Comparison of fuzzy and crisp systems via system dynamics simulation

Polat, Seçkin; Bozdag, Erhan

doi:10.1016/s0377-2217(01)00124-2

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…There have been several attempts to bridge fuzzy logic with dynamic modeling. (Levary, 1990;Tessem and Davidsen, 1994;Maeda et al, 1996;Maeda and Nobsada, 1998;Ortega et al, 2000;Polat and Bozdag, 2002;Chang et al, 2006;Kunsch and Springael, 2008;Campuzano et al, 2010). Furthermore, a number of authors adopt fuzzy logic to model human decision-making behavior within an SD model (Morgan and Ammentorp, 1994;Takahagi, 1995;Eksin et al, 2008).…”

Section: The Relevant Literaturementioning

confidence: 99%

Fuzzy rule‐based inference in system dynamics formulations

Sabounchi

Triantis

Kianmehr

et al. 2019

System Dynamics Review

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In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context. Copyright © 2020 System Dynamics Society

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Section: The Relevant Literaturementioning

confidence: 99%

Fuzzy rule‐based inference in system dynamics formulations

Sabounchi

Triantis

Kianmehr

et al. 2019

System Dynamics Review

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“…Moreover, the deployed fuzzy logic in various studies highlights its context dependency in terms of defining rules and utilizing different methods of inference and defuzzification (Kunsch and Fortemps 2004 ; Kunsch and Springael 2008 ; Liu et al 2011 ; Mutingi and Mbohwa 2012 ). Additionally, the behavior difference in crisp and fuzzy systems may vary across contexts (Mutingi and Mbohwa 2012 ; Polat and Bozdag 2002 ). In this regard, we aim to investigate the competence of different fuzzy models in modeling the joint effect of multiple linguistic variables in the SDI-development context.…”

Section: Literature Reviewmentioning

confidence: 99%

The stock-flow model of spatial data infrastructure development refined by fuzzy logic

2016

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The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average–Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

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“…There is a comparison of fuzzy and crisp decision rules in System Dynamics simulation by Polat [8], and he concluded that the superiority of one of these systems depends on the situation. But in this paper, the investigation of the two different decision making systems is in ABS.…”

Section: The Bdi Framework Imitates Human Thinking Process With Beliementioning

confidence: 99%

Comparison of crisp systems and fuzzy systems in agent-based simulation: A case study of soccer penalties

Siebers

Wagner

2013

2013 13th UK Workshop on Computational Intelligence (UKCI)

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Abstract-The Belief-Desire-Intention (BDI) software model is an example of a reasoning architecture for a bounded rational software agent. In our research we plan to expand the application of the BDI software model to the area of simulating human behaviour in social and socio-technical systems. To this effect, in this paper we explore the differences in using a classical crisp rule-based approach and a fuzzy rule-based approach for the reasoning within the BDI system. As a test case we have chosen a football penalty shootout. We have kept the case study example deliberately simple so that we can focus on the effects the different BDI implementations have on the decisions made. Our experiments highlight that the crisp system can result in unwanted "preferred" actions because of sudden leaps or drops between different ranges of decision variables, while the fuzzy system results have smoother transitions which results in more consistent decisions. The behaviour, as showcased in this simple context, underlines that a change from crisp to fuzzy rule based systems as the underlying reasoning model in BDI systems can provide the path to a superior approach for the simulation of human behaviour, which we will explore further in the future.

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Comparison of fuzzy and crisp systems via system dynamics simulation

Cited by 13 publications

References 6 publications

Fuzzy rule‐based inference in system dynamics formulations

Fuzzy rule‐based inference in system dynamics formulations

The stock-flow model of spatial data infrastructure development refined by fuzzy logic

Comparison of crisp systems and fuzzy systems in agent-based simulation: A case study of soccer penalties

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