Agent-based modeling (ABM) is a simulation technique that models complex systems as collections of autonomous agents, allowing for the analysis of emergent behaviors from their interactions. It utilizes a bottom-up approach to understanding systems, contrasting with traditional top-down methods, and excels in handling heterogeneity, realism, and dynamic behaviors. However, ABM faces challenges such as data limitations, programming complexities, and computational demands.

1. Agent Based Modeling (ABM)
Introduction
Dr. Yudi Limbar Yasik
mail: yudiyasik@yahoo.com
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2. Why ABM??
o Complex and non linear systems
o Not easy to approach by mathematical or
statistic model
o Bottom up aproach
o System simulation
o Scenario, prediction
o Ethical problem, Non Parametrik
o Computer and simulation technology
development
o artificial Intelegent & interaction 2

3. ABM Theory
o “ Agent-based modeling is a powerful simulation modeling technique
that has seen a number of applications in the last few years, including
applications to real-world business problems. In agent-based modeling
(ABM), a system is modeled as a collection of autonomous decision-
making entities called agents.” (Bonabeau 2002:7280)
o “Agent-based modeling is a bottom-up approach to understanding
systems which provides a powerful tool for analysing complex, non-
linear markets. The method involves creating artificial agents designed to
mimic the attributes and behaviours of their real-world counterparts. The
system’s macro-observable properties emerge as a consequence of these
attributes and behaviours and the interactions between them. The
simulation output may be potentially used for explanatory, exploratory
and predictive purposes”. (Twomey dan Cadman 2002:56)
o ”It is a methods for studying systems exhibiting the following two properties:
(1) the system is composed of interacting agents; and (2) the system
exhibits emergent properties, that is, properties arising from the interaction
of the agents that cannot be deduced simply by aggregating the properties
of the agents.” (Axelroad dan Tesfatsion 2005:3) 3

4. DEFINISI
o ABM :
o Research / Experiment metodology
o bottom-up approach
o Interaction among individual behavior
o Computer simulation
o mimic the attributes and behaviours of their real-world
o interacting agents; and
o the system exhibits emergent properties
o yang dapat digunakan lagi sebagai alat bantu untuk
eksplanatori, eksploratori atau prediksi dalam mengambil
keputusan di dunia nyata.
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5. 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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6. Agent Based Modeling Experiment
Testfatsion, 2005, ACE Modeling Economies as Complex Adaptive Systems
o Modeler constructs a
virtual world populated
constructs 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)
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7. Building an agent-based model
Twomey & Cadman, 2002, Agent-based modelling of customer behaviour in the telecoms
and media markets
o Constructing a population of interacting elements or agents.
These agents may be people, firms or other objects.
o Agents are designed to mimic their real life counterparts consists
of:
o Attributes (e.g. age, sex, preferences).
o Behaviours (e.g. actions based on decision making algorithms such a
utility maximization).
o The attributes and behaviours may not only vary across agents in
the model but they may also change within agents during the
simulation. For example, an agent-based model may incorporate
genetic algorithms or neural nets within the agents which allow the
agents to learn throughout the simulation
o Once the agents are created and parameterised the model can
then be run
o During and after the simulation run, output probes can provide
information on any particular agent’s attributes and behaviours as
well as the system’s macro-variables.
o One objective of the simulation is to provide the user with a good
idea of how the real system would behave under the conditions 7
portrayed in the model.

8. Perbandingan ABM dengan Model Kuantitatif
Pemodelan Ekonomi
Pemodelan dengan Agent Based (ABM)
Secara Kuantitatif
Model dibangun untuk mengungkapkan
Model dibangun untuk
permasalahan dengan pendekatan dari bawah ke
menyederhanakan
atas (bottom up approach), Twomey dan Cadman
permasalahan
(2002:56)
Model adalah langkah awal untuk menghasilkan
Model dihasilkan dari
data empirik, simulasi yang dijalankan dengan
pengolahan 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 tetapi
memecahkan masalah agen-agen dalam model yang akan memecahkan
yang dihadapi masalah yang dihadapi, Bonabeau (2002:7280)
Model yang dibuat adalah Model yang dibuat adalah langkah awal dari
hasil akhir dari penelitian penelitian, Bryson ett. all (2005:1) 8

9. 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. 9

10. 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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11. 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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12. 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
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http://ccl.northwestern.edu/netlogo/

13. Proses Pembuatan ABM
(1)
Studi Pustaka
Observasi
wawancara
Spesifikasi:
(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
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14. ABM Area of implementation (in Management)
Learning and the There exists a broad range of algorithms which represent the learning process of
embodied mind computational agents, e.g. genetic algorithms.
Evolution of Norms are generated by interaction and in social settings. AXELROD (1997, 47) uses the
behavioural 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 a
of 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 of
economic 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 performance
organisations 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 not
computational possible to know explicitly why real agents respectively human beings make a particular
agents 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 are
computational environments developed and still under construction which support application for non-skilled
laboratories researchers. These computational laboratories permit the study of systems of multiple 14
interacting agents by means of controlled and replicable experiments, e.g. Swarm or RePast.

15. CONTOH ABM dengan Netlogo
CONSUMER BEHAVIOUR MODEL
For VOICE MUSIC SMS (VMS)
VALUE ADDED SERVICE
AT GSM OPERATOR IN INDONESIA
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16. TERIMA KASIH
yudily@yahoo.com
0816-420-8382
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17. ABM Cont..
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 . 17

18. ABM Cont. (Axelroad&Tesfatsion, 2005)
o Sistem yang terdiri dari agen-agen yang saling
berinteraksi satu sama lainnya;
o Sistem yang memunculkan emergent properties
yaitu sifat-sifat yang muncul dari interaksi antar
agen yang tidak bisa di deduksi secara sederhana
dengan mengumpulkan semua sifat dari agen-agen
tersebut.
o Ketika interaksi antar agen merupakan kesatuan
dengan pengalaman masa lalu, khususnya bila
agen tadi secara terus-menerus beradaptasi
terhadap pengalaman yang dihadapi,
o analis secara matematis biasanya sangat terbatas
dan sangat sukar untuk memodelkan sistem
o [i] Axelrod, R dan Tesfatsion, L., 2005, “A guide for Newcomers to Agent-based Modeling in the Social Sciences”, University of Michigan
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