The desire to better understand the transmission of infectious disease in the real world has motivated the representation of epidemic diffusion in the context of quantitative simulation. In recent decades, both individual-based models and aggregate models (such as System Dynamics) are widely used in epidemiological modeling. This paper com-pares the difference between aggregate models and individual-based models in the context of Tuberculosis (TB) transmission, considering smoking as a risk factor. The merits and impact of capturing individual heterogeneity is examined via representing Bacillus Calmette-Gurin vaccination and reactivation in both models. The simulation results of the two models exhibit distinct discrepancies in TB incidence rate and prevalence. Results also suggest that, at the level of practical application, individual-based models offer significantly greater accuracy and easier extension, especially when representing a decreasing reactivation rate, waning of immunity and heterogeneous individual at- tributes. Another experiment sought to evaluate the impact of network structure on TB diffusion. Simulations are conducted under three widely used network topologies, namely random, scale-free and small world. The results reveal large differences between results of individual-based models and aggregate models, which further give insights into the difference between these two model types in the context of practical decision-making.
Over the next several decades, population trends sweeping across the world will challenge cultural traditions, health systems capacity, and social infrastructure. As average age of populations increase, health care needs change from acute to chronic. Of all the causes of age-related dependency, dementia presents a particular problem: the elderly with dementia have extensive care demands and, as their dementia progresses from mild and moderate to severe, institutionalization becomes more likely. Our research applies system dynamics methodology to estimate future population-level severity of dementia and the challenges of age-related dementia to family and community infrastructure.
Indonesia, through a state-owned aircraft industry named PT. Dirgantara Indonesia (PTDI), is trying to develop its national capacity in aerospace industrial technology. The strategy being thought to realize this objective is to build the aerospace supply chain industries through which the Small & Medium Enterprises (SMEs) can take a role in the global aerospace supply chain industries in the near future. As a main focus for this purpose, the Quality Management Systems (QMS) like AS 9100 has to be internalized in the SMEs; because, in reality, the Indonesian SMEs have not yet been experiencing with the quality requirement. Therefore, it is important to simulate the QMS learning process in the SMEs through an outsourcing collaboration between PTDI and SMEs. To simulate the learning process, a system dynamics model of knowledge development is constructed based on the inter-organizational learning dynamic model developed by Otto and Richardson (2004). A modification of the original model is made to accommodate an assistance mechanism for SMEs learning process in order that the QMS knowledge and experience of SMEs is adequate enough prior to the outsourcing partnership with PTDI. This study shows that the assistance is very important for SMEs those have not adequate prior knowledge and experience in QMS to increase their knowledge level.
This paper presents a soft landing model and a related control heuristic. The aim of this modeling effort is to represent the process of landing a spacecraft on the surface of a celestial body. This problem is known as the soft landing problem because crashing the spacecraft to the surface should be avoided. At the same time, long landing period necessitates extensive use of fuel, which should also be avoided. Consequently, the main goal in soft landing problem is to land the spacecraft as gently and as fast as possible. We adapted a control heuristic from the mass-spring damper model. According to the initial simulation runs, the adapted heuristic is successful in landing the spacecraft.
This paper introduces system dynamics approach to the domain of psychiatric research. We have tried to develop a computer simulation model based on theoretical findings and facts known to clinicians and looked for an answer to the problem of different cortisol reactivity between major depression and PTSD patients with respect to trauma severity, length and proposed genetically based differences in hippocampal volume. Modeling PTSD and depression in one structure is to our knowledge the first attempt to grasp these widely spread disorders with substantial societal and clinical burden. Even though the current model structure is simplified, proposed approach has a powerful predicting potential in clinical practice and social policy. Model structure and model equations are in Appendices 1 and 2.
With increasingly volatile oil prices, unprecedented US dependence on imported petroleum, and growing environmental concerns, the creation of economically sustainable markets for alternative fuel vehicles (AFVs) is vital. However most efforts to supplant the current transportation system have failed or had limited success. The diffusion of AFVs is complex, being both enabled and constrained by powerful positive feedbacks arising from scale and scope economies, experience curves, network effects and complementary assets. While such feedbacks are sometimes discussed, dominant mental models among both policy makers and academics may underestimate the strength of these feedbacks and the fact that they also operate to advantage the current dominant technology. The result has been a series of overly-optimistic forecasts for the extent and speed of diffusion for AFVs and EDVs, and insufficient investment in standards and policies to help such vehicles over the tipping point to self-sustained adoption. We describe a model we have developed a suite of behavioral dynamic, spatially disaggregated models with a broad scope and its key actors. We demonstrate, through various thought experiments, that higher oil prices, while important in speeding EDV adoption, are less effective than many expect, due to a range of compensating feedbacks that enable internal combustion-gasoline technology to adapt.
This paper examines challenges and opportunities for policy actions that transform healthy living behaviour. We examine how policies and other decisions, made by various types of actors (i.e. consumers, industry, agriculture, government, NGOs, and global institutions) evolve as they interact and collectively shape nutritious food markets over time. Such a transformation is characterized by multiple feedbacks and long-term delays, and involving disjointed public and private level interactions, produces counterintuitive behaviour. To develop an in-depth understanding of the major challenges and identify high-leverage strategies in transitioning away from low nutrition / high motivational (LN-HM)-based food system we have developed a behavioral dynamic model with a broad scope. Key actors in the models include consumers, producers, and policy-makers. In this paper we describe the model and carry out simulation experiments designed to examine barriers to self-sustaining market shifts between supply and demand factors. Collective action among producers to improve nutrition, while important in achieving nutritional change, builds up slow and is failure prone, due to a range of compensating supply and demand feedbacks. We analyze and discuss the role of cross-product category substitution, the contextual role of consumer switching dynamics, and a variety of initiatives, including those oriented around marketing and R&D. We conclude by discussing the importance of coordination and commitment across actors and model extensions.
Language dynamics in multi-language societies is a growing field of study. Most extant research focuses on the dynamics of language death in multilingual societies. However, empirically, languages form more complex patterns, including survival in local clusters. This paper lays the foundation for a model to explain the process by which dominated languages sustain themselves. The key mechanism we explore in this paper is the social-network effect that affects single or multiple language adoption. In particular we hypothesize an important role of bilangualism. To analyze this we extend existing, stylized, models that predict one single dominant language. We simulate the competition of two language groups who interact through a bilingual population. We include factors such as language status and ease of learning. The model is tested against the empirical case of Quebec from 1931 to 2006. We explore the importance of bilingual parents raising their children as bilinguals or unilinguals according to the relative attractiveness of each language. We find that this factor, while not critical in explaining qualitative patterns, is instrumental to replicate more accurate patterns. We conclude by developing a hypothesis of how spatial disaggregation of the network effects may explain the local cluster survival of dominated languages.
The stock management (SM) problem is of high relevance for a broad range of decision makers in society, business, and personal affairs. Although in some areas highly sophisticated models and control concepts have been developed, human stock management performance is lamentable. One recent explanation for this failure is offered by a stream of research, which finds evidence for widespread and persistent deficits in understanding how flows accumulate in stocks. This misunderstanding of accumulation (MoA) is proven even among well-educated adults. This research uses laboratory experiments to test the hypothesis that the better people understand accumulation, the higher is their performance in SM tasks. Correlation and univariate regression analysis show that MoA indeed contributes to explaining performance differences in stock management. However, the effect is moderate and vanishes almost completely when intelligence and economic knowledge are included as control variables in a multiple regression model. The value of this paper lies in explicitly testing the relation of MoA and SM, whose existence is widely taken for granted. Future research could explore a broader set of control variables and should increase the number of cases to allow for advanced theory testing using, for example, structural equation modelling.