Prediction -- The Quintessential Policy Model Validation Test

It is essential to objectively test how well policy models predict real world behavior. The method used to support this assertion involves the review of three SD policy models emphasizing the degree to which the model was able to fit the historical outcome data and how well model-predicted outcomes matched real world outcomes as they unfolded. Findings indicate that while historical model agreement is a favorable indication of model validity, the act of making predictions without knowing the actual data, and comparing these predictions to actual data, can reveal model weaknesses that might be overlooked when all of the available data is used for model development. Although this finding is based on just three cases, the value of using prediction to validate models, as recommended by leaders in the field, is compellingly demonstrated. The implication for decision makers is to be cautious about analyses made using models that have been tested only against historical data. The primary contribution is to clearly demonstrate the oft-prescribed but less often performed validation method of testing model predictions against reality

Four Decades of Systems Science Teaching and Research at PSU

Systems science (SySc) is defined, and a brief background is provided regarding some of the systems science-related societies, conferences, journals, research institutes, and educational programs. The SySc program at Portland State University in Portland, OR, USA, is described in detail, including its history, curriculum, students, faculty, and degrees granted. Dissertation topics are summarized via word diagrams created from dissertation titles over the years. MS degrees, student placement, and undergraduate courses are also mentioned, and future plans for the program are described including its support for sustainability education.https://pdxscholar.library.pdx.edu/systems_science_seminar_series/1013/thumbnail.jp

Reducing Overdose Deaths Associated with Pharmaceutical Opioid Treatment of Chronic Pain: Analyzing Interventions with a System Dynamics Model

A dramatic rise in the use pharmaceutical opioids to treat pain, and the associated opioid abuse and addiction, has created a substantial public health problem in the United States. Effective tools and interventions are needed to identify policies to reduce opioid abuse, addiction, and overdose deaths. A system dynamics model is used to identify policy interventions that will reduce the prevalence of adverse outcomes attributed to pharmaceutical opioids. Results suggest that it will be difficult to minimize negative outcomes without adversely affecting the degree to which chronic pain patients can access pharmaceutical treatment, and also indicate the importance of the metric(s) chosen for evaluating effectiveness.https://pdxscholar.library.pdx.edu/systems_science_seminar_series/1019/thumbnail.jp

Transforming Technology Management Courses for Web Delivery

This paper explores the ramifications of using web technology in teaching technology management courses, computer-based modeling and simulation courses in particular. The emphasis is on what works, but disappointments are also mentioned. Web technology is being used to supplant lectures with self-paced materials and lab exercises that enable students to take courses remotely and asynchronously. Web-based exams are also discussed

A Dynamic Model of the Opioid Drug Epidemic with Implications for Policy

Background: The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking. Objectives: Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects. Methods: The model was developed from literature review and data gathering. Its outputs, starting 1990, were compared against 12 historical time series. Illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied. Results: The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged.Conclusion: No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths

System Dynamics Implementation of a Model of Population and Resource Dynamics with Adaptation

We build and analyze a dynamic ecological economic model that incorporates endogenous innovation on input substitutability. The use of the system dynamics method allows us to depart from conventional equilibrium thinking and conduct an out-of-equilibrium (adaptation) analysis. Simulation results show that while improvement in input substitutability will expand an economy, this change alone may not improve sustainability measured by indicators such as utility-per-capita and natural resource stock. It could, however, be possible that in combination with other technological progress, improvement in input substitutability will contribute to sustainable development. Sensitivity analysis also indicates a possible complication with the use of exogenous consumer preference, which is often assumed in standard economics

Creating Clinically Useful \u3ci\u3eIn Silico\u3c/i\u3e Models of Intracranial Pressure Dynamics

To create clinically useful computer simulation models of intracranial pressure (ICP) dynamics by using prospective clinical data to estimate subject-specific physiologic parameters

A Hybrid Simulation Model for Studying Acute Inflammatory Response

The modeling of complex biological systems presents a significant challenge. Central to this challenge is striking a balance between the degree of abstraction required to facilitate analysis and understanding, and the degree of comprehensiveness required for fidelity of the model to its reference-system. It is likely necessary to utilize multiple modeling methods in order to achieve this balance. Our research created a hybrid simulation model by melding an agent-based model of acute local infection with a system dynamics model that reflects key systemic properties. The agent based model was originally developed to simulate global inflammation in response to injury or infection, and has been used to simulate clinical drug trials. The long term objective is to develop models than can be scaled up to represent organ and system level phenomena such as multiple organ failure associated with severe sepsis. The work described in this paper is an initial proof of concept of the ability to combine these two modeling methods into a hybrid model, the type of which will almost certainly be needed to accomplish the ultimate objective of comprehensive in silico research platforms

Measuring the Longitudinal Effects of Food Carbon Footprint Training on Consumers: Knowledge, Attitudes, and Behavioral Intentions

Although the supply chains through which foods are produced, processed, and transported can have a significant impact on carbon dioxide (CO2) emissions, consumers are largely unaware of how their food choices may impact the environment. Based on a previous related study, we hypothesized that a web-based training process could increase consumer knowledge and perhaps influence consumer behavior longitudinally. To test this, food distribution networks were modeled and analyzed to determine CO2 footprints for a variety of foods, and a training process was designed to teach consumers about the CO2 emissions for different types of foods that are provided either locally or transported over long distances. The training allowed users to compare alternative choices for their daily food menu. Participants from two major urban universities were given an initial knowledge survey after which they participated in the online training program including the carbon footprint of foods associated with production, preparation, transportation, and storage. Later they took a post-treatment survey regarding their knowledge and their intentions to change their purchasing behavior in selecting foods. Follow-up surveys were administered after one month and after three months. Results indicate that participants’ post-training knowledge increased and participants indicated that they intended to use the knowledge they gained to make more sustainable food choices. Additionally, participants partially retained the knowledge gained over time, maintained their intentions to change behavior, and followed through by implementing behavior change related to more sustainable food choices

A Systems Approach to Stress, Stressors and Resilience in Humans

The paper focuses on the biology of stress and resilience and their biomarkers in humans from the system science perspective. A stressor pushes the physiological system away from its baseline state towards a lower utility state. The physiological system may return towards the original state in one attractor basin but may be shifted to a state in another, lower utility attractor basin. While some physiological changes induced by stressors may benefit health, there is often a chronic wear and tear cost due to implementing changes to enable the return of the system to its baseline state and maintain itself in the high utility baseline attractor basin following repeated perturbations. This cost, also called allostatic load, is the utility reduction associated with both a change in state and with alterations in the attractor basin that affect system responses following future perturbations. This added cost can increase the time course of the return to baseline or the likelihood of moving into a different attractor basin following a perturbation. Opposite to this is the system’s resilience which influences its ability to return to the high utility attractor basin following a perturbation by increasing the likelihood and/or speed of returning to the baseline state following a stressor. This review paper is a qualitative systematic review; it covers areas most relevant for moving the stress and resilience field forward from a more quantitative and neuroscientific perspective