Public policies often fail to achieve their intended result because of the complexity of both the environment and the policy making process. In this article, we review the benefits of using small system dynamics models to address public policy questions. First we discuss the main difficulties inherent in the public policy making process. Then, we discuss how small system dynamics models can address policy making difficulties by examining two promising examples: the first in the domain of urban planning and the second in the domain of social welfare. These examples show how small models can yield accessible, insightful lessons for policy making stemming from the endogenous and aggregate perspective of system dynamics modeling and simulation.

The academic job market has become increasingly competitive for PhD graduates. In this note, we ask the basic question of ‘Are we producing more PhDs than needed?’ We take a systems approach and offer a ‘birth rate’ perspective: professors graduate PhDs who later become professors themselves, an analogue to how a population grows. We show that the reproduction rate in academia is very high. For example, in engineering, a professor in the US graduates 7.8 new PhDs during his/her whole career on average, and only one of these graduates can replace the professor’s position. This implies that in a steady state, only 12.8% of PhD graduates can attain academic positions in the USA. The key insight is that the system in many places is saturated, far beyond capacity to absorb new PhDs in academia at the rates that they are being produced. Based on the analysis, we discuss policy implications.

Understanding how environmental factors impact COVID-19 transmission informs global containment efforts. We studied the relative risk of COVID-19 due to weather and ambient air pollution. We estimated the daily reproduction number at 3,739 global locations, controlling for the delay between infection and detection, associating those with local weather conditions and ambient air pollution. Controlling for location-specific fixed effects and local policies, we found a negative relationship between the estimated reproduction number and temperatures above 25 o C, a U-shaped relationship with outdoor ultraviolet exposure, and weaker positive associations with air pressure, wind speed, precipitation, diurnal temperature, SO 2 and ozone. We projected the relative risk of COVID-19 transmission due to environmental factors in 1,072 global cities. Our projections suggest warmer temperature and moderate outdoor ultraviolet exposure may offer a modest reduction in transmission; however, upcoming changes in weather alone will not be enough to fully contain the transmission of COVID-19.

This is a preprint version, not peer-reviewed yet, and readers should treat it accordingly. 2 Summary Background: The COVID-19 disease has turned into a global pandemic with unprecedented challenges for the global community. Understanding the state of the disease and planning for future trajectories relies heavily on data on the spread and mortality. Yet official data coming from various countries are highly unreliable: symptoms similar to common cold in majority of cases and limited screening resources and delayed testing procedures may contribute to underestimation of the burden of disease. Anecdotal and more limited data are available, but few have systematically combined those with official statistics into a coherent view of the epidemic. This study is a modeling-in-real-time of the emerging outbreak for understanding the state of the disease. Our focus is on the case of the spread of disease in Iran, as one of the epicenters of the disease in the first months of 2020. Method:We develop a simple dynamic model of the epidemic to provide a more reliable picture of the state of the disease based on existing data. Building on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework we incorporate two behavioral and logistical considerations. First we capture the endogenous changes in contact rate (average contact per person) as more death are reported. As a result the reproduction number changes endogenously in the model. Second we differentiate reported and true cases by including simple formulations for how only a fraction of cases might be diagnosed, and how that fraction changes in response to epidemic's progression. In estimating the model we use both the official data as well as the discovered infected travelers and unofficial medical community estimates and triangulate these sources to build a more complete picture. Calibration is completed by forming a likelihood function for observing the actual time series data conditional on model parameters, and conducting a Markov Chain Monte Carlo simulations. The model is used to estimate current "true" cases of infection and death. We analyze the future trajectory of the disease under six conditions related to the seasonal effects and policy measures targeting social distancing. Findings:The model closely replicates the past data but also shows the true number of cases is likely far larger. We estimate about 493,000 current infected cases (90% CI: 271K-810K) as 1 A Persian summary of the article is provided as an appendix. ‫است‬ ‫موجود‬ ‫انتها‬ ‫ضمیمه‬ ‫در‬ ‫فارسی‬ ‫به‬ ‫مقاله‬ ‫خالصه‬

The explosive increase in the number of postdocs in biomedical fields is puzzling for many science policymakers. We use our previously introduced parameter in this journal, the basic reproductive number in academia (R0), to make sense of PhD population growth in biomedical fields. Our analysis shows how R0 in biomedical fields has increased over time, and we estimate that there is approximately only one tenure-track position in the US for every 6.3 PhD graduates, which means the rest need to get jobs outside academia or stay in lower-paid temporary positions. We elaborate on the structural reasons and systemic flaws of science workforce development by discussing feedback loops, especially vicious cycles, which contribute to over-production of PhDs. We argue that the current system is unstable but with no easy solution. A way to mitigate the effects of strong reinforcing loops is full disclosure of the risks of getting PhD.