Towards the algorithmic detection of archetypal structures in system dynamics

Schoenenberger, Lukas; Schmid, Alexander; Schwaninger, Markus

doi:10.1002/sdr.1526

Cited by 9 publications

(21 citation statements)

References 17 publications

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“…Due to the structural equivalence of three generic problem archetypes, Schoenenberger et al . () use only two algorithms to discover all four archetypal structures. Each archetype contains an intended‐consequence (ic) feedback loop and an unintended‐consequence (uc) feedback loop.…”

Section: Taming Model Complexitymentioning

confidence: 99%

“…A more detailed description of Wolstenholme's archetypal structures, the coding procedure, and the two detection algorithms can be found in Schoenenberger et al . ().…”

Section: Taming Model Complexitymentioning

confidence: 99%

“…In the polarity matrix P , p i , j = 1 if the causal relationship between the variables i (independent) and j (dependent) is positive, and p i , j = −1 if the causal relationship between i and j is negative. In the temporality matrix T , t i , j = 1 if i immediately affects j , t i , j = 2 if i is a stock variable, and t i , j = 4 if a change in i affects j only after a significant time delay (Schoenenberger et al ., ). The one relationship we assumed to be significantly delayed is the link between takedown rate and preventive maintenance (variables 8 and 10).…”

Section: Illustrating the Application Of Variety Filters By Means Of mentioning

confidence: 99%

“…In both cases, model behavior could be improved considerably, as policies were shaped according to a solution link. Hence ADAS is a powerful method for system dynamicists, given its diagnostic power and potential for policy design (Schoenenberger et al , ).…”

Section: Introductionmentioning

confidence: 99%

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The challenge of model complexity: improving the interpretation of large causal models through variety filters

Schoenenberger

Schmid

Ansah

et al. 2017

System Dynamics Review

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While large causal models provide detailed insights to the analysts who develop them, general users are often challenged by their complexity. Commonly, these models overwhelm the cognitive capacities of human beings. The inaccessibility of large causal models is particularly regrettable when they deliver valuable expertise and information that should be shared with other researchers and practitioners. To address this issue, we propose a set of tools—so‐called variety filters—to reduce model complexity and promote the accurate interpretation of their results. These filters encompass interpretive model partitioning, structural model partitioning and algorithmic detection of archetypal structures (ADAS). We demonstrate the efficacy of the proposed variety filters using the World3–2003 Model—a simulation model of remarkable complexity. The filters drastically attenuate the complexity while enhancing the comprehension of the model. Based on our findings, we derive implications for the use of complex models and their interpretation. Copyright © 2018 System Dynamics Society

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Section: Taming Model Complexitymentioning

confidence: 99%

“…A more detailed description of Wolstenholme's archetypal structures, the coding procedure, and the two detection algorithms can be found in Schoenenberger et al . ().…”

Section: Taming Model Complexitymentioning

confidence: 99%

Section: Illustrating the Application Of Variety Filters By Means Of mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The challenge of model complexity: improving the interpretation of large causal models through variety filters

Schoenenberger

Schmid

Ansah

et al. 2017

System Dynamics Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, their utilisation might also lead to different perceptions of behaviour, which, as a consequence, can doubt the responsibility and competence (Bureš 2015). Positive news is that the latest development tries to improve the functionality and, for instance, include the algorithmic detection of archetypal structures (Schoenenberger et al 2015).…”

Section: Value Of System Archetypes At the Organisational Levelmentioning

confidence: 99%

Application of System Archetypes in Practice: An Underutilised Pathway to Better Managerial Performance

Bureš

Racz²

2016

Journal of Business Economics and Management

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Abstract. Current dynamic business environment forces managers to apply various tools in order to improve organisational performance and effectiveness. System archetypes enable to cope with complexity and make appropriate decisions. This study is theoretically grounded in the field of economic cybernetics and system archetype analysis. It applies qualitative research on the sample of 54 managers with the high level of seniority. The aim is to reveal whether system archetypes are effectively used in practice. Two hypotheses focused on both knowledge about and application of system archetypes are tested. The results prove that there are inconsistencies tied to forms of system archetypes insight and knowledge. Moreover, there is an inadequate level of attention identified in investigated organisations. Results imply various potential research pathways that are outlined in the final section of the paper. Hence, the manuscript offers a unique insight into the current state of practical system archetypes utilisation and contributes to the explanation of the role of system archetypes in the economic cybernetics framework.

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Exploring the landscape of system dynamics archetypes: A systematic review

Sánchez

2024

Syst Res Behav Sci

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System dynamic archetypes reflect common problematic behaviours that occur in the real world. The high level of abstraction embodied in these archetypes is both promising and daunting. Identifying relevant archetypes for real‐world problems is not a straightforward task. In this work, we aim to provide a comprehensive background for understanding current knowledge on the application of system archetypes. The methodology is based on a systematic literature review. Most of the works contribute with prescriptive models that address problems in specific areas. These models generally tackle complex problems where social, environmental and political issues converge. The unit of analysis encompasses geographical units, organizations and artefacts. The steps followed in the development are described with different level of detail. Stakeholders' participation is typically integrated into the development process. Some reported problems are that archetypes could induce a hypothesis that does not include all relevant factors; some participants may have difficulty in understanding the models; confidence in results, difficulty of having data to configure all the variables; and the cost burden for small enterprises.

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Towards the algorithmic detection of archetypal structures in system dynamics

Cited by 9 publications

References 17 publications

The challenge of model complexity: improving the interpretation of large causal models through variety filters

The challenge of model complexity: improving the interpretation of large causal models through variety filters

Application of System Archetypes in Practice: An Underutilised Pathway to Better Managerial Performance

Exploring the landscape of system dynamics archetypes: A systematic review

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