under construction which support application for non-skilledlaboratories researchers. These computational laboratories permit the study of systems of multiple 19 interacting agents by means of controlled and replicable experiments, e.g. Swarm or RePast.
  21. 21. Model Perilaku Konsumen VMSyudi limbar yasik, 2008Komunikasi Pemasaran ACCEPTANCE Advertising RATE OF Sales Promotion Publicity VMS SERVICES Personal Selling Direct Marketing Pelanggan Telepon Selular GSM Opinion DecisionInformasi Cara Penggunaan Informasi Lagu Informasi Harga + - Informasi CakupanRumor Cara Menggunakan Behavioral Attitude Need Rumor Lagu Rekomendasi Cara Rekomendasi Lagu Imitation Diskualifikasi Cara Diskualifikasi Lagu Conditioning Inactive Consumer Profile - Kemampuan menggunakan Opportunity - Kesesuaian Lagu Kelompok Rujukan - Sensitifitas Harga Keluarga Distrust - Daftar Phonebook Teman Pemimpin Pendapat 21
  22. 22. Virtual World 22
  23. 23. Agentso Konsumen VMS o Pelanggan Telkomsel o Pelanggan Indosat o Pelanggan Excelcomo Operator Telekomunikasi Selular GSM o Kinerja Komunikasi Pemasarano Group Reference Influence o Rumor o Rekomendasi o Diskualifikasi 23
  24. 24. Agent’s Attributeo Pelanggan o Tingkat kemampuan menggunakan o Tingkat kesesuaian lagu o Tingkat Kesesuaian Harga o Behaviour Attitude o Operator yang digunakano Kinerja Komunikasi Pemasaran o Info cara menggunakan, info lagu, info harga, info cakupan pelayanano Kelompok Rujukan o Rumor, diskualifikasi dan rekomendasi 24
  25. 25. Agent’s Methods Active BA (imitating) menerima pengaruh positif State=2o Behaviour AttitudePositif Stimulus > Positif Threshold Negatif_Threshold < Negatif Stimulus Active BAo Komunikasi Antar (conditioning) Siap menerima pengaruh positif pelanggan Positif Stimulus > Positif Threshold State=1 Negatif_Threshold < Negatif Stimuluso Keputusan pelanggan Inactive BA (Min_threshold, Maks_threshold, state=0) Positif Stimulus > Positif Threshold Negatif_Threshold < Negatif Stimulus Active BA (opportunis) Siap menerima pengaruh negatif State=-1 Positif Stimulus > Positif Threshold Negatif_Threshold < Negatif Stimulus Active BA (distrust) menerima pengaruh negatif State=-2 25
  26. 26. Implementasi Model PerilakuKonsumen VMS Berbasis ABM 26
  27. 27. TERIMA 0816-420-8382 27