The number of incidents provoked by a domestic terrorist organization shows an oscillatory though irregular behaviour over time. There are periods of time where the organization carries out many incidents whereas during other periods, the activity diminishes or even is null. This paper attempts to explain the reasons of that behaviour considering a causal structure that picks up the interrelations between the actions of the organization and the government of the country where the organization focus mainly its activities. While the terrorist organization controls positive feedback loops, the governmental policies implemented to fight against it are led by negative feedback loops fraught with uncertainty. The dynamic emerging from the interrelations between the positive and the negative feedback loops would explain the evolution of the number of attacks carried by the organization. In order to check the strength of the causal structure a simulation exercise is proposed to characterize the number of incidents of a specific organization during a concrete temporal horizon. The aim is to check the degree of fit between the real data and those obtained by simulation, which includes specific features of the organization to study.
Much attention is focused on the rational-style development and application of System Dynamics models. Even group model building focuses primarily on the formulation and understanding of the model by the group members themselves. There is a dearth of attention for communication of the insights derived during the model building process to those peripherally or (un)involved in this process. In this study, the multi-actor context of model implementation is addressed explicitly. The feedback loop connecting model-derived insights and results back to the problem owners, the client and stake-holders, is explored. A number of principles for use in the communication of models are derived and the rôle of interactive learning environments as a tool in communicating model results and insights in such a multi-actor context is discussed.
This paper shows system dynamic model of labor market and labor migration in Latvia. The hypothesis of the research is that: labor migration is determined primarily by the payment level in the countries under consideration and indicator derived from it â payment difference in compared countries; also employment level, unemployment level, number of work places (market capacity) and number of vacant work places. Secondary factors influencing migration may be costs connected with labor migration, formal legal barriers of migration and personal propensity to migrate. Statistics on the Latviaâs labor market are not complete; there is also no common view of experts about determinant processes. In such circumstances forecasting of market with quantitative methods is problematic. One approach is to simulate indicators and to estimate their influence on national economy. The model has three parts: labor force expansion, allocation and migration sub models. Labor force expansion sub model is based on allocation of population in various categories during transition to a working age population. Allocation by level of educational is further used in labor markets analysis, where, according to the education level, worker groups are formed. Mutual interaction of groups of workers together with labor migration is represented in paper. In results is shown model factors sensitivity from personal propensity to labor migration.
This paper reports on an experimental study testing the relative effect of using simulation models on systems thinking in a college-level Introduction to Environmental Science class. The preliminary findings show mixed results. It is unclear whether this is a result on the systems simulations used in the interventions or the assessment techniques employed to study their effectiveness.
In a study done by Saeed and Pavlov a generic microstructure of resource competition was developed and stylized using the dynastic cycles that occurred throughout Chinese history. The result was a model that demonstrated how economic drivers contribute to the cycles observed in the rise and fall of dynasties and lawlessness. Using their structure, with only a few substitutions of names, the same model suitably describes numerous systems where similar cycles in resource levels can be observed. Yet, in some systems, such as gangs, the economic motivations alone do not adequately describe the social factors clearly evident in rise and fall of criminal behavior attributed to gangs. This paper explores the social influence gaps in the purely economic model, identifies a social structure that can be used instead of the economic mechanisms, and then examines implications of a model that combines both aspects of the system. The result of this research indicates that both economic and social influences are capable of producing cycles and when combined, only further exacerbate the problem. These findings have import implications on policy design, suggesting that solutions will need to simultaneously consider both aspects.
Sastry's (1997) simulation model of Tushman and Romanelli's (1985) classic theory of punctuated organizational change supported the underlying causal theory and yielded several important insights regarding executive management's role in monitoring the strategic fit with the environment and allowing for a trial period directly after reorientation. However, Sastry's model focuses exclusively on reactive strategic reorientations triggered by sustained poor performance due to organization-environment misalignment, leaving no room for proactive strategic shifts in response to anticipated events. The extremely common process of strategic planning is geared toward just this type of change; routine planning attempts to manage uncertainty, anticipate future demands, and make targeted strategic changes before performance deficits make radical reorientation necessary. This paper explores the impact of adding a strategic planning routine to Sastry's model on organizational performance and change.
A system dynamics model is developed to describe how insurgency groups pursue funding for their operations and the choices they make in how they allocate these funds to maintain their operations and advance their causes. The model illustrates that the insurgent groups, under survival pressure, will seek necessary resources to continue their operations by any means necessary regardless of ideology or higher goals. This self-preservation hypothesis is predicated on evidence-based counter-insurgency research. The model focuses on four primary activities of the insurgency: force maintenance, public relations, commission of violent acts, and community outreach. The model shows how decisions to re-allocate resources among these four activities, affects the overall financial well-being of the insurgency. Indeed, the model can be used to determine the pressure points of an insurgency which may provide insight in how to financially damage such an organization.
Since 2005, there has been an opportunity for joint reflection about the quality of the peer review process at each conference. In the subsequent conferences, discussin became more structured and this year, a substantial effort has been made. Still there remains work to be done in order to arrive at a policy that would achieve a satisfactory balance between paper and presentation quality on one side and other goals of the conference on the other. This year's meeting is the opportunity to discuss a set of indicators to express the presentation and paper quality perceived by attendees; also we can assess up to which point the new policies have yielded improvements. Reviewers, chairs and organizers are specially invited to give their input.
Some large system dynamics models drive simulator interfaces used for teaching; this is the case of the MacroLab model. Such a model may be useful for making students with basic instruction in system dynamics explore the economy as a dynamic system, allowing for diverse inquiry itineraries. The question is if different exploration itineraries yield sufficiently similar learning outcomes. This has been tried with ten student groups. The results are encouraging, but also indicate that the inquiry scenario design should be based on systematic analysis of the modelâs structure: some variables may not be reachable from everywhere. An ad-hoc structure exploration found such isolated areas. The use of a reachability matrix is suggested and an initial example is shown. Also, students need systematic guidance in constructing a loop set that will frame their exploration. Concluding, it is argued that this kind of instructional design may bring other large system dynamics models closer to instructional use.