1 Ass. Wr. Wb. SYSTEM THINKING ANALYSIS & MODELLING PROCESSES Soemarno 2009 BAHAN KAJIAN MK. ANALISIS EKOSISTEM

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3 Systems thinking Systems thinking is an approach to analysis that is based on the belief that the component parts of a system will act differently when isolated from its environment or other parts of the system. Because the whole is greater than the sum of its parts, (the relationship between the parts is what should be under observation) any atomistic analysis, is considered reductionistic. Standing in contrast to Descartes's, and others', reductionism, it proposes to view systems in a holistic manner. Consistent with systems philosophy, systems thinking concerns an understanding of a system by bringing the linkages and interactions to bear between the elements that comprise the entirety of the system. It depicts all human-activity systems as open systems, that they are affected by the environment in which they exist.

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5 Systems thinking attempts to illustrate that, in complex systems, events are separated by distance and time; hence, small catalytic events can cause large changes in a system. Acknowledging that a change in one area of a system can adversely affect another area of the system, it promotes organizational communication at all levels in order to avoid the silo effect. Both systems thinkers and futurists consider that: 1.a "system" is a dynamic and complex whole, interacting as a structured functional unit; 2.information flows between the different elements that compose the system; 3.a system is a community situated within an environment; 4.information flows from and to the surrounding environment via semi- permeable membranes or boundaries 5.systems are often composed of entities seeking equilibrium but can exhibit oscillating, chaotic, or exponential growth or decay behavior.

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7 What is a system? A system is any set (group) of interdependent or temporally interacting parts. Parts are generally systems themselves and are composed of other parts, just as systems are generally parts or holons of other systems. Systems thinking techniques may be used to study any kind of system — natural, scientific, human, or conceptual. The Systems approach rests on two tenets: "The Whole is more than the sum of the parts" — Aristotle The development ethic.

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9 Examples Systems thinking often involves considering a "system" in different ways: Rather than trying to improve the braking system on a car by looking in great detail at the composition of the brake pads (reductionist), the boundary of the braking system may be extended to include not only the components of the car, but the driver, the road and the weather, and considering the interactions between them. Looking at something as a series of conceptual systems according to multiple viewpoints. A supermarket could be considered as a "profit making system" from the perspective of management, an "employment system" from the perspective of the staff, and a "shopping system" — or perhaps an "entertainment system" — from the perspective of the customers. As a result of such thinking, new insights may be gained into how the supermarket works, why it has problems, or how changes made to one such system may impact on the others.

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11 Methods The application of Systems thinking has been grouped into three categories based on the techniques used to tackle a system: Hard systems — involving simulations, often using computers and the techniques of operations research. Useful for problems that can justifiably be quantified. However it cannot easily take into account unquantifiable variables (opinions, culture, politics, etc), and may treat people as being passive, rather than having complex motivations.

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13 Soft systems — For systems that cannot easily be quantified, especially those involving people holding multiple and conflicting frames of reference. Useful for understanding motivations, viewpoints, and interactions and addressing qualitative as well as quantitative dimensions of problem situations. Soft systems are a field that utilizes foundation methodological work developed by Peter Checkland, Brian Wilson and their colleagues at Lancaster University. Morphological analysis is a complementary method for structuring and analysing non-quantifiable problem complexes.

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15 Evolutionary systems — Bela H. Banathy developed a methodology applicable to the design of complex social systems. This technique integrates critical systems inquiry with soft systems methodologies. Evolutionary systems, similar to dynamic systems are understood as open, complex systems, but with the capacity to evolve over time. Banathy uniquely integrated the multidisciplinary perspectives of systems research (including chaos, complexity, cybernetics), cultural anthropology, evolutionary theory, and others.

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19 Morphological analysis Morphological analysis (or General Morphological Analysis) is a method developed by Fritz Zwicky (1967, 1969) for exploring all the possible solutions to a multi- dimensional, non-quantified problem complex. Fritz Zwicky As a problem-structuring and problem-solving technique, morphological analysis was designed for multi-dimensional, non-quantifiable problems where causal modeling and simulation do not function well or at all.

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21 ZwickyZwicky developed this approach to address seemingly non-reducible complexity. Using the technique of cross consistency assessment (CCA) (Ritchey, 1998), the system however does allow for reduction, not by reducing the number of variables involved, but by reducing the number of possible solutions through the elimination of the illogical solution combinations in a grid box. A detailed introduction to morphological modeling is given in Ritchey (2002, 2006).

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23 Overview Morphological analysis (MA) is a method for exploring all possible solutions in a complex problem space. The method was developed by Fritz Zwicky, the Swiss astrophysicist based at the California Institute of Technology. Zwicky applied MA inter alia to astronomical studies and the development of jet and rocket propulsion systems. Morphology comes from the classical Greek concept morphé, meaning shape or form. MA concerns the arrangement of objects and how they conform to create a whole or Gestalt. The objects in question can be a physical system (e.g. anatomy), a social system (e.g. an organisation) or a logical system (e.g. word forms or a system of ideas).

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25 Swedish Morphological Sociery The Swedish Morphological Society is a non-profit scientific organization, whose purpose is the development and dissemination of knowledge concerning the scientific use of general morphological analysis, its theory and practice.Swedish Morphological Society The site contains articles and links on morphological analysis, a history of its development, case studies and a tutorial.

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28 PROSES PEMODELAN INTRODUCTION DEFINITION HYPOTHESES MODELLING VALIDATION INTEGRATION SISTEM - MODEL - PROSES Bounding - Word Model Alternatives: Separate - Combination Relevansi : Indikator - variabel - subsistem Proses : Linkages - Impacts Hubungan : Linear - Non-linear - interaksi Decision table: Data : Plotting - outliers Analisis : Test - Estimation Choice : Verifikasi: Subyektif - reasonable Uji Kritis: Eksperiment - Analisis/Simulasi Sensitivity: Uncertainty - Resources - - Interaksi Communication Conclusions

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30 Proses Pemodelan SISTEM: Approach Simulasi Sistem Analisis Sistem Model vs. Pemodelan Mathematical models: An exact science, Its Practical Application: 1. A high degree of intuition 2. Practical experiences 3. Imagination 4. “Flair” 5. Problem define & bounding

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32 DEFINISI & PEMBATASAN IDENTIFIKASI dan PEMBATASAN Masalah penelitian 1. Alokasi sumberdaya penelitian 2. Aktivitas penelitian yang relevan 3. Kelancaran pencapaian tujuan Keseluruhan SISTEM vs. sub-sistem nya Proses pembatasan masalah: 1. Bersifat iteratif, tidak mungkin “sekali jadi” 2. Make a start in the right direction 3. Sustain initiative and momentum System bounding: SPACE - TIME - SUB-SYSTEMS Sample vs. Population

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34 KOMPLESITAS DAN MODEL The real system sangat kompleks Proses Pengujian Model Hipotetik The hypotheses to be tested Sub-systems MODEL Trade-off: complexity vs. simplicity

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36 MODEL KATA-KATA Masalah penelitian dideskripsikan secara verbal, dengan meng-gunakan kata (istilah) yang relevan dan simple Pengembangan Model simbolik Hubungan-hubungan verbal dipresentasikan dengan simbol-simbol yang relevan Simbolisasi kata-kata atau istilah Setiap simbol (simbol matematik) harus dapat diberi deskripsi penjelasan maknanya secara jelas

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38 PENYELESAIAN MODEL Alternatif “solusi” jawaban permasalahan, berapa banyak? Pada awalnya diidentifikasi sebanyak mungkin alternatif jawaban yang mungkin Penggabungan beberapa alternatif jawaban yang mungkin digabungkan

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40 HYPOTHESES Tiga macam hipotesis: 1. Hypotheses of relevance: mengidentifikasi & mendefinisikan faktor, variabel, parameter, atau komponen sistem yang relevan dg permasalahan 2. Hypotheses of processes: merangkaikan faktor-faktor atau komponen- komponen sistem yg relevan dengan proses / perilaku sistem dan mengidentifikasi dampaknya thd sistem 3. Hypotheses of relationship: hubungan antar faktor, dan representasi hubungan tersebut dengan formula-formula matematika yg relevan, linear, non linear, interaktif. Penjelasan / justifikasi Hipotesis Justifikasi secara teoritis Justifikasi berdasarkan hasil-hasil penelitian yang telah ada

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42 MODEL CONSTRUCTION Konstruksi Model Proses seleksi / uji alternatif yang ada Manipulasi matematis Data dikumpulkan dan diperiksa dg seksama untuk menguji penyimpangannya terhadap hipotesis. Grafik dibuat dan digambarkan untuk menganalisis hubungan yang ada dan bagaimana sifat / bentuk hubungan itu Uji statistik dilakukan untuk mengetahui tingkat signifikasinya

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44 VERIFICATION & VALIDATION VERIFIKASI MODEL 1. Menguji apakah “general behavior of a MODEL” mampu mencerminkan “the real system” 2. Apakah mekanisme atau proses yang di “model” sesuai dengan yang terjadi dalam sistem 3. Verifikasi: subjective assessment of the success of the modelling 4. Inkonsistensi antara perilaku model dengan real-system harus dapat diberikan penjelasannya Proses Pemodelan VALIDASI MODEL 1. Sampai seberapa jauh output dari model sesuai dengan perilaku sistem yang sesungguhnya 2. Uji prosedur pemodelan 3. Uji statistik untuk mengetahui “adequacy of the model” 4.

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46 SENSITIVITY ANALYSIS Perubahan input variabel dan perubahan parameter menghasilkan variasi kinerja model (diukur dari solusi model) ……… analisis sensitivitas Validasi MODEL Variabel atau parameter yang sensitif bagi hasil model harus dicermati lebih lanjut untuk menelaah apakah proses-proses yg terjadi dalam sistem telah di “model” dengan benar

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48 PLANNING & INTEGRATION PLANNING Integrasi berbagai macam aktivitas, formulasi masalah, hipotesis, pengumpulan data, penyusunan alternatif rencana dan implementasi rencana. Kegagalan integrasi ini berdampak pada hilangnya komunikasi : 1. Antara data eksperimentasi dan model development 2. Antara simulasi model dengan implementasi model 3. Antara hasil prediksi model dengan implementasi model 4. Antara management practices dengan pengembangan hipotesis yang baru 5. Implementasi hasil uji coba dengan hipotesis yg baru DEVELOPMENT of MODEL 1. Kualitas data dan pemahaman terhadap fenomena sebab- akibat (proses yang di model) umumnya POOR 2. Analisis sistem dan pengumpulan data harus dilengkapi dengan mekanisme umpan-balik 3. Pelatihan dalam analisis sistem sangat diperlukan 4. Model sistem hanya dapat diperbaiki dengan jalan mengatasi kelemahannya 5. Tim analisis sistem seyogyanya interdisiplin

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