Static and Dynamic Aspects of Application Domains: An Inductively Defined Modeling Technique That Allows Decomposition

Weide, Th.P. van der; Tulinayo, F.P.; Bommel, P. van

doi:10.7250/csimq.2016-7.02

Cited by 4 publications

(7 citation statements)

References 20 publications

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“…For a detailed discussion see [40]. The current state of the Universe of Discourse is recorded by the corresponding ORM scheme as the population of this scheme with all valid (elementary) facts in that particular state at that moment.…”

Section: Rulementioning

confidence: 99%

“…Sofar we have extended ORM with the concepts of state and state transition [40]. However, extending ORM with state transitions is not new.…”

Section: A Continuous Approximationmentioning

confidence: 99%

“…In [41], [42] they explore the extension of ORM to support declarative specification of dynamic rules restricted to single-step transitions. The extension of ORM in [40] allowed us analyze SD model behavior at a conceptual level and provide an inductive definition by presenting a mechanism to use the information structure for describing information structure states and their relations in time. For a deeper understanding of the dynamics of the conceptual system described by the ORM model we focus on groups of objects rather than individual object instances and their transitions.…”

Section: A Continuous Approximationmentioning

confidence: 99%

“…Note that an object type may have more than one behavioral description, but this will be generally not the case in practice. Further discussion on states (decomposition and transition) is presented in [40].…”

Section: Depends(patient ω Bed)mentioning

confidence: 99%

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Decomposition and Conceptualization to Support System Dynamics Behavior Modeling

Tulinayo

Weide²,

Bommel³

2018

CSIMQ

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With the increasing need for data-based decision making, social systems and the ecosystems; practitioners and decision makers need guidance in their decision making, as is offered by data-based models and a systematic generation of simulation tools. Overtly, relations between data and practice have been under-conceptualized. Data modelers and decision makers tend to lack a mutual understanding of each other's knowledge systems which has led to huge knowledge gaps. Assimilation of modeling methods therefore is vital. Modeling methods use a specific way of thinking, rules and directions on how to model different aspects of systems. These rules and directions are what we refer to as constructs. Conceptualizing model relations and formulations requires significant domain knowledge and understanding of the constructs. In this article, we use the decomposition mechanism to better conceptualize and understand the System Dynamics (SD) model behavior, and show how using a natural language based domain modeling method (Object-Role Modeling, ORM) helps in dealing with complex SD models. Through applying the decomposition mechanism, we are able to better understand the underlying concepts of the stock and flow diagram and update behaviors of ORM objects. To achieve this, we use examples and an SD model derived from a case "Intrapartum process in Ugandan hospitals" to study the object behaviors. The main results of this article include: a theoretical founding of integrating ORM with SD; quantitative analysis at the level of ORM reasoning; and transformation rules from ORM into SD.

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Section: Rulementioning

confidence: 99%

“…Sofar we have extended ORM with the concepts of state and state transition [40]. However, extending ORM with state transitions is not new.…”

Section: A Continuous Approximationmentioning

confidence: 99%

Section: A Continuous Approximationmentioning

confidence: 99%

Section: Depends(patient ω Bed)mentioning

confidence: 99%

See 2 more Smart Citations

Decomposition and Conceptualization to Support System Dynamics Behavior Modeling

Tulinayo

Weide²,

Bommel³

2018

CSIMQ

Self Cite

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“…In this approach causal diagrams are used to develop SD models. A conceptual base normally is the result of a particular focus on the application domain [42]. The conceptual base, in terms of the conceptual model, provides the dimensions that describe the states of the (considered part of the) application domain.…”

Section: Causal Reasoningmentioning

confidence: 99%

A Linguistic-Based Systematic Approach to Complex System Dynamics and its Application to E-government Introduction in Zanzibar

Khamis¹,

Weide²

2017

CSIMQ

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System thinking has become an effective strategy when dealing with complex systems. Such systems are characterized by mutual interactions, causality and inter-dependency between system components. A typical example is the cooperation between governmental organizations and stakeholder interaction. The complexity of developing an e-government system suggests a more fundamental approach, where the roles of domain expert and system analyst are clearly separated. The main focus of this article is (1) to propose a linguistically-based systematic approach to the construction of models for the dynamics of complex systems, and (2) to propose extended causal diagrams. Our research methodology is based on Design Science. We start from a conceptual language developed for the application domain at hand and use this to define the dynamic factors. Then, we show how the resulting extended causal diagram is transformed into a framework for System Dynamics. We have demonstrated this approach by using a basic form of an e-government as a running example. Our intention is to use this approach as a basis for a systematic step-wise introduction of e-government in Zanzibar. Besides, this method is useful for modeling any complex system, especially for the description and evaluation of intended policies.

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From conceptual model to implementation model Piloting a multi-level case study in Cameroon.

Kameni¹,

Weide²,

Groot³

2019

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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This paper presents the application of a Multi-Level Agent Based Model technology through a Natural Model based Design in Context (NMDC) to describe and model a class of environ-mental problems. NMDC allow training domain expert to design a conceptual model for a concrete environmental problem. This model describes the underlying application domain in terms of environmental concepts and neither requires specific technical skills nor involves implementation details. We show how the associated TiC (Tool-in-Context) develop through NMDC can help the domain expert to describe in semi-natural (specific) language the environmental problem. This description is the basis for TiC to generate a simulation tool. On the base of this, we transform the specific language to NetLogo agent based code, thereby facilitating an early prototype application to be used by the domain expert. Finally, we applied this approach to explain and analyze the process of deforestation around the Laf Forest Reserve and discuss the prototype resulting from our approach. Cet article présente une application de l’approche de modélisation multi-échelle à base d’agent en s’appuyant sur un modèle spéciﬁque (NMDC) pour décrire et modéliser une classe de problèmes environnementaux. Le modèle NMDC permet à l’expert du domaine de décrire le domaine d’application sous-jacent en termes de concepts environnementaux et ne nécessite pas de compétences techniques spéciﬁques, ni de détails de mise en oeuvre. Nous montrons comment l’outil TiC (Tool-in-Context) associé développé à partir du modèle NMDC peut aider l’expert du domaine à décrire dans un langage naturel (spécifique) le problème environnemental. Cette description est la base pour l’outil TiC de générer un outil de simulation. A travers cet outil, nous transformons le langage spécifique en code multi-agents NetLogo, facilitant ainsi un premier prototype de l’application pouvant être utilisé par l’expert de domaine. Enfin, nous appliquons cette approche pour expliquer et analyser le processus de déforestation autour de la réserve forestière de Laf et discuter du prototype résultant de notre approche.

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Static and Dynamic Aspects of Application Domains: An Inductively Defined Modeling Technique That Allows Decomposition

Cited by 4 publications

References 20 publications

Decomposition and Conceptualization to Support System Dynamics Behavior Modeling

Decomposition and Conceptualization to Support System Dynamics Behavior Modeling

A Linguistic-Based Systematic Approach to Complex System Dynamics and its Application to E-government Introduction in Zanzibar

From conceptual model to implementation model Piloting a multi-level case study in Cameroon.

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