Education as a Complex System

It was a cold rainy day in Cambridge, Massachusetts. Navid was working in his office at E40, one of MIT ’s oldest buildings. E40 used to be a factory in the 1930s and was now hosting a large number of students, researchers, visitors, and faculty members. As a postdoctoral researcher, Navid was sharing an office with another postdoc, a long-time friend. Like many other researchers in temporary positions, they were both looking for tenure-track academic jobs. Navid and his friend had different training and areas of interest, but they shared an opinion on the job outlook: it was not a seller’s market. Each job opening received hundreds of applica- tions, and it was very difficult to compete. Standing near the window and sipping from his cup of coffee, Navid heard Professor Dick Larson knock on the door. Dick was Navid’s supervisor.National Institutes of Health (U.S.) (Grant 2U01GM094141-05

A Simulation-Based Analysis of PTSD Prevalence among US Military Personnel and Veterans in 2025

We developed and simulated a systems model of the population of military personnel and veterans affected by post-traumatic stress disorder (PTSD). Simulation results fit the historical data on PTSD prevalence in 2000-2014, and forecast the trends for the next decade under several scenarios of US involvement in future wars. Using the model, we tested the effects on PTSD prevalence and healthcare costs of four PTSD policies aimed at improving: 1) resiliency, 2) screening, 3) treatment, and 4) a combination of the three. Results showed that in a postwar period, there is no silver bullet for overcoming the problems of PTSD, and screening and treatment policies must be revolutionized to have any noticeable effect. One critical characteristic of this system is the long time that it takes, about 40 years, to vanquish the psychiatric consequences of a war. In a very optimistic scenario, estimated PTSD prevalence among veterans in 2025 will be at least 10%.Department of Defens

The Coming Hangover: Magnified Effects of Sequestration on Research Enterprises

As of March 1, 2013 the US government is taking an $85 billion budget cut. Also referred as the “sequestration”, this automatic spending cut policy might continue for several upcoming years and potentially affect many industries, including the research enterprise. The cut is expected to reflect in the budget of federal agencies that support research activities, such as the National Institutes of Health (NIH) and the National Science Foundation (NSF). For a wide range of structural reasons, discussed in this commentary, the impacts of the budget cut on research enterprises can be magnified

Hiring College Graduates to Flip Hamburgers: An Endogenous Theory of Professionalization

In this paper, we offer an endogenous theory of professionalization and ever-higher degree attainment. We theorize that higher education is a self-driving growth engine. We introduce two endogenous mechanisms that act on the education enterprise, causing the number of educated people to increase dramatically with relatively short-term changes in the job market. Using an illustrative dynamic model based on simple rules of degree attainment and job selection, we argue that these self-driving growth engines are adequate to over-incentivize degree attainment, and can affect the match between supply and demand for college-educated labor. We also show that the mechanisms magnify effects of short-term recessions or technological changes, and create long-term waves of mismatch between workforce and jobs. The implication of the theory is degree inflation, magnified pressures on those with lower degrees, underemployment, and job market mismatch and inefficiency

Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection

Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms for this task utilize a set of Finite Impulse Response (FIR) band-pass filters as a front-end followed by a Convolutional Neural Network (CNN) model. In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable. Different initialization strategies for the learnable filters, including random parameters and a set of predefined FIR filter-bank coefficients, are examined. Using the proposed tConv layers, we add constraints to the learnable FIR filters to ensure linear and zero phase responses. Experimental evaluations are performed on a balanced 4-fold cross-validation task prepared using the PhysioNet/CinC 2016 dataset. Results demonstrate that the proposed models yield superior performance compared to the state-of-the-art system, while the linear phase FIR filterbank method provides an absolute improvement of 9.54% over the baseline in terms of an overall accuracy metric.Comment: 4 pages, 6 figures, IEEE International Engineering in Medicine and Biology Conference (EMBC

Epidemic Modeling with Generative Agents

This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various simulation experiments, we present compelling evidence that generative agents mimic real-world behaviors such as quarantining when sick and self-isolation when cases rise. Collectively, the agents demonstrate patterns akin to multiple waves observed in recent pandemics followed by an endemic period. Moreover, the agents successfully flatten the epidemic curve. This study creates potential to improve dynamic system modeling by offering a way to represent human brain, reasoning, and decision making

Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence

We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful diffusion models that include realistic human reasoning and decision-making

A Dynamic Model of Post-Traumatic Stress Disorder for Military Personnel and Veterans

Post-traumatic stress disorder (PTSD) stands out as a major mental illness; however, little is known about effective policies for mitigating the problem. The importance and complexity of PTSD raise critical questions: What are the trends in the population of PTSD patients among military personnel and veterans in the postwar era? What policies can help mitigate PTSD? To address these questions, we developed a system dynamics simulation model of the population of military personnel and veterans affected by PTSD. The model includes both military personnel and veterans in a “system of systems.” This is a novel aspect of our model, since many policies implemented at the military level will potentially influence (and may have side effects on) veterans and the Department of Veterans Affairs. The model is first validated by replicating the historical data on PTSD prevalence among military personnel and veterans from 2000 to 2014 (datasets from the Department of Defense, the Institute of Medicine, the Department of Veterans Affairs, and other sources). The model is then used for health policy analysis. Our results show that, in an optimistic scenario based on the status quo of deployment to intense/combat zones, estimated PTSD prevalence among veterans will be at least 10% during the next decade. The model postulates that during wars, resiliency-related policies are the most effective for decreasing PTSD. In a postwar period, current health policy interventions (e.g., screening and treatment) have marginal effects on mitigating the problem of PTSD, that is, the current screening and treatment policies must be revolutionized to have any noticeable effect. Furthermore, the simulation results show that it takes a long time, on the order of 40 years, to mitigate the psychiatric consequences of a war. Policy and financial implications of the findings are discussed.United States. Dept. of Defense. Office of the Assistant Secretary of Defense for Health Affairs (W81XWH-12-0016