Multiple tests for validation of system dynamics type of simulation models

Barlas, Yaman

doi:10.1016/0377-2217(89)90059-3

Cited by 359 publications

(193 citation statements)

References 5 publications

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“…This study used behavioural validity tests (Barlas, 1989(Barlas, , 1996Schwaninger and Groesser, 2009) examinig two components the model behaviour is valid; that its ability to mimic the major pattern exhibited by the real system and its structure has no major error. For this purpose, this study used the extreme condition test (Sterman, 2000).…”

Section: Methodsmentioning

confidence: 99%

Using Behavioural Validity Method to Analyse the Dynamic Model of Smallholder Beef Farming Systems in Indonesia

Setianto

2016

JAP

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Abstract. Smallholders beef farming is a complex systems which has wide range of stakeholders whose interests are varied. Systems thinking is one approach which can be recommended to study the complexity of a system. Model is developed to mimic the situation of the farming situation in the real world. A model opens up possibilities for simulating an intervention easier, less dangerously, and more ethically than experimenting in the real world. However, before a model were used to simulate any intervention strategy, it needs to be validated. This paper aimed to describe one validity method which used to test the validity of a model describing the smallholder beef farming. A series of surveys have been undertaken to harness perspectives, opinion, and data from 2 beef farmers group in Kabupaten Banjarnegara and Kabupaten Banyumas. Model were developed using iThink software developed by Ventana®. Behavioural validity was conducted using extreme condition test which use 4 combination of extreme value; calving interval, share to farmer, purchasing price, and selling price. Result showed that behavioural validity method using extreme value test was able to show the consistency of the logic which construct he structure of the model. Key words: model validity, smallholder, beef farming, systems thinking, extreme condition test Abstrak. Usaha peternakan sapi potong skala kecil merupakan sebuah system yang kompleks. Banyak pihak yang terlibat dalam system tersebut. Masing-masing pihak memiliki tujuan dan kepentingan yang berbedabeda. Pendekatan yang sebaiknya dilakukan ketika mempelajari sebuah system yang kompleks adalah menggunakan systems thinking. Model, yang merupakan salah satu luaran dari systems thinking, dibuat untuk mensimulasikan kondisi yang terjadi sebenarnya. Penggunaan model dianggap lebih etis, lebih mudah, dan lebih aman untuk melakukan simulasi dari skenario-skenario yang dirancang untuk meningkatkan performa system. Namun demikian, sebelum digunkan sebagai alat simulasi, sebuah model harus melalui tahap uji validitas. Artikel ini bertujuan untuk menggambarkan satu metode validasi model, yakni menggunakan behavioural method. Serangkaian survey sudah dilakukan terhadap 2 kelompok peternak sapi potong di Kabupaten Banjarnegara dan Kabupaten Banyumas untuk berdiksusi dengan peternak tentang struktur hubungan yang ada di dalam model. Model disusun menggunakan software iThink yang dikembangkan oleh Ventana®. Uji validitas yang digunakan dalam behavioural method ini menggunakan uji nilai ekstrim. Terdapat 4 pasang nilai ekstrim yang diujikan, yakni selang beranak, bagian peternak, harga beli, dan harga jual. Berdasarkan hasi uji diketahui bahwa behavioural method dapat digunakan untuk menguji validitas model peternakan sapi perah rakyat. Model menunjukkan konsistensi yang logis sehingga dapat digunakan sebagai instrumen untuk mensimulasikan strategi pengembangan.Kata kunci: uji validitas model, peternak skala kecil, systems thinking, uji nilai ekstrim

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Section: Methodsmentioning

confidence: 99%

Using Behavioural Validity Method to Analyse the Dynamic Model of Smallholder Beef Farming Systems in Indonesia

Setianto

2016

JAP

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“…We expected that due to the use of electronic data in our study, measurement error would not be a major concern. To partially address the risks of hypotheses and specification errors, we performed behavior reproduction (Barlas, 1989), unit consistency and extreme condition tests (Sterman, 2000)-extreme conditions such as: if no one was interested in the petition, the total number of signatures should remain zero; or if everybody who received an announcement was fully interested in the petition and no one forgot to sign, the total number of signatures should not exceed the assumed initial unaware population. These tests provided some confidence in the model, but generalization beyond similar petition diffusion settings is not guaranteed.…”

Section: Confidence Intervals and Sensitivity Analysismentioning

confidence: 99%

Information diffusion through social networks: The case of an online petition

Jalali

Ashouri

Herrera-Restrepo

et al. 2016

Expert Systems with Applications

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People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers.Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have 'disproportionate' effects in increasing the total number of signatures. The model is fully documented for further development and replications.

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“…The notion of ambiguity in model identification and calibration can be valued differently [3,18]. In statistical modelling traditions, ambiguity in model calibration is typically interpreted as over-parameterisation of the model.…”

Section: Uncertainty In Energy Modelsmentioning

confidence: 99%

Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Ruijven

Sluijs

Vuuren

et al. 2009

Environ Model Assess

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Uncertainties in energy demand modelling originate from both limited understanding of the real-world system and a lack of data for model development, calibration and validation. These uncertainties allow for the development of different models, but also leave room for different calibrations of a single model. Here, an automated model calibration procedure was developed and tested for transport sector energy use modelling in the TIMER 2.0 global energy model. This model describes energy use on the basis of activity levels, structural change and autonomous and priceinduced energy efficiency improvements. We found that the model could reasonably reproduce historic data under different sets of parameter values, leading to different projections of future energy demand levels. Projected energy use for 2030 shows a range of 44-95% around the best-fit projection. Two different model interpretations of the past can generally be distinguished: (1) high useful energy intensity and major energy efficiency improvements or (2) low useful energy intensity and little efficiency improvement. Generally, the first lead to higher future energy demand levels than the second, but model and insights do not provide decisive arguments to attribute a higher likelihood to one of the alternatives. Keywords

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Multiple tests for validation of system dynamics type of simulation models

Cited by 359 publications

References 5 publications

Using Behavioural Validity Method to Analyse the Dynamic Model of Smallholder Beef Farming Systems in Indonesia

Using Behavioural Validity Method to Analyse the Dynamic Model of Smallholder Beef Farming Systems in Indonesia

Information diffusion through social networks: The case of an online petition

Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

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