Browsing by Subject "System dynamics"

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Combining systems thinking, model-based reasoning, and project-based learning to advance student agency, increase student engagement and understanding, and provide an authentic and accurate method of assessing student competencies in a high school aquatic science course
(2013-08) Ryan, Douglas Wayne; Stroup, Walter M.; Petrosino, Anthony J
Science elective courses for high school seniors provide an opportunity to engage students in rigorous, relevant instruction that requires students to employ a broad range of science knowledge and skills from previous courses toward real world problems with relevance to students’ current and future life experiences. The goal of this work is to provide teachers of high school science courses with a methodology for the introduction of strong STEM components into traditional science courses, particularly model eliciting activities, system dynamics, and engineering based design challenges. Employing these instructional methods in an aquatic science course produced an effective, engaging curriculum that increased students understanding of science content and provided students with the tools to analyze, evaluate and design solutions to real world problems. Teaching the concept of system dynamics early in the course gave students tools, including causal loop diagrams, to create useful models for analyzing interactions in complex systems. Student creation of such models proved an effective instructional method for teaching science content and the nature of scientific processes. Students displayed the ability to apply these techniques, once taught, to a diverse set of problems and expressed an intention to continue to use these skills both personally and professionally in the future. Having students create, analyze, and discuss their own models of complex systems provided the teacher with an effective method for both formative and summative assessment of student knowledge and comprehension. The models provided a more authentic and accurate evaluation of student knowledge and understanding than a written test or multiple choice response exam alone. Student use of software modeling tools, such as STELLA, can be added to these methods, providing students with the ability to add the concepts of rate and flow to their models.
Macroscale modeling linking energy and debt : a missing linkage
(2017-06-29) Jayaswal, Harshit; King, Carey Wayne
What if we realized that the fundamental economic framework of models that are meant to guide a low-carbon energy transition prevents them from actually answering the question they are supposed to answer? Instead of assuming a series of energy investments, and then estimating the economic impacts of those choices, they actually do the exact opposite. They assume economic growth and then make a series of investments to meet emissions targets without actually factoring in how the energy systems themselves feedback to economic growth. The research here would be to try to understand how energy and resource extraction are linked with long-term economic outcomes, specifically addressing the idea of accumulation of debt in the economy. Many economic models implicitly assume that energy resources are not constraints on the economy. These energy-related constraints have to be introduced if we are to effectively understand long-term debt and natural resource interactions. Same is also true with various biophysical models which do not consider economic parameters like debt, employment and wages etc. while modeling population growth and resources in the system. The research objective is to develop a consistently merged model combining both a biophysical and an economic model to describe the industrial transition to the contemporary macroeconomic state. The research approach would be to integrate macro-scale system dynamics models of money, debt, and employment (specifically the Goodwin and Minsky models of (Keen, 1995 & Keen, 2013)) with system dynamics models of biophysical quantities (specifically population and natural resources such as in (Meadows et al., 1972, Meadows et al., 1974, Motesharrei et al., 2014)). The proposed research concept is critical to link biophysical modeling concepts with those economic models that specifically include the link of debt to employment and economic growth. This type of modeling is anticipated to help answer important questions for a low-carbon transition, for example, how does the rate of investment in “energy” feedback to growth of population, economic output, and debt; and how does the capital structure (e.g. fixed costs vs. variable costs) of fossil and renewable energy systems relate to, and affect, economic outcomes.
Risk mitigation strategies for project management, platform development and supply chain design
(2010-12) Tan, Burcu; Anderson, Edward George; Feng, Annabelle (Qi); Dyer, James S.; Parker, Geoffrey G.; Seshadri, Sridhar
This dissertation studies strategies to mitigate the risks associated with operational and strategic decisions of a firm, particularly focusing on project management, product development and procurement decisions. In the first essay we develop two simulation-based methods to evaluate risky capital investment projects that involve managerial flexibility. Many risky projects are characterized by significant demand and operational risks (such as learning curve uncertainty) that are difficult to capture by simple stochastic processes. We propose using system dynamics simulations to estimate the cash flow resulting from these projects and build upon prior work on real options valuation in the decision analysis literature to develop two valuation algorithms. In the second essay we explore the technology investment decisions for platforms in markets that exhibit cross-network effects. We focus on the trade-off firms must make between investing new product development resources to increase a platform's core performance and functionality versus investments designed to leverage the platform's cross-network effects. Abstracting from examples drawn from multiple industries, we use a strategic model to gain intuition about how to make such trade-off decisions under competition. In the third essay, we analyze the optimal procurement strategy of a firm that faces supply and demand risk. In particular, the firm can source from two unreliable suppliers with different delivery characteristics. We study the optimal order allocation policy shaped by the trade-offs between delivery leadtime, reliability and procurement cost. Further, we discuss the value of leadtime flexibility in supply risk mitigation and highlight the role of an inferior supplier in a firm's multi-sourcing strategy. The main contribution of this dissertation to the operations management literature is two-fold. First, it illustrates the role of effective risk mitigation through operational strategies of leadtime flexibility and supply diversification as well as through recognizing managerial flexibility. Second, it highlights the importance of leveraging third-party content development while making technology investment decisions for platforms in two-sided markets.