IndShaker: A Knowledge-based Approach to Enhance Multi-perspective System Dynamics Analysis (original) (raw)
Related papers
Specification of dynamics for knowledge-based systems
Lecture Notes in Computer Science, 1998
During the last years, a number of formal specification languages for knowledge-based systems have been developed. Characteristic for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects: they have to provide means to specify a complex and large amount of knowledge and they have to provide means to specify the dynamic reasoning behaviour of a knowledge-based system. This paper will focus on the second aspect, which is an issue considered to be unsolved. For this purpose, we have surveyed existing approaches in related areas of research. We have taken approaches for the specification of information systems (i.e., Language for Conceptual Modelling and Troll), approaches for the specification of database updates and the dynamics of logic programs (Transaction Logic and Dynamic Database Logic), and the approach of Abstract State Machines.
Using System Dynamics for Grand Challenges: The ESDMA Approach
Systems Research and Behavioral Science, 2013
Exploratory Modeling and Analysis (EMA) is an approach that uses computational experiments to analyze complex and uncertain issues. It has been developed mainly for model-based decision support. This paper investigates the extent to which EMA is a promising approach for future oriented technology analysis (FTA). We report on three applications of EMA, using different modeling approaches, in three different technical domains. In the first case, EMA is combined with System Dynamics (SD) to study plausible dynamics for mineral and metal scarcity. The main purpose of this combination of EMA and SD is to gain insight into what kinds of surprising dynamics can occur given a variety of uncertainties and a basic understanding of the system. In the second case, EMA is combined with a hybrid model for airport performance calculations to develop an adaptive strategic plan. This case shows how one can iteratively improve a strategic plan through the identification of plausible external conditions that would cause the plan to perform poorly. In the final case, EMA is combined with an agent-based model to study transition dynamics in the electricity sector and identify crucial factors that positively and negatively affect a transition towards more sustainable functioning of the electricity sector. This paper concludes that EMA is useful for generating foresights and studying systemic and structural transformations despite the presence of a plethora of uncertainties, and for designing robust policies and plans, which are key activities of FTA.
Ontological Support to address the Multi-Dimensionality of Complex Systems Engineering Challenges
Every systems engineer knows about the six interrogatives: who, what, where, when, how, why. Engineers and technical managers also know what knowledge about each system has to for each phase of its life cycle, and that in each phase multiple team members and stakeholders are involved. The likelihood for communication break-downs and misunderstandings is already great for just one system and increases manifold in complex systems that are part of a portfolio. Semantic technologies allow for a more consistent approach to capture the expert knowledge associated with systems across their life cycles and provide an economical approach to enforce consistency and enable flexibility in domain specific engineering efforts. The key elements and enablers that ensure expert knowledge is captured and consistently composed and represented are ontologies, and rule sets, and reasoning logic. Ontologies have been identified as successful support to ensure semantic consistency. This paper evaluates what ontologies can do to successfully address the multi-dimensionality of complex systems engineering challenges and makes some recommendations how a general solution may look like.
Potential Data Collections Methods for System Dynamics Modelling: A Brief Overview
International Journal of Advanced Computer Science and Applications, 2021
System Dynamics (SD) modelling is a highly complex process. Although the SD methodology has been discussed extensively in most breakthroughs and present literature, discussions on data collection methods for SD modelling are not explained in details in most studies. To date, comprehensive descriptions of knowledge extraction for SD modelling is still scarce in the literature either. In an attempt to fill in the gap, three primary groups of data sources proposed by Forrester: (1) mental database, (2) written database and (3) numerical database, were reviewed, including the potential data collections methods for each database by taking into account the advancement of current computer and information technology. The contributions of this paper come in threefolds. First, this paper highlights the potential data sources that deserved to be acknowledged and reflected in the SD domain. Second, this paper provides insights into the appropriate mix and match of data collection methods for SD...
2014
We describe a new platform for system dynamics modeling that supports detailed and object oriented modeling while preserving attractive features of existing tools, including a completely declarative language with a graphical representation. New concepts supporting this platform include collections of entities, attributes, relationships, aggregation and allocation functions, and actions, which are presented with examples. The design facilitates modularity and collaboration, provides a more natural description of detail than arrays, and solves sparse matrix problems. It has application to both traditional system dynamics, with modular sectors, and to agent based modeling.
System Dynamics is often used to explore issues that are characterised by uncertainties. This paper discusses first of all different types of uncertainties that system dynamicists need to deal with and the tools they already use to deal with these uncertainties. From this discussion it is concluded that stand-alone System Dynamics is often not sufficient to deal with uncertainties. Then, two venues for improving the capacity of System Dynamics to deal with uncertainties are discussed, in both cases, by matching System Dynamics with other method(ologie)s: first with Multi-Attribute Multiple Criteria Decision Analysis, and finally with Exploratory Modelling.
2007
ion (including separating model from implementation) is a common technique for individual-level modeling, but is far from the only option for modelers. Procedural abstractions (and associated vectorbased data structures) are more traditional technique, and can be used to good effect, for example, in conjunction with individual-based models [41]. 2.5 Analysis/Visualization Support The main purpose of many models is to gain insight into the phenomena in the real world, and the impact of actions on those phenomena. In order to better understand a complex system in the real world, we build a model of that system that is simpler, but still often quite complex. We hope that this model will capture important aspects of behavior in the real-world, and seek to understand the causes for the overall behavior of this model.
System Dynamics and Scenario Planning: Implementation Challenges
Proceedings of the 2013 Systems Engineering and Evaluation and Testing Conference
In this paper we discuss key implementation challenges of a systems approach that combines System Dynamics, Scenario Planning and Qualitative Data Analysis methods in tackling a complex problem. We present the methods and the underlying framework. We then detail the main difficulties encountered in designing and planning the Scenario Planning workshop and how they were overcome, such as finding and involving the stakeholders and customising the process to fit within timing constraints. After presenting the results from this application, we argue that the consultants or system analysts need to engage with the stakeholders as process facilitators and not as system experts in order to gain commitment, trust and to improve information sharing. They also need be ready to adapt their tools and processes as well as their own thinking for more effective complex problem solving.
A Framework for Requirements Engineering Using System Dynamics
In many systems engineering activities the elicitation of requirements is regarded as a central activity for the efficient and effective functioning of the intended system. In recent years, the field of Requirements Engineering has received much attention and many research and practical approaches have been proposed. In this paper we present a Requirements Engineering framework that is motivated by the System Dynamics paradigm. The framework consists of four key activities: ontology modelling, goal modelling, process modelling and scenarios generation. It is our premise that the synergy between these four activities results in a robust way of working that provides requirements stakeholders with a systematic approach to articulating, defining, debating, and agreeing on the set of desirable functional and non-functional properties of the intended system. The approach is demonstrated with examples from a very large application and claims substantiated from experiences from this project.