In a previous work the philosophical concept of Mechanism was proposed in order to characterize the kind of scientific explanations provided by the system dynamics approach. That earlier paper positioned such idea epistemologically contrasting it with the traditional view used by mainstream management research and developed a different ontological ground based on the structuralist approach of Bertrand Russell. However, it seems to be needed an emphasis on the epistemological nature of the Mechanism thesis because of its common association with reductionism and the view of mechanistic thinking as supposedly opposed to holism and emergentism. This paper constitutes a second part; it underlines the epistemic status of Mechanism which explains why it is in no way opposed to ideas like holism or emergentism, on the contrary it is consistent with such worldviews. Based on this condition the paper explores the repercussions of such characterization for philosophy of science and for system dynamics by developing a research agenda for both fields around this topic.
The Swiss electricity market - as well as the European electricity markets, is now facing a period of fundamental structural changes. Emergent liberalisation is taking place, and nuclear dismantling is being debated. Given this scenario, it is important to evaluate market response to those changes in terms of security of supply and the viability of international exchanges - imports and exports of electricity from and to neighbouring countries. The approach we adopt to analyse various aspects of security of supply in Switzerland differs from the traditional economic methodology which focuses on equilibrium of outcomes as opposed to on how the new situation is reached. We use system dynamics simulation models, which incorporate information feedback and behavioural policies, to study scenarios of the transient period currently faced by the Swiss electricity market.
Many organizations set ambitious schedules for their product development projects to gain competitive advantages. However, ambitious schedules might be the main factor to deteriorate project performance. The features of system dynamics application in project management is discussed and a generic simulation model is built for studying the effects of schedule pressure and providing a helpful tool for project management to understand feedbacks, delays and nonlinear relationships among projects and how project behaviors are driven by project structures. Finally, the future works about this study are discussed.
Abstract: By means of system thinking, the issues of traveling resources, traveling condition and finance in tourism business as well as the interrelations among them were analyzed to search the effective approach to the prosperity of tourism business. The software STELLA is used to build the operation model of tourism business, including the modules of ticket price, the payment of loans and the development and demolishment of resources in order to find out an efficient way to prosper the tourism industry while protecting the environment.
There's no real data for it's just a student's exercise. It mainly analyzes the genegal idea, and every related aspects.
Also, its purpose is to show the importance of system thinking in high school students' studies.
An enterprise model is a computational representation of the structure, activities, processes, information, resources, people, behavior, goals, and constraints of an organization. It can be both descriptive and definitionalspanning the as-is and/or the to-be. The role of an enterprise model is to achieve model-driven enterprise design, analysis, and operation.
Of particular concern is an analysts ability to determine the impact of changes, and/or proposed changes, on each functional component of the enterprise. For example, how would relaxing recruiting standards affect the quality of Sailors throughout the enterprise? What is the attrition impact to the fleet if we shorten or expand time in the delayed entry program?
Upon successful completion, analysts and/or decision-makers will receive an interactive analysis system to facilitate strategic planning. The system will allow component managers to explore the impact that functional policy and resource decisions have across the entire personnel enterprise. The goal is to create a more effective personnel environment through strategic evaluations of potential futures.
The task of finding optimal policies in stochastic dynamic systems is challenging. The theory of stochastic dynamic programming (SDP) is quite complex and the available software packages are not intended for non-specialists. Furthermore, SDP is traditionally limited to quite small and well defined problems. Stochastic optimization in policy space (SOPS) seems to be an attractive alternative, particularly for people with a background in simulation of dynamic systems. However, to date no user friendly software has been available for this method. In this paper we present and demonstrate a new program package for this task. The resulting software allows the user to formulate the model in a well-known simulation program, Powersim Studio 2005. The model is automatically transferred to a standalone program. The SOPS program allows the user to reset model parameters, to specify search criteria, and to study the results of repeated searches for optimal policies. To maximise speed, Monte Carlo simulations utilise C+ code.
Firms often attempt to imitate successful practices of other firms. When implementing new practices, individuals in organizations learn new ways of doing things, develop new skills, and adopt new organizational routines. In the paper, we view implementation as a learning process and apply learning curve theory to the understanding of implementation dynamics. We extend classic learning curve to include a required output level for an individual who must choose between an old and a new way to achieve the output. Doing work the new way builds experience, increasing productivity and thus favoring continued use of the new skill, but this reinforcing process works to favor the new skill only at relatively high levels of productivity. Otherwise, the same process is a vicious cycle, driving out the new skill. We use a system dynamics model to demonstrate a mode of behavior in which learning begins and then stalls and another mode in which the new skill becomes the preferred one. We identify the tipping point between these two modes and characterize the transition problem: Learning by doing is a dynamic process, a transition from use of an old way to a new way that requires accumulating experience beyond a threshold.
The social sciences are in need of an alternative to the variance approach to causal inference, whichbecause it requires a counterfactualrestricts the claim of valid inference to conclusions drawn from experimental and quasi-experimental methods. Process theory proposes an alternative by way of demonstrating the presence of observable characteristics of the causal mechanism, a method that, while accepted in principle, has proven elusive in practice. It is suggested that system dynamics can improve the process approach, and in so doing open a path for itself to wider application in the social sciences. Educational research is suggested as a place to start, and results from two models drawn on topics from that field are offered as examples.
Both system dynamics (SD) and discrete-event simulation (DES) are used to help understand and explain puzzling real world dynamics. But what are the similarities and differences between these two approaches and which should be used in a specific circumstance? These are questions few have ventured to answer. In this research the two approaches are compared by developing an SD and DES model of the same problem situation, a fishery. An SD expert and a DES expert separately develop a model of the fishery through a number of evolutionary steps. At each step differences in the representation and interpretation of the models are identified. Overall it is apparent that while SD illuminates 'deterministic complexity', DES illuminates 'constrained randomness'. Either or both may be important in understanding and explaining puzzling dynamics. SD and DES should therefore be seen not as opposing modelling approaches, but as complementary.
I develop a System Dynamics simulation model to examine the conditions under which large firms adapt to sharp changes in the competitive landscape. This paper capitalizes on, and aims to extend the intra-organizational ecological theory of organizational adaptation, which reconciles the workings of selection and adaptation forces in the process of organizational change. The paper interprets the narrative theory from the feedback perspective, translates it into a system dynamics model, and simulates the model under different assumptions of firm characteristics. More specifically, I experimentally reproduce in vitro a firms adaptation process to closely scrutinize how the morphology of firms resource allocation mechanisms interacts with stocks of available resources to mould an unfolding adaptation path. The simulation experiments unveil the role of timing, path-dependence and self-reinforcing mechanisms in decision-making.