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Cancer Prevention and Control with System Dynamics

The webinar titled “Cancer Prevention and Control with System Dynamics” presented a systematic review focused on the use of System Dynamics modeling in the field of cancer research. The main objective was to evaluate how this methodology is applied across various studies to address complex issues in cancer prevention and control, including treatments, risk assessments, and intervention strategies.

System Dynamics Modeling for Cancer Prevention and Control: A Systematic Review

Key insights from the webinar included:

  1. Application of System Dynamics: The review detailed how both simulation models and causal-loop diagrams are utilized to study the dynamics of cancer-related issues, ranging from chemotherapy effectiveness to the impacts of environmental contaminants on cancer risks.
  2. Quality Assessment: The studies were assessed for quality based on criteria like clarity of objectives, adequacy of information sources, and the involvement of stakeholders. This highlighted a need for more rigorous standards in modeling to enhance reliability and applicability.
  3. Focus Areas and Interventions: The research covered diverse topics such as the effectiveness of cancer treatments, prevention through behavioral changes, and early detection techniques. It also underscored the importance of System Dynamics in modeling interventions like tobacco use reduction and vaccination strategies.
  4. Recommendations for Improvement: The presentation stressed the necessity for greater transparency and rigor in System Dynamics studies within cancer research. It called for the development of supportive infrastructures and best practices to foster multidisciplinary collaborations.

The presenters, Erin Kenzie and Wayne Wakeland, through their extensive backgrounds in systems science and health policy, emphasized the potential of System Dynamics to offer comprehensive insights and effective solutions in cancer prevention and control.

For those interested in exploring innovative methodologies and their practical applications in addressing complex health issues, watching the recording of this webinar is highly recommended. It promises valuable learnings in System Dynamics and its significant role in advancing cancer research.

Watch the recording below

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PRESENTERS

Erin Kenzie is an Assistant Professor at the OHSU-PSU School of Public Health at Oregon Health and Science University in Portland, Oregon and holds faculty roles at the Portland State University System Science Program and the Oregon Rural Practice-based Research Network. She received her PhD in Systems Science from PSU in 2021. Dr. Kenzie’s research spans system dynamics, implementation science, and public health. She has been involved in research applying System Dynamics to colorectal cancer screening, behavioral health system capacity, unhealthy alcohol use screening and treatment, rural Veteran access to care, health plan-clinic partnerships, behavioral health integration, traumatic brain injury recovery, and climate change mitigation behavior.

Wayne Wakeland is Professor Emeritus of Systems Science at Portland State University. He also served as the Systems Science Program Chair for many years. He earned a B.S. and a Master of Engineering at Harvey Mudd College (1973); and a Ph.D. in Systems Science at Portland State U. (1977). He developed and taught courses on computer simulation methods and more recently a course on system sustainability and organizational resilience. His research focused on the use of computational models for studying a variety of topics, including complications during human pregnancy, recovery from concussion, and policies to reduce opioid drug diversion, abuse, and overdose deaths. Other topics included environmental/ecological sustainability and elevated intracranial pressure due to traumatic brain injusy. He has been active in the System Dynamics Society for many years and helps lead its Health Policy Special Interest Group.

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Fast-Track Cities Uses System Dynamics to Enhance HIV Care

EXECUTIVE Summary

  • Low levels of viral suppression at 69% for people with HIV make it hard to believe the 95% target level will be achieved by 2030 in St. Louis, USA.

  • As a solution, Fast-Track Cities-STL opted to use group model building means to 1) gain understanding of the fragmented service landscape and 2) to empower the community to address causes of health inequality.

  • The causal loop diagram revealed the importance of community-building for care engagement and created the foundation to build capacity by generating interest and commitment among participants. It also shed light on policies’ unintended consequences leading to service fragmentation and the need for rapid start programs to consider holistic social support for sustained care.

#Fast-Track Cities #HIV #Health #USA

The Problem

Levels of viral suppression at 69% for people living with HIV in St. Louis are far below the targeted goal of 95% required to end the HIV Epidemic in the City of St. Louis, U.S., by 2030.

To increase viral suppression, Fast-Track Cities- St. Louis created a subcommittee dedicated to expand access to rapid initiation of antiretroviral (ART) medication (also called Rapid Start) and to utilize a collaborative governance model to engage in consensus-oriented change. This initiative organized a series of group model building sessions, bringing together diverse stakeholders: those living with HIV, patient advocates, healthcare professionals, researchers, and policymakers. The objective of these sessions was threefold:

    1. To identify structural barriers affecting the adoption and accessibility of Rapid Start services in St. Louis.
    2.
To comprehend the varying perceptions and mental models of providers and clients concerning HIV diagnosis and treatment.
    3. To guide a standardized approach for implementing Rapid Start ART across different service providers.

Figure 1 – Behavior-over-time graph displaying the percentage of virally suppressed people receiving ART medication in the St. Louis region according to different outcome perceptions.

The Solution

The landscape of Rapid Start in St. Louis remains intricate. Despite three major healthcare providers rolling out rapid start programs, the data and insights from these initiatives have remained compartmentalized. Recognising this challenge, Fast-Track Cities-STL was compelled by the group model building approach as it offered an opportunity to map both the service provision landscape as well as important factors impacting quality HIV service while building capacity among communities. Fast-Track Cities-STL finds it incredibly important to utilize empowerment approaches to build a more agile, responsive public health HIV response.

Hence, the aim of Fast-Track Cities-STL was to use participatory group model building not only as means to uncover a greater understanding about the system of access to rapid start ART in the St. Louis region, but also to lay the groundwork for a grassroots community led collaborative in HIV care that aims at addressing the root causes of health inequities and outcomes. Fast-track Cities-STL anticipated that an interactive approach like group model building could help generate interest and community commitment in working on the solutions.

The resulting model combined insights from participatory GMB sessions involving those living with HIV in St. Louis, healthcare providers, and advocacy groups. The Causal Loop Diagram (CLD) crafted from this process comprised three reinforcing loops and eight feedback loops. Factors like mistrust, personal agency over health, peer support, and access to pivotal services emerged as the most influential determinants in the model’s behavior.

Figure 2 – The resulting CLD based on the GBM sessions.

Three fundamental feedback loops are highlighted. The “Problem of Delays” is a reinforcing loop where waiting for healthcare coverage causes individuals to lose their sense of control over their health. This diminished sense of control subsequently leads to decreased engagement within care spaces. Essentially, the longer the wait, the less control individuals feel, leading to even lesser engagement in care, creating a reinforcing cycle.

In the “Mistrust” balancing loop, experiences of racism and interpersonal stigma within the care system amplify mistrust. This heightened mistrust deters individuals from engaging with care, particularly in “non-peer-led groups.” As a result, they face a reduced risk of encountering cultural incompetency from providers. This loop underscores a critical insight: individuals who face racism and interpersonal stigma in care settings are more likely to disengage due to these negative experiences, balancing out their exposure to potential further harm.

The “Operation in Silos” balancing loop highlights how navigating care within bureaucratic silos makes individuals feel dehumanized, akin to “feeling like cattle.” This sentiment fosters greater mistrust, leading to decreased engagement in care. The more compartmentalized the care, the more individuals feel like mere numbers, leading to a balancing effect where they trust and engage less with the system.

Outcomes

The tangible outcome of the project was the CLD that participants developed and validated during the participatory group model building sessions. Key insights from the model revealed the importance of community-building opportunities for engagement in care, especially in the uptake of Rapid Start ART. The model also shed light on how some well-intentioned policies inadvertently led to service fragmentation and undermined the autonomy and peer support vital for those living with HIV. Furthermore, the model highlighted that the factors influencing the initiation of medication were intrinsically linked to the continuity of care. In essence, the model underscored the need for rapid start programs to holistically consider the social scaffolding essential for individuals to initiate and sustain care.

As aimed by the project, the Group Model Building approach also provided means to build capacity among communities disproportionately impacted by HIV and leverage their insights for system change. On the one hand, the insights offered by the model pointed out the importance of building community to generate engagement in care—including the uptake of Rapid Start ART.  On the other hand, the project stimulated interest among participants and recruited them to continue their engagement with the organization; several individuals expressed a commitment to continue working on the project beyond the exercise.

Regrettably, a major setback led to a leadership vacuum, halting the initiative. Nonetheless, a participating organization has taken the baton, advancing the rapid initiation of ART services.

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System Dynamics Unravels ICU Tensions at the Portuguese Oncology Institute

EXECUTIVE Summary

  • The Portuguese Oncology Institute (IPO) faced a critical challenge in its intensive care unit (ICU) where doctors and nurses experienced high turnover due to tensions with surgeons and limited resources, ultimately leading to a high death rate. Surgeons accused ICU staff of holding patients longer than necessary, leading to a backlog in surgeries. Despite utilizing external ICUs, the issue persisted.

  • Multicriteria and optimization approaches did not provide a solution. Only a System Dynamics approach using a management flight simulator enabled IPO’s leadership to understand the dynamics and discuss the causes and leverage points around the problem.

  • Based on the System Dynamics model, the IPO established 10 new beds in the appropriate care units leading to the resolution of tensions and decreased turnover and death rates in the ICU.

#IPO #PatientCare #Oncology #Health #Portugal

The Problem

Despite the demanding nature of the work in IPO’s intensive care unit (ICU), doctors and nurses were passionate about their roles. However, the turnover rate was high due to strained relationships with their colleagues. Surgeons, who would only schedule delicate surgeries when the ICU had availability, accused the ICU staff of unnecessarily prolonging patient stays and not vacating beds promptly. As a result, the ICU became a bottleneck, leading to a growing waiting list for surgeries. To address the issue, management began utilizing external ICUs, but this did not alleviate the resentment or reduce turnover. Figure 1 displays the growth of the quitting rate of the IPO’s ICU waiting list, which is the rate at which patients leave the waiting list without being admitted to the ICU for surgery. 

Figure 1 – Cumulative distribution of waiting list quitting rate

The ICU faced a high death rate, and care sharply declined when patients left for general wards, as readmission was rare due to bed shortages. To prevent readmission or premature deaths, the ICU kept patients until they were stable for general wards. Surgeons hesitated to operate on fragile cancer patients without available ICU beds. Despite being undersized, the ICU’s high cost per bed (equipment and staff) deterred expansion. IPO’s management hesitated to invest in ICU or intermediate care, as adjacent wards needed more beds for patients awaiting surgery. This compromised the organization’s performance.

The Solution

By employing the System Dynamics approach, stakeholders in the IPO’s ICU, including doctors, nurses, surgeons, and management, were able to gain valuable insights into the intricate interactions and dilemmas that existed. The approach provided a platform to examine the underlying causes of the tensions, identify the systemic drivers contributing to high turnover, and uncover the unintended consequences of certain actions. Figure 2 shows the causal loop diagram (CLD) that was developed along with the medical team and used to discuss the uses of a management flight simulator.

Figure 2 – CLD used for discussion with medical team

Through the use of the management flight simulator, stakeholders were able to witness the unfolding dynamics of the problem. This new understanding enabled IPO’s leadership to make informed decisions and take proactive steps to address the challenges. Figure 3 shows the core components of the System Dynamics model underlying the management flight simulator.

Figure 3 – Simulator core, showing the waiting list at the bottom

Outcomes

The application of the model had a significant impact on IPO’s management. As a result, a new Intensive and Intermediate Care Unit was established, reducing reliance on external ICUs and improving resource management. The ICU’s limited capacity and absence of intermediate care beds had previously led to high death rates and compromised patient care during the transition to general wards. Additionally, the practice of retaining patients in the ICU until they were fit for general wards caused delays in surgeries and increased costs.

With the opening of the new unit, equipped with 6 intensive care beds, 4 intermediate care beds, and 2 isolation rooms, these issues were effectively addressed. IPO’’s dependence on external ICUs was significantly reduced, leading to improved resource allocation within the organization. The expanded capacity and inclusion of intermediate care beds ensured smoother patient transitions and better continuity of care. Timely scheduling of surgeries enhanced efficiency and reduced costs while curbing death rates. The establishment of the Intensive and Intermediate Care Unit demonstrated IPO’s commitment to improving patient outcomes, resulting in a positive impact on both patient care and organizational performance.

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Achieving a Polio-Free World Through System Dynamics Simulation

EXECUTIVE Summary

This System Dynamics model underpinned a 192 country resolution to eradicate polio globally and led the Bill and Melinda Gates Foundation to give Rotary International $100 million to fund the polio eradication effort.

The model made a case for continued funding policy eradication by providing compelling evidence that polio outbreaks will cost more than continued intense vaccination. While the reduction in the incidence of cases was making it look like the cost of immunization was exceeding its benefits, this application of System Dynamics shows that dealing with ongoing, long-term sporadic outbreaks resulting from stopping or slowing down immunization programs is even more costly than dealing with sporadic outbreaks.  The process resulted in a simulation model that estimates the costs of two alternative policies. Option 1 was to continue efforts to eradicate polio and option 2 was to reduce the immunization rate and deal with sporadic outbreaks.

This analysis came at a critical time. In February 2007, the WHO Director-General, Dr. Margaret Chan, convened an urgent stakeholder consultation to discuss the option of switching from eradication to control. Clearly showing the dynamics and giving the wavering commitment a name helped key stakeholders appreciate the options quantitatively and with a much longer time horizon. Since then, efforts have continued to focus on finding the resources needed for complete eradication and on dealing with the other complex challenges that remain. With the support of the simulation model, national and global health leaders and financial supporters re-committed to completing eradication, which led to several hundreds of millions of dollars of resources.

#Polio #WHO #Vaccination #Health

The Problem

Following the successful eradication of smallpox and impressive progress in the elimination of polio in the Americas, in 1988 the World Health Assembly committed to the global eradication of wild polioviruses by the year 2000. By 2000, the Global Polio Eradication Initiative (GPEI) had significantly reduced the global circulation of wild polioviruses. However, in 2002–3, faced with insufficient funding to continue intense vaccination everywhere, the GPEI focused its vaccination efforts. At the time, wild polioviruses continued to circulate in six countries, but many other countries remained vulnerable to importation. Political and logistical challenges led to outbreaks and exportations, and between 2004 and 2006 wild polioviruses appeared again in previously polio-free African and Asian countries.

Toward the end of 2005, a debate began about abandoning the goal of eradication. How could the world continue to justify the significant use of resources (both financial and human) on polio, particularly with the number of cases globally already so low and so many other disease control and health services programs in need of resources?

The Solution

The dynamic disease outbreak model represents a more complicated version of the standard SIR model used in a popular System Dynamics textbook (Sterman, 2000). However, in Polio, we must deal with different types of imperfect immunity (i.e., from historic or recent exposure to polioviruses – including the oral poliovirus vaccine and/or vaccination with the inactivated poliovirus vaccine – as well as a latent period and routine or supplemental immunization rates). Modifying and expanding our existing model allowed us to determine that it was not possible to “effectively control” (i.e., achieve low cases) at a low cost. This means that control either implies high costs and low cases, or low costs and high cases, but not low costs and low cases.

Stock an Flow Diagram

However, our most significant insight came from exploring the dynamics of the economic investment in eradication. After watching the GPEI deal with the reintroductions of wild polioviruses in previously polio-free countries between 2004 and 2006, we recognized that reducing vaccination led the stock of susceptible individuals to build up and ultimately to outbreaks after some delay. Responding to the outbreaks requires reinvesting in intensive vaccination, which after some delay contains the outbreak and reduces or eliminates the circulation of the virus. With success comes a perception that the high level of investment compared to the low incidence is no longer justified. If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.

“If policymakers succumb to the resulting pressure to reduce vaccination spending, this creates a situation in which populations again become vulnerable to new outbreaks.”

To capture this behavior, we constructed the negative feedback loop shown here, which we called  “wavering”. We incorporated this feedback loop into our dynamic disease model and tailored the model to two populous northern Indian states in which wild poliovirus still circulates. We explored two options: (1) vaccinate intensively until eradication;  and (2) vaccinate intensively only if the costs per incident case remain below a certain acceptable level, but reduce the vaccination intensity otherwise  (i.e.,  a “control” option with the possibility of wavering).

Causal Loop Diagram Polio
Simulation Chart Polio

Outcomes

This application of System Dynamics highlights the systemic causes of overruns and emphasizes the importance of understanding the complex physical and social systems within which large projects operate. We, fortunately, saw the wavering commitment loop when no one else seemed to see it, and we went beyond just seeing the loop to build and use a model that provided answers to critical questions at the time the decision makers could use them (and needed them and asked us). In the presentation to the stakeholders, we showed the results to tell the dynamic story in the simplest possible way (i.e., by comparing a firm commitment to a wavering commitment showing the cumulative costs and cases).

We did not focus on explaining the model itself to attempt to walk the decision makers through the equations or diagrams. Instead, we focused on communicating the key insights based on what they already knew (e.g., the 2002–3 reduction in vaccination led to big outbreaks and high costs). However, we anticipated and received (as anticipated) some criticism from economists who did not recognize in the model a traditional health economic analysis, but these were relatively limited.

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System Dynamics Helps Reduce Waiting Lines for NHS Patients

System Dynamics Real-World Examples and Case Studies

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Looking for a Systems Consultant? Fill out this form and we will follow up with a call and, soon enough, the right person for the job. Meet our Executive Director, Rebecca Niles, to swiftly identify how to meet your needs. We can introduce Systems Thinking to your organization through renowned exercises like the Beer Game.

Nov 2021

by Vanessa Perez Perez

EXECUTIVE Summary

The impact of COVID-19 on the National Health Service (NHS) in England, led to growing record waiting lists, deterioration of patients and recovery of elective care became the number one priority.

Creative solutions were needed to tackle the elective recovery backlog, however, the health service in Norfolk and Waveney in the East of England were constrained by limited resources. System Dynamics allowed operational and clinical staff to test the impact of major interventions, aiding decisions for leaders in the system about where best to allocate resources and transform services to benefit patients and reduce waiting times.

The modelling exercise explored ways to address backlogs in the Musculoskeletal pathway with the aim of returning them to sustainable levels while providing good outcomes for patients. The insights provided by the model considered not only capacity restrictions but also the final outcomes for patients.

The model starts from the population and incidence of activity, and through the community (including GP referrals, diagnostics, community physiotherapy), before then moving through into secondary care. Moving into secondary care it covers first appointment, follow up, waiting lists, beds, and incorporates how trauma impacts on elective waiting lists while considering constraints in theatres capacity and community services.

#England #NHS #Health #Waiting lists #Musculoskeletal

The Problem

Integrated care systems (ICSs) are partnerships of organisations that come together to plan and deliver joined up health and care services, and to improve the lives of people who live and work in their area. Norfolk and Waveney Integrated Care System (ICS) in England had a number of challenges across the health system which comprises three acute hospitals, two community providers, and a mental health trust. Prior to COVID-19, elective care across Norfolk and Waveney ICS was already seeing increases in waiting lists and it was not meeting national standards with some patients waiting over 52 weeks for planned care. COVID-19 then saw the postponement of the elective care which further impacted the situation negatively, leading to one of the biggest cohorts of 104 week waits in England. 

To tackle the challenges across the whole system, clinical and non-clinical colleagues came together with the support of NHS England and Whole Systems Partnership, to develop three System Dynamics models in Stella Architect starting with the Trauma and Orthopaedics speciality within the Musculoskeletal (MSK) pathway and then also models for the Dermatology and Eye Care pathways. The models were used to test different scenarios and identify how healthcare operational interventions designed to reduce the elective care backlog impact patients across the Norfolk & Waveney ICS system.

The Solution

For the MSK pathway model, a group of analysts across the system first engaged with clinical and operational colleagues in stakeholder workshops identifying the key issue, exploring what-if questions, and developing a high-level conceptualisation of the model.

The model was developed using a modular approach by building separate parts of it first and then bringing the various components together. A user interface was created to support operational colleagues to understand the outputs of the model and enable decision-making. Operational colleagues used the interface to interactively adjust variables such as expected demand and see impact projections on capacity and waiting lists.

The model was able to test the following ‘what if’ scenarios:

What if…

 …we provided open access to early self-referral and self-management including advice on holistic/lifestyle choices to improve ultimate outcomes and minimize the impact of delays caused directly or indirectly by the COVID epidemic?

…we deployed a multi-disciplinary team Advanced Clinical Practitioners (ACP) to triage long-wait patients and fast track those in greatest need, possibly to other providers, facilitating this with integrated IT systems?

…we eliminated interruptions to the capacity and therefore flow along the elective pathway from trauma or unscheduled care requirements, i.e. protected capacity?

MSK diagram 1

Once the high level model conceptualisation was agreed upon, a prototype model was developed by analysts across the system, who had begun to receive technical training in using the software. Analysts from across the system each took a different part of the system to model and once these were completed, the different parts were brought together to create the complete system model. The stock and flow diagram below shows main components in the model. Please note that the actual model is more complex than the one illustrated in the diagram.

SFD MSK

The user interface was accessible through a link on the internet, which allowed password holders to use and run the model.

Outcomes

There were many significant benefits and impact for doing this work including enabling high quality operational decision making around the elective recovery. This work helped to develop cohesion within the ICS, improving working relationships between analysts, clinicians, and operational staff across all of the stakeholders in the region.

 Here are some highlighted benefits and impact of creating a system model in Norfolk and Waveney:

> Different impacts of scenarios could be understood and visualised through the model

> End users are now testing their own what-if questions, and understanding how changes to parts of the pathway will impact other parts of the system

> An iterative process captured a multiplicity of voices with real life experiences of the service

> This project complemented existing quality improvement and elective recovery work happening in Norfolk and Waveney ICS

> Staff shifted from silo working to genuine system working

> More conversations are now happening about the harmonisations or pathways, the locations of diagnostic assessment centres and community hubs.

> This approach is empowering discussions with Integrated Care Boards at a system level

> Analysts are now equipped with technical skills in order to conduct system modelling

> Analysts have developed much closer relationships with clinicians.

learn more

Model
Please note that this is an example model, it is not validated and it does not contain genuine data – it should be used to understand what the tool looks like and should not be used operationally.

Connect with Vanessa Perez Perez

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Using System Dynamics to Teach and Learn about COVID-19

Using System Dynamics to Teach and Learn about COVID-19

This Webinar is free due to the generous contribution of the University at Albany and California State University, Chico

A distinguished team of panelists demonstrated how we can all think globally and act locally on the most challenging topics of the day. From David Anderson’s discussion of work that has been ongoing since the onset of Covid to Babak Bahaddin’s pointing us to the latest diaries at the New Fadam farm site (needs website reference), the entire webinar is packed with insight.

By showing how cross-discipline expertise and international exchange of ideas and experiences can come together in a system dynamics initiative, this panel has placed the impacts of Covid at the center of their work.  We all know how the pandemic has influenced our lives, and this team is looking into why that happened and how to lessen its impact on us going forward.

Using a model developed by Ali Mashayekhi and applied extensively by Daniel Gordon, a component-based study and survey tool for COVID has been refined over the course of the COVID era. Luis Lunar-Reyes has applied the model to its effects on business and governmental response and Hyunjung Kim has taken the model and developed a self-study learning tool kit that is available under a Creative Commons license.

There is so much great work going on, watching this video can inspire System Dynamics specialists, and researchers from all disciplines, to take a look at Covid-19 through the lens of this model.

Ali N. Mashayekhi is a retired professor of management from the Sharif University of Technology in Tehran, Iran where he taught System Dynamics and strategic management. He received his BSc in Mechanical Engineering from Sharif University and his Ph.D. in System Dynamics from MIT in Cambridge Massachusetts.

Babak Bahaddin works as an associate consultant at isee systems. Babak holds a bachelor’s degree in engineering from Sharif University of Technology, and a Ph.D. in Information Science, from the University at Albany, State University of New York.

Daniel Gordon trained in System Dynamics at Rockefeller College, the State University of New York at Albany. He is retired from the New York State Health Department, where he spent 34 years working in health care policy analysis and HIV epidemiology.

David Andersen is Professor Emeritus in Public Administration and Information Science at the University at Albany – SUNY. He is a former President and Vice President for Finance for the System Dynamics Society as well as a winner of the Forrester Award.

Hyunjung Kim is a professor of management at California State University, Chico. She teaches strategy and management courses using system dynamics. She received her Ph.D. in Public Administration from the Rockefeller College of Public Affairs and Policy, University at Albany.

Luis Felipe Luna-Reyesis a Professor of Public Administration and Policy at the University at Albany and a National Academy of Public Administration Fellow. His research is at the intersection of Public Administration, Information Systems, and Systems Sciences.

Present at the Seminar Series

The Society Seminar Series consists of periodic online meetings on topics of interest to the systems thinking and System Dynamics communities. These virtual activities cover a wide range of topics that cross many domains while bringing together academics, practitioners, and students together for learning and lively discussion. Send your seminar proposal here

Sponsor a Seminar

The Society is actively looking for Seminar sponsors. This allows making a seminar open to all and free of charge. If your organization would like to sponsor one of these events, where you can promote your organization, firm or software, for instance, contact us at office@systemdynamics.org

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New Horizons of Systems Science

New Horizons of Systems Science

This Seminar was sponsored by the International Council on Systems Engineering (INCOSE).

Systems theory is developing to include new perspectives with a focus on integrated and inclusive transdisciplinary system approaches. This panel discusses new advances in systems science including critical systems thinking, social/socio-technical systems, and complex systems, which come together in the systems engineering principles. They also discuss where Systems Dynamics fits into this picture as well as other types of systems models.

By providing three perspectives on the discipline of Systems Engineering, the panelists shared a wide range of insights and experiences.  What the perspectives shared were ways Systems Engineering practitioners and the System Dynamics community could work together going forward.  One key to making New Horizons for System Science become reality is to merge the insights and experiences of each group into a shared, and sharable, practice.

 The relationship between Systems Science, Systems Thinking, and Systems Engineering is a key to understanding the range of applicable solution patterns

Erika Palmer began with the hope that both organizations, INCOSE and the System Dynamics Society, would continue to engage, learn, and innovate as part of a worldwide collaboration. The goal of the INCOSE panel is to foster an inclusive dialog on Systems Science. The purpose of the dialog is to accelerate the exchange and adoption of tools, techniques, and theories between the two sets of practitioners.

Michael Watson shared with the attendees that the upcoming release of System Engineering Principles will include Sociology as a topic.  By setting out the fifteen principles of Systems Engineering concisely, System Dynamics solutions can be applied to the principles.  Common patterns used across domains or across principles will provide leverage for other contributors.

Javier Calvo-Amodo shared insights from the perspective of building Systems Science disciplines and that students can participate with journal articles. Since System Dynamics provides a specific lens through which to view models, it can be used to validate the findings of other modeling types or to provide insights into what other modeling systems might reveal. A Systems Science map using Randomness and Complexity as the axes provided a guide to where specific System Dynamics developments can be best applied.

Erika Palmer (Cornell University) began with the hope that both organizations would continue to engage, learn, and innovate as part of a worldwide collaboration. The goal of the INCOSE panel is to foster an inclusive dialog on Systems Science. The purpose of the dialog is to accelerate the exchange and adoption of tools, techniques, and theories between the two sets of practitioners.

Michael Watson (NASA) shared with the attendees that the upcoming release of System Engineering Principles will include Sociology as a topic. By setting out the fifteen principles of Systems Engineering in a concise manner, System Dynamics solutions can be applied to the principles. Common patterns which apply across domains or across principles will provide leverage for other contributors.

Javier Calvo-Amodo (Oregon State University) shared insights from the perspective of building Systems Science disciplines and that students can participate with journal articles. Since System Dynamics provides a specific lens through which to view models, it can be used to validate the findings of other modeling types or to provide insights into what other modeling systems might reveal. A Systems Science map using Randomness and Complexity as the axes provided a guide to where specific System Dynamics developments can be best applied.

Complex systems are engineered by complex organizations.

Watch the recording below

Q&A

Q: Question to Javier: Why are there so few academic programs in Systems Science compared to Systems Engineering? Is this a problem?

A: They require interdisciplinary approaches, which are difficult to implement as they usually would span across different colleges within a university (e.g. College of Science, College of Liberal Arts, College of Business, College of Engineering, etc.)

Q: Question to Javier: What textbooks or papers would you recommend for learning more about systems science theory and the principles of systems science?

A: I recommend the following: Introductory: Cabrera, D., & Colosi, L. (2008). Distinctions, systems, relationships, and perspectives (DSRP): A theory of thinking and of things. Evaluation and Program Planning, 31(3), 311-316. and Cabrera, D., & Cabrera, L. (2022). DSRP Theory: A Primer. Systems, 10(2), 26.

Original work on systems science: Bertalanffy, A. R., Boulding, K. E., Ashby, W. R., Mead, M., & Bateson, G. (1968). L. von Bertalanffy, General System Theory. New York: George Braziller. and Von Bertalanffy, L. (2010). General systems theory. The Science of Synthesis: Exploring the Social Implications of General Systems Theory, 103.

Latest work on systems science: Rousseau, D. (2015). General systems theory: Its present and potential. Systems Research and Behavioral Science, 32(5), 522-533.;

Rousseau, D. (2018). On the architecture of systemology and the typology of its principles. Systems, 6(1), 7.

Rousseau, D., Billingham, J., Wilby, J., & Blachfellner, S. (2016). In search of general systems theory. Systema, 4(1).;

Rousseau, D. (2018). A framework for understanding systems principles and methods. Insight, 21(3), 9-18.;

Rousseau, D., Billingham, J., & Calvo-Amodio, J. (2018). Systemic semantics: A systems approach to building ontologies and concept maps. Systems, 6(3), 32.

Q: Can you suggest further introductory reading on category theory? This is new to me and a bit uncomfortable with this framing

A: Conceptual Mathematics by William Lawrence

Q: One thing caught my attention comments from Mike…. we need …. “to help build the complex system” and this…. helps… “development of a complex system”…. this is quite different from the underlying philosophy of System Dynamics where the emphasis is often trying to understand an existing system and adjust

A: The difference is in the context and/or domain of application; SD is designed to understand the underlying structures that give rise to System Dynamics as a means to understand from a high-level perspective how the system works. While useful for that purpose, the SD perspective places its main focus on control through feedback and feedforward loops, which may not capture other systemic and holistic arguments necessary to realize a complex engineered system. This is in alignment with Prof. Mike Jackson’s CST and CSP.

Q: Michael’s explanation of Category Theory introduced several concepts that are new (at least, new to me). Does INCOSE offer an introductory reference to supplement his insights?

Yes, go to INCOSE Systems Science Working Group Wiki and search in meetings. We have several presentations by Category Theorists in the meetings section.

Q: How would you differentiate between detailed complexity and dynamic complexity?

A: Those are two kinds of complexities that might or might not be present at the same time.

Q: The term engineering can mean the designing of a system, but is also heavily based on the activity of problem-solving. System Dynamics has problem-solving very strongly in its intellectual foreground. How are the latter activity and strength of System Dynamics used in Systems Sciences activities?

A: Causal loop diagrams and if needed the following simulation can be very powerful to help initial conceptualizations of complex problems. But they rarely yield the full answer; mostly because the models are difficult to verify and validate rigorously (especially if what is being designed is new and there is no frame of reference).

Q: Systems thinking means many things to many people some of these definitions are very loose and perhaps meaningless… is this a problem? Can it be fixed?

A: We believe that Derek Cabrera’s definition is quite good (it was developed using the scientific method). See Cabrera, D., & Colosi, L. (2008). Distinctions, systems, relationships, and perspectives (DSRP): A theory of thinking and of things. Evaluation and Program Planning, 31(3), 311-316. and Cabrera, D., & Cabrera, L. (2022). DSRP Theory: A Primer. Systems, 10(2), 26.

Q: How do we reduce the distance between the research and practice in Systems Engineering? The gap is much wider than, say, between physics and electrical engineering.

A: That is an excellent question that requires a much longer answer than what I can provide here. At the Systems Science Working Group, we are tackling exactly that. What I can say for certain is that we first MUST begin by defining the theoretical foundations for systems engineering. We have several projects working on that. Join us at INCOSE International Workshop to learn more.

Q: Can one mention articles and cases where the presented principles (of both speakers) are applied?

A: Calvo-Amodio, J., & Rousseau, D. (2019). The human activity system: Emergence from purpose, boundaries, relationships, and context. Procedia Computer Science, 153, 91-99. ;

Kittelman, S., Calvo‐Amodio, J., & Martínez León, H. C. (2018). A systems analysis of communication: defining the nature of and principles for communication within human activity systems. Systems Research and Behavioral Science, 35(5), 520-537.;

Taylor, S., Calvo-Amodio, J., & Well, J. (2020). A method for measuring systems thinking learning. Systems, 8(2), 11.;

Q: Why haven’t we seen System Dynamics modeling get as much attention as did machine learning modeling in recent years?

A: It is difficult to verify and validate rigorously.

Q: Does “Organized simplicity” equate to a reductionist approach?

A: Not quite, but the reductionist approach is most efficient in an organized simplicity

Q: Can you please talk about the role of soft systems methods (problem structuring methods for example) in systems engineering? They are useful in scoping poorly understood problem spaces but you rarely see them linked directly to System Engineer.

A: They are very useful to help address the social aspects of Systems Engineer endeavors (John Warfield and Peter Checkland developed their approaches (IM and SSM) to help with this issue); however, it is important to have frameworks that help us integrate all approaches. Mike Jackson’s CST and CSP are great foundations.

Q: Any books you’d recommend?

Mike Jackson’s 2019: Managing Complexity

Q: In System Dynamics, we often talk about the dynamic problem and the reference mode, then try to mode the system with the dynamic problem in mind. What might be the code switch for Systems Engineering’s approach?

A: There is no code switch conceptually. I would say that in Systems Engineer we look at requirements, value, or mission, and we design based on those (maybe similar to dynamic hypotheses, but not quite the same). We use MBSE (model-based System Engineer), in particular, a digital twin as the closest to a reference mode, but these are not isomorphic.

 

Erika Palmer is a Senior Lecturer in the Cornell Systems Engineering Program. She is the founder and chair of the Social Systems Working Group (SocWG) at the International Council for Systems Engineering (INCOSE); the Americas lead for Empowering Women Leaders in Systems Engineering (EWLSE) at INCOSE and represents Cornell on INCOSE’s Academic Council.

Michael D. Watson is the chair of the INCOSE Complex Systems Working Group and chair of the Systems Engineering Principles Action Team. He is the Technical Advisor in the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) Advanced Concepts Office. He graduated with a BSEE from the University of Kentucky in 1987 and obtained his MSE in Electrical and Computer Engineering (1996) and Ph.D. in Electrical and Computer Engineering (2005) from the University of Alabama in Huntsville.

Javier Calvo-Amodio is an Associate Professor of Industrial Engineering at Oregon State University; Chair of the Systems Science Working Group at INCOSE and Deputy Editor of Systems Research and Behavioral Science Journal. His research focus is on developing a fundamental understanding of how to integrate systems science into industrial and systems engineering research and practice to enable better engineering purposeful human activity systems.

Present at the Seminar Series

The Society Seminar Series consists of periodic online meetings on topics of interest to the systems thinking and System Dynamics communities. These virtual activities cover a wide range of topics that cross many domains while bringing together academics, practitioners, and students together for learning and lively discussion. Send your seminar proposal here

Sponsor a Seminar

The Society is actively looking for Seminar sponsors. This allows making a seminar open to all and free of charge. If your organization would like to sponsor one of these events, where you can promote your organization, firm or software, for instance, contact us at office@systemdynamics.org

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Can You Fix the American Health Care System? ReThink Health Dynamics

Can You Fix the American Health Care System? ReThink Health Dynamics

Experience first hand the ReThink Health Dynamics Model used by Dartmouth, Brown, MIT, Atlanta, San Diego, Cincinnati, and many other universities and communities throughout the United States. Thousands have come together to explore how they might steward regional resources to achieve the Triple Aim (improve health, cut costs, and quality of care) and beyond (equity and worker productivity).

In this webinar, we:

• Learned how this System Dynamics simulation model has been used to create change,

• Converged on a sound strategy for achieving the Triple Aim, and

• Got a chance to test drive the simulation in an experience much as the one had by real communities doing real work.

And the best part is that this simulation is free for classroom and community use, so you can provoke a more fruitful conversation about how we can make real progress in population health.

Learn more about the Seminar Series.

Watch the recording below

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ISDC 2021 Highlights: Modeling for Action in Environmental Health

ISDC 2021 Highlights: Modeling for Action in Environmental Health

The International System Dynamics Conference (ISDC) convenes practitioners who demonstrate what’s new and developing in their fields with System Dynamics. This section of the WiSDom Blog, “Conference Highlights,” asks system dynamicists to spotlight key presentations and innovations presented at the conference. 

Conference Highlights Editorial Team: Saras Chung, Will Glass-Husain, Jack Homer, Sara Metcalf, and Remco Peters with coordination by Christine Tang

This highlight by Martha McAlister shares a first-time conference attendee’s perspective on modeling for action in environmental health. 

Modeling for Action in Environmental Health

 

When environmental risks remain unmitigated, they end up hurting our ability to lead healthy and productive lives. These risks are often concentrated where populations are the most marginalized, thereby creating or contributing to unjust health disparities. Environmental health and justice problems can be complex, as they intersect multiple domains (social, economic, political, legal, institutional, etc.) and may involve years or decades of lag time, starting from the accumulation of multiple exposures and ending in life-threatening chronic illnesses. 

System Dynamics offers opportunities for modelers to engage with broad audiences to address environmental health and justice challenges. Modelers can elicit public or expert participation before, during, and after the modeling process to promote confidence in the results and to encourage holistic learning that goes beyond narrowly epidemiological approaches. 

As a first-time attendee of the International System Dynamics Conference, I wanted to learn how System Dynamics is being used in the environmental health context and about the challenges of applying System Dynamics to such complex problems.

The first hint came during the Student-Organized Colloquium, where keynote speaker Dr. Josephine Musango stated that “engagement is crucial.”  As the conference progressed, I heard several presenters talk about their use of participatory modeling to study global environmental and health issues. 

Laurent Smets spoke about using group model building with virology experts to connect early vaccine research and development to the user requirements at the “last mile” in low- and middle-income countries. 

Kelsey Werner described workshops with local community groups in India (organized by the Social Systems Design Lab at Washington University) to model factors affecting their use of less harmful liquefied petroleum gas (e.g., for cooking) in place of solid fuels like firewood or charcoal..

Others reported on using System Dynamics simulation interfaces that engage stakeholders. As Juliette Rooney-Varga put it, this requires translating well-informed scientific models into meaningful, recognizable intervention levers and outputs. 

Allyson Beall King, presenting on her work with Tyler Opp, echoed this concept of scientific translation in describing their model of toxic sediments in Lake Coeur d’Alene.  They wanted to make sure this model would not only satisfy scientists but also be fully accessible and transparent for the public.

I also learned from Daniel Kliem’s talk about how to involve experts in participatory modeling. He said that if a simulation was the ultimate goal, then one should “fail fast” by developing the quantitative model sooner rather than later.  He also advised modelers to remember that we are the translators and integrators of others’ knowledge, and as such we should always give those experts the credit they are due. 

This last point reminded me of something that the other Student-Organized Colloquium keynote speaker, Dr. Irene Headen, said about one of the strengths of System Dynamics: the process allows modelers to collect and integrate multiple perspectives on a single topic. 

The conference is a heady experience for a first-time attendee like myself. Thinking about the presentations I attended, I realize that none precisely addressed environmental health and justice per se.  But that doesn’t really matter, because the presenters made it easy to see how their experiences and insights have broad application, and I look forward to applying these ideas in my own work.     

 

Martha McAlister – mcalisterm@usf.edu

Martha is a PhD student of Environmental Engineering at the University of South Florida. She studies the efficacy and sustainability of environmental health interventions. Martha’s participation in the International System Dynamics Conference was supported by USF NRT Strong Coasts (National Science Foundation under Grant No. 1243510). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, USF, or NRT Strong Coasts.

 

Check out the Society’s SIGs – including Environmental SIG and Health Policy SIG

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System Dynamics for Climate Change Mitigation

System Dynamics for Climate Change Mitigation

We had an insightful Webinar with the participation of With Juliette Rooney-Varga, Carolyn McCarthy, Sibel Eker, and Steve Arquitt .

Integrated System Dynamics models of economy and environment have long been used for research and decision support for sustainability problems, starting with the seminal work of World Dynamics and Limits to Growth. We discussed how System Dynamics models support decision-making, stakeholder, and public engagement for climate change and sustainability problems. We reflected on existing models and tools, such as Climate Interactive’s En-ROADS and Millennium Institute’s iSDG tool, and their use cases. We also discussed how the Climate Change Initiative at UMass Lowell uses System Dynamics tools to raise awareness on climate change.

If you’re a member, you can watch the webinar recording here.

Below are the answers to questions asked live during the Webinar.

Learn more about the Seminar Series.

Q&A Seminar | System Dynamics for Climate Change

Climate Interactive:

How would you describe the interaction between complex models (GCMs …) and simpler system dynamic models in more detail? (how can they support each other?)

Shortly, large detailed models help for cross-validating the simpler models. In return, simple models support the complex models in stakeholder engagement and scenario co-production.

What are the similarities between the EN-Roads Model & the iSDG Model? What are the main differences?

Both EN-Roads and iSDG are based on the System Dynamics method. Both emphasize transparancy, user friendliness, and shared learning. Both place great emphasis on facilitation and support in shared learning. The differences: EN-Roads is a global model, where iSDG is customized to support planning in a particular country or geographic region. EN-Roads is focused on strategies to keep global temperature below a specified level, where iSDG’s focus is more diffuse taking on all the SDGs.

What are some of the major impacts that the Climate Interactive team see on the application side of the models?

This question was not clear to me during the webinar. If “application” means the use of Climate Interactive’s models, we see quite a substantial impact. Only En-ROADS has been used in 73 different countries so far, engaging almost 63000 people. We have a wide audience, from policymakers and philantropists to higher education students and community members. One of the most striking and uplifting recent examples of En-ROADS outreach is the events organized by one of our ambassadors with smallholder farmers in Tanzania. 

Why don’t you use python as the intermediate language? Thanks

Answered during the webinar. Python is a user-friendly language but not as fast as C. We need speed in interactive simulators, so the Vensim model is converted to C.

Are system dynamics models being used in conjunction with Big data and AI?Can system dynamics models learn with machine learning?

There are initiatives about this as far as I know, and ML is very useful for quantifying empirical relationships, but outsourcing the model building completely to AI is not possible, neither desirable in my opinion. System Dynamics’s main strength lies in its descriptive nature, accessibility and understandability. While a hard coupling of SD and machine learning can provide many benefits, it might override the main strengths.

Since there seems to be many questions/comments regarding implementation/compliance, might it be helpful to start focusing on modeling the topic of governance itself, in order to identify and understand the influencing dynamics and loops on the gaps between the ideal solutions actually implemented?

In general, especially regarding specific sustainability governance problems, I agree that this should be the approach, because problem delineation and understanding the system strructure is key to developing any solution. In En-ROADS, though, the primary purpose is public engagement around the topic of “solutions”, hence the underlying dynamics are not co-modelled but shared with the users through various indicators and graphs.

Does the Climate Interactive climate-economy feedback have anything to do with Nordhaus’ “damage” function?

Since En-ROADS is an interactive simulator, it includes a damage function that allows the users to experiment with various “damage functions” found in the literature, including Nordhaus, or make their own assumptions. You can read more about it here 

When will the nature-based/land-based parts in En-ROADS be accessible online?

In the next few months. Please check either the En-ROADS simulator or this page

Very interesting presentation Sibel. Can SDeverywhere and the implementation into a website be done by somebody completely unfamiliar with C or Java or any programming? Many thanks.

I must say that it would be a bit challenging for someone who has no programming experience. There are guidelines, though, which might be helpful to get started.

Questions to Millennium Institute

What are the similarities between the EN-Roads Model & the iSDG Model? What are the main differences?

Both EN-Roads and iSDG are based on the System Dynamics method. Both emphasize transparancy, user friendliness, and shared learning. Both place great emphasis on facilitation and support in shared learning. The differences: EN-Roads is a global model, where iSDG is customized to support planning in a particular country or geographic region. EN-Roads is focused on strategies to keep global temperature below a specified level, where iSDG’s focus is more diffuse taking on all the SDGs.

Are parts of iSDG Model publically available?

Yes, go to www.millennium-institute.org/isdg . There you can access a demonstration model, videos on the iSDG, and the model documentation.

To what extent is the Millennium Institute SDG model open source? It would be so nice to use it rather than starting modelling from scratch in every research project.

At this time the iSDG is not open source. iSDG models are developed within the frame of a specific project. However, much can be learned about the model and its structure by visiting www.millennium-institute.org/isdg.

@Steve, how do you integrate “indicators” of SDG’s to report a single metric?

The iSDG reports the status of each of the 17 SGDs separately. The level of performance of the targets falling under each SDG are averaged to calculate the SDG performance. Targets can be thought of as desired levels of indicators. For a complete explanation see https://www.pnas.org/content/pnas/suppl/2019/10/29/1817276116.DCSupplemental/pnas.1817276116.sapp.pdf

SDG and how it is implemented in real world is highly context-dependence – how iSDG address this?

The iSDG is customized for the country or regional setting. Workshops are held with local experts, decision-makers, and stakeholders who review the model and identify key issues and policies to include inthe iSDG model.

Steve’s question regarding connecting real action to the plan is important. How do we make the interactive modeling tools an integral part of follow up and feedback on action?

With climate change and the SDGs both this really hits the crux of the matter. With the iSDG it is important to involve a broad spectrum of stakeholders on the modeling team and in the workshops who are motivated to see that the selected scenarios are being transformed into policies and then funded. This will require a well-trained team that can run scenarios, derive policies and work with the relevant government people to assure implementation. Monitoring is essential, and needs to be built into the projects. I fully agree with Juliette that the models need to engage with citizens who will then push leaders to make the necessary changes. I would love to hear others’ ideas and experiences on this.

Why choose poverty as a key #1 SDG?

“No poverty” as SDG1 was defined and designated by agreement of the 193 Agenda 2030 signatory countries. There is debate about which SDG is the most important. The iSDG takes no position on which SDG is the most important However, in the iSDG poverty is linked to almost every SDG.

Is this model (MI iSDG Tool) built in STELLA?

If you mean the iSDG, yes the model I showed was built in STELLA however we also have a version in Vensim.

What are some of the active projects that MI is doing today?

Currently we are working on iSDG projects in Afghanistan, Bhutan, China, Uganda, Namibia, Djibouti, Kenya, Democratic Republic of the Congo.

This question–or 2 questions–are for Steve. First, are worldviews and values included in the iSDG models? If so, how? A second question relates to how the highest-level decision-makers regard the models. I’m new to SDS but spent a number of years working with a roughly analogous set of high-level

After some relection, worldviews and values are pervasive in the iSDG model by virtue of the SDGs themselves. iSDG is intended to help policy-makers design strategies and allocate resources for attaining the SDGs. This includes the “leave no one behind” principle, gender equity in education and economic opportunities, equitable income distribution, preserving biodiversity for future generations, rule of law and many others.

Questions to CCI

@CCI any tips on how to engage kids with these tools?

Find resources: Comprehensive Facilitator Resources  Online World Climate Resources

Steve’s question regarding connecting real action to the plan is important. How do we make the interactive modeling tools an integral part of follow up and feedback on action?

Watch the recording for a full answer

@Juliette, could you say a bit about hope? There is a political divide especially in the US, but I read recently that % of the US population who feels anxious about climate change is ~68%. Could role-play games help deal with this anxiety?

Watch the recording for a full answer

“Research shows that showing people research doesn’t work”. What are your thoughts on this @Juliette?

We agree with John Sterman! But if you want to read more about this research, you can here

Why do you think that higher levels of “hope” begin and end higher with the i-H group?

Watch the recording for a full answer

Was the ethnic cultural diversity in your simulation group meetings more diverse than the photos would suggest? If not it’s concerning that you have a rather restricted sample?

Thank you for this question. The breakdown of participants’ racial and ethnic diversity for Building Consensus for Ambitious Climate Action through the World Climate Simulation can be found on page seven. Limitations relevant to our sample can be found on page 24 and reads, “Our sample was not randomly drawn from the general population and is therefore not expected to be representative of the American public. In addition, because the youngest participants in our study were drawn from programs serving low-income, first-generation-to-college students, age likely correlates with other demographic traits in our sample. We therefore do not claim that the observed effects of the simulation or demographics extend to the general American population.”

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Watch the recording below

Whoops, this recording is available for members only. If you have a membership, please log in. If not, you can definitely get access! Purchase a membership here. If you're not a member but have purchased a ticket to this webinar, please contact us at office@systemdynamics.org