In the political violence scholarship, there is a gap in explaining how group-level dynamics cause mass political violence. There are several theories of why political groups become violent. Because of the qualitative nature of these theories and the feedback complexity of political violence, it is hard to test these theories against each other and against data. This paper describes an attempt to use a combination of system dynamics and agent-based modeling to create a simulation pitting rival theories of political violence against each other and against empirical data. The purpose of the research is theory testing: to see what theory or combination of theories best explains political violence. The paper provides an overview of the relevant theory and data. The paper then develops a dynamic hypothesis and a prototype hybrid Netlogo simulation of two theories, political opportunity and collective action.
Out of Stock (OOS) has long been a plaguing problem in the consumer packaged goods (CPG) industry for both manufacturers and retailers. It refers to a situation in which an item is unavailable for sale as intended at a store. One study estimated that OOS items on average cost retailers 4 percent of their annual sales, and manufacturers $23 million for every $1 billion in sales. OOS could be caused by many factors, such as manufacturer production shortage, distribution center delay, consumer demand surge, and sub-optimal store operations, etc. There have been many previous attempts to model and fix OOS. To the authors knowledge, this is the first study to address the full production-distribution system using system dynamics (SD) and help all players understand the structure of the CPG distribution system that contributes to the system behavior that causes OOS, and interventions or improvements that can be made to the system to improve its performance related to OOS events. This paper will describe the OOS model we developed, the insights we gained, and the process we used to translate the initial SD model into a business application that can be employed on a wide range of product and retailer scenarios.
Rework in construction development projects can significantly degrade project cost and schedule performance. In a typical construction development project which involves design and construction, rework in the construction phase could increase construction cost by 10%-15% of the contract price (Burati. 1992, Josephson & Hammerlund 1999, Love & Li 2000). The proportion of money and time spent on rework in the design phase is usually higher (Smith & Eppinger 1997). In large, complex projects, undiscovered rework in the design phase can induce rework in the construction phase. The time when rework is discovered during the project development process affects the impact of rework on overall project performance. However, available knowledge is not always successful in improving project managers understanding of the feedback mechanisms which drive undiscovered rework impacts on project performance, specifically the interaction between different phases during the developing process. The current work uses a system dynamics model of a two phase project development cycle to identify high leverage points for minimizing the impacts of rework on development project performance. Model analysis suggests that failing to discover rework near its creation in the project development process can magnify the impact of rework on project performance.
Large, complex construction projects subject to unique types of risks. One such unique risk is the combination of rework and increased project scope that can push a project from a behaviour mode of progress toward completion, past a tipping point, and into a behaviour mode of falling farther and farther behind. Previous research has demonstrated the potential of rework-induced tipping point dynamics to cause poor cost and schedule performance on large, complex construction projects and the effectiveness of project design strategies in mitigating tipping point risk. Previous research has also examined three project labor control policies (overtime, workforce, and work intensity) and their impact on project performance. However, the impacts of project labor controls on tipping point dynamics have not been fully investigated. The current work uses a simulation model of a construction project to investigate the ability of project labor control actions to respond to tipping point dynamics. The model demonstrates that some well intended and reasonable project labor control actions, such as the extended use of overtime, can push a project over the tipping point to failure.
This paper examines the relationship between sovereign debt dynamics and the stability of financial institutions using a system dynamics framework. The model, which builds upon the seminal work of Saeed and Parayno (1993), incorporates three heterogeneous banks, a central government and a rating agency. Further, the banks and the central government are assumed to be boundedly rational and backward looking interacting via both the local and international capital markets. The model is calibrated to conform to time-series data of Jamaicas debt-deficit dynamics and banking system performance between FY 1997/8 and FY 2003/4 and then used to perform a set of counterfactual exercises based on the impact of exogenous hypothetical shocks to the Jamaican economy four years prior to the onset of recent the global financial crisis. Accordingly, the paper proposes an early warning system for the vulnerability of banking institutions to a default on public debt. Scenario analyses, conducted using the framework, suggests that significant shocks to net international reserves and exports in 2004 would catalyze a significant fall-out of the banking sector in the near to medium term, with the country being more vulnerable to shocks to net international reserves. We close with some implications for prudential regulation.
Industrial companies increasingly rely on services to stand out from the crowd. Alongside direct strategic and economic advantages, these industrial services can also provide impulses for the further development of the producer´s good. Likewise, modifications of the material product may lead to new or advanced service offers. Hence, product modifications may lead to service adaptations, which again lead to product enhancements. Consequently, product and service innovations seem to interact, driving a dynamic loop and accelerating the innovation activities of a company. This article analyzes the causes by means of a literature review. Afterwards a system dynamics model is constructed to describe the assumed interaction system of product and service innovations in industrial companies and show first consequences resulting from this dynamic loop. Finally, the impacts of some drivers are tested, to give some insights into the behavior of this system.