Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Search in posts
Search in pages

Go Home

How to Sell System Dynamics (Or Anything Else)

If it’s so good – why is System Dynamics so hard to sell?

The key to selling System Dynamics has nothing to do with what we’ve learned in grad school. Instead, the key is to clarify a fundamental confusion everyone seems to have. When you ask people what makes business development successful they’ll often respond “sales is about personality.” Instead, it should be, “sale is a process.”

Watch the webinar recording on this topic.

This guide is designed for startups, early-career contractors, or other practitioners slogging through the business development cycle of landing system dynamics contracts. Additionally, the concept and application of a sales pipeline is transferable to other applications of system dynamics: grad school or grant applications, employment pursuits, and even publication.

In this article, I cover the basics of a sales pipeline; the difference between sales as a personality vs. sales as a process; and how to use the sales pipeline with added tips and tricks.

What is a Sales Pipeline?

A major confusion that practitioners have about selling technical work is the belief that business development is personality versus a process. I thought that myself for years – that to be “good at sales” was like being good at math. You were either born with the skills or you weren’t.

Sales, however, is a process. The structure of selling as a system is depicted in this simplified aging chain.

The units of measure moving through the aging chain are “Opportunities” (Opps) which represent a potential sale. But opportunities only become a sale in the final outflow of the stock on the far right. Along the way, opportunities either advance along the pipeline to the next stage or get abandoned for various reasons. Structurally speaking both outflows can serve a purpose.

“Knowing which stage an opportunity is in is important, you can’t just take it for granted it will always begin at the left even though most will.”

You want good opportunities to move to the right along the aging chain and opportunities that aren’t ready to move out through the abandonment downwards. Note that all flows are bi-flows. Opportunities may move back and forth horizonally and sometimes abandoned deals re-enter the pipeline back in the same stage they were. I’ve provided a proposal on an opportunity that went cold and was abandoned because the circumstances weren’t right. Years later the buyer sent me an email with the proposal asking if it was still valid. Circumstances had changed. This is why knowing which stage an opportunity is in is important, you can’t just take it for granted it will always begin at the left even though most will.

Learning to see sales as a process through an aging chain provides immediate insights. At each stage, there’s a natural abandonment rate – regardless of personality or salesmanship. What this means is that selling as a process is a numbers game that operates as a process. The more you put in at the left and work the process through the stages, the more you’ll have come out the right.

If it helps, think of the aging chain as a series of ratios and drivers:

This is notional but it illustrates a few key aspects of the ratio and drivers. First, if the ratio is 30:1 from opportunities to engagement and you want to have at least one engagement – do you have 30 opportunities in your pipeline?  If you want two engagements, do you have 60?  If you’re on a paying engagement that is about to end and you haven’t filled your pipeline with 30 more potential opportunities, where’s your paycheck going to come from?

This “feast or famine” effect is particularly hard for small firms where both selling and delivering are done by the same person. This sales pipeline is also useful for fields other than private-sector business sales. The sales pipeline, ratio, and drivers also apply to non-business uses. Whether you’re pursuing employment, grants, or other opportunities you’ll move through some version of these stages.

“Everything we learned about first-order control, unit dimensionality, and truth & beauty in models only ever gets a chance to shine if first, we have exercised other skills”

The only stage where our technical knowledge of System Dynamics arises is during the proposal. Everything we learned about first-order control, unit dimensionality, and truth & beauty in models only ever gets a chance to shine if first, we have exercised other skills: qualifying and nurturing opportunities to get them to the point of being able to submit a proposal. The value prop of a proposal matters a lot too, but that’s a different blog post.

Stages of the Sales Pipeline – Sellers Perspective:

In this simplified seller’s perspective, the stages of the four stages of the pipeline are:

  1. Qualifying
  2. Nurturing
  3. Proposing
  4. Selection & Negotiation

Entire books can be written about any one of these – but to get started here are some simple definitions.

Qualifying: This stage is about evaluating opportunities to ensure they have the potential to reach the final stage. This means, at minimum, determining the potential buyer(s) have:

  1. A clear problem they need to solve.
  2. A budget with which to solve it.
  3. The authority to use that budget and grant a contract award.
  4. A timeline in which they want to start the work.

Nurturing: Nurturing consists of two things: first, identifying concerns, questions, or barriers a buyer might have and addressing them. This not only builds confidence in the buyer but gives you insights into the proposal. Second: keeping in regular positive contact. Be proactive on this. End every meeting with a review of what concerns, questions, or barriers you’re going to resolve next and confirm the timeline and schedule of decision-making. If a buyer hasn’t responded – politely nudge them on a regular basis. If you no longer think a buyer is serious – find out by asking “is now not the right time, and if not when might be?” You don’t want to spend time chasing deals that won’t happen.

Proposing: Congratulations! All that work has paid off and you finally get to put your best foot forward in providing a proposal. Proposals usually consist of both technical and financial elements. And if you’ve done a good job qualifying and nurturing the opportunity through the pipeline the proposal should almost write itself. How you will solve their problem, address any concerns that came up, and show how you’ll deliver the results they seek.

Selection & Negotiation: The work isn’t over when you hit send on the proposal! There may be clarifying questions, request for a demonstration, or additional details. Selection & Negotiation is a lot like nurturing – find out what the barriers to selection are and manage them while keeping positive regular contact with the buying team.

Tips for Selling as a Process with Pipeline Development

Numerous books and blogs give advice on how to work on pipeline development. But when you’re just beginning, there are three tips I’d suggest starting with based on my own experience over the years:

1. Don’t mistake the opportunity stage you’re in.

2. Don’t spend too much time on unqualified opportunities vs. developing qualified ones.

3. Managing the pipeline efficiently:

  1. Load new opportunities frequently.
  2. Disqualify opportunities that aren’t ready quickly
  3. Nurture qualified opportunities through development.

Resources for Sales Pipeline

Just as there are many books and website posts about the sales pipeline – lots of people will try and sell you fancy tools. You become an opportunity in their pipeline! But ask yourself – if you’re not already getting that notional 30 opportunities loaded that lead to one sale; do you really need a fancy tool? At Dialectic, for five years now we’ve managed our sales pipeline on a simple google sheet and we’ve attached a free copy available through Google Sheets.

Closing

Selling System Dynamics work – whether to a private business, government research, or finding a faculty posting – requires many elements. But one thing it doesn’t require is a sales personality. Understanding selling as a process and using a sales pipeline to manage that process helps immensely.

I’m always happy to network and exchange ideas – so if you need help on the sales pipeline or any aspect of developing System Dynamics as a business offering feel free to drop me a line at timc@dialecticsims.com or hit me up on Twitter @DialecticSims or @InfoMullet. Be sure to say hi and let me know if you appreciate this post or if there are other topics you’d like to hear about. And don’t forget to bookmark the System Dynamics Society Practitioners Blog!

Watch the recording below

Want to know more about How to Sell System Dynamics? Watch the recording below!

In this webinar, Timothy Clancy clarifies how selling System Dynamics is less about having a sales personality than it is about following a process known as a sales pipeline. This seminar is designed for startups, early-career contractors, or other practitioners slogging through the business development cycle of landing System Dynamics contracts. Additionally, the concept and application of a sales pipeline are transferable to other applications of System Dynamics: grad school or grant applications, employment pursuits, and even publication.

Whoops, this recording is available for members and ticket purchasers only. Please login to verify. If you’re not a member, purchase a membership here.

About the Author

Timothy Clancy (Tim) is the founder of Dialectic Simulations Consulting, LLC a firm focused on delivering systems thinking and simulation capabilities to public, private, and non-profit 500 clients. Tim’s career in consulting spans over 25 years, including 10 years at IBM, where he was deeply involved in both business development and delivery. Tim has a Ph.D. in System Dynamics from WPI.

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Q&A Session: From Problem Selection to Modeling and Career Development with Mohammad Jalali

A Q&A session with Mohammad Jalali. An interactive event where the audience is the main driver of the talk. Questions from all directions, from how to choose a good dynamic problem to career development. 

Watch the recording below

About the Speaker

Mohammad S. Jalali (MJ) is an Assistant Professor at Harvard Medical School and works on data science and simulation-based approaches to help policymakers develop effective policies. He works with decision-makers, does fieldwork, and collects data to inform his models and analyses. Since 2019, he has received over $5 million in grant funding and his work has been featured in several publications. He has also held several editorial positions and has received multiple awards for his work. Before joining Harvard, he was a research faculty at MIT Sloan and a consultant at the World Bank. 

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

How Food and System Dynamics Gave me A Career

A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact.

Papers mentioned:

  • Transforming Food Systems at Local Levels: Using Participatory System Dynamics in an Interactive Manner to Refine Small-Scale Farmers’ Mental Models – read
  • Participatory Modeling Updates Expectations for individuals and Groups, Catalyzing BehaviorChange and Collective Action in Water-Energy-Food Nexus Governance – read
  • Sustainable and Healthy Diets: Synergies and Trade-offs in Switzerland – read

Download Presentation

Watch the recording below

About the Speaker

Birgit Kopainsky is a systems thinker and modeler who studies the role of System Dynamics analysis and modeling in facilitating transformation processes in social-ecological systems. She aims to provide guidelines for understanding complex dynamic systems and making information on climate change, agriculture, and food security accessible and relevant for action. She works in Europe and sub-Saharan Africa and engages with a wide range of stakeholders to achieve breakthrough moments of understanding and promote change toward resilience and sustainability. She currently works as a full-time professor at the University of Bergen for the Master’s program in System Dynamics.

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Documenting the Modeling Process

Documenting the Modeling Process

Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily recalled and shared for months or even years after being collected? The authors of the System Dynamics Review article “Documenting the modeling process with a standardized data structure described and implemented in DynamicVu” propose that adopting a standardized data structure is the first step. This presentation describes such a data structure and focuses on the many advantages of documenting the modeling process with such a structure, including a demonstration of an online database specifically designed for documenting the process of building a simulation model called DynamicVu.

Watch the recording below

Whoops, this recording is available for members and ticket purchasers only. Please login to verify. If you’re not a member, purchase a membership here.

About the Speakers

Warren Farr is currently working with business owners and managers to increase productivity and to plan confidently. Warren combines simulation modeling with data transparency to create understanding. Intuitive access to data using insightful database design is often a part of the solution. To organize the information collected to inform and build simulation models, Warren developed DynamicVu, a secure web-enabled application. During his career, Warren spent 20 years as President/CEO of Refrigeration Sales Corporation, a midwest wholesaler of heating, ventilating, air conditioning, and refrigeration equipment, parts, and supplies. Through long-term planning, technology adoption, and process improvement, the business grew from $50M to over $120M without increasing the employee count. Prior to RSC, Warren held various product design, engineering, and sales positions in the growing computer networking industry of the 1980s and 1990s, including The MITRE Corporation in Boston. Warren obtained his Bachelor of Science degree as well as his MBA degree from Duke University. Warren obtained his Master of Science in System Dynamics from Worcester Polytechnic Institute. Warren’s career has been spent designing and operating complex systems: mechanical, electrical, and social. Since 2000, System Dynamics has provided him with a robust way of describing, understanding, and improving important systems. Warren is an active member of the International System Dynamics Society.

Samuell D. Allen is a Ph.D. Candidate at the Worcester Polytechnic Institute. In his dissertation research, he’s studying supply chain sustainability from a strategy and operations management theory development perspective. Samuell also studies complex health services and quality improvement situations. In these efforts, he specializes in the application of innovative methods for leveraging qualitative data and theoretical resources to develop and evaluate causal loop diagrams and simulation models.

Andrada Tomoaia-Cotisel is a Policy Researcher at the RAND Corporation and Professor of Policy Analysis at the Pardee RAND Graduate School. She teaches and mentors Ph.D. students in mixed-methods approaches to system dynamics modeling and systems thinking. She received her Ph.D. in Health Services Research & Policy from the London School of Hygiene and Tropical Medicine. She specializes in developing and applying formal methods bringing the strengths of qualitative and quantitative data to improve conceptualization and validation. Her current work explores dynamic complexity in health service delivery, implementation, and outcomes, as well as the influence of context and resulting variation.

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Practitioner Profile: Jack Homer, Homer Consulting

Practitioner Profile: Jack Homer, Homer Consulting

Welcome to Practitioner Profiles, a series of up-close blog-length interviews with experienced System Dynamics practitioners.  We have a standard set of 10 questions and let practitioners take the responses in any direction they choose.  They tell us about who they are, how they got involved with the field, how they work with clients, and in what new directions they may be heading.  

For any questions or comments, please contact Dr. Saras Chung (saras@skipdesigned.com). 

For this spotlight, we talked with Dr. Jack Homer from Homer Consulting.

What kinds of SD project applications do you do?

I have operated as Homer Consulting for nearly 35 years now, first in California, then in New Jersey, and now in New York.  For the first 15 years, nearly all my consulting projects were in the private sector, working directly with large corporations or as a subcontractor to larger consulting companies.  I experienced a major shift toward the public sector in the early 2000s, including more than a dozen years with the US Centers for Disease Control and Prevention (CDC).  For the last 20 years, about 70% of my work has been with government and non-profit organizations, most often on health and healthcare policy, but also on climate change and decarbonization.  I have some new clients every year, but also some clients with whom I’ve worked for many years, including Rethink Health, Kaiser Permanente, Climate Interactive, and Deloitte.

What is distinctive in your approach to SD projects? 

Starting with my PhD dissertation 40 years ago, I have always taken a “structure and data” approach. That means I press the client not only for plausible causal hypotheses and details, but also for any numerical data that might be relevant.  There’s plenty of data out there these days to support modeling, although sometimes it needs a deep dive and statistical analysis or algebraic manipulation to see it clearly.  This effort is worth it, and I always find that the data tell me something important I didn’t know and that even the client didn’t know or at least didn’t think to communicate.

In what way is Homer Consulting unique or different from other organizations doing SD work?

I’m a solo practitioner, though I often do projects in conjunction with other modelers and consultants.  I may miss out on the benefits of being a fixture at a larger organization, but I have always valued my independence and autonomy.  This has allowed me to split my time between projects and writing papers about them.  Publishing papers has always been important to me personally, and it’s also turned out to be a good way to attract new clients.

What is your role in the organization, especially with regard to SD Project work? 

I’m the chief cook and bottle washer, as they say.

How did you originally get interested in SD, and when was that?

Like quite a few SD old-timers, I first became excited as a teenager in the potential of computer simulation as dreamed up by science fiction writer Isaac Asimov in his Foundation Trilogy books.  In 1972, I happened to meet Dale Runge from the MIT SD group (he was the husband of my former Spanish teacher), and he told me all about SD and gave me a copy of Jay Forrester’s “Counterintuitive Behavior of Social Systems” paper.  As an undergraduate at Stanford, I studied applied mathematics including statistics and operations research, but never found anything that approached SD for its breadth and explanatory power.  I started at MIT in 1977 and completed the PhD in 1983.

What individuals and organizations are inspirations to you?

I’m impressed by people in the public sphere who not only put it in the hard analytic work themselves but also grow an effective and long-lived organization around it.  People like Amory Lovins (famous for “Soft Energy Paths” and founder of the “think-and-do-tank” RMI), Don Berwick (founder of the Institute for Healthcare Improvement and later head of the US Centers for Medicare and Medicaid Services), Jay Forrester (for building our entire discipline from scratch), and, of course, John Sterman (my classmate at MIT), who more than anyone else moved SD toward recognition as a legitimate management science discipline.

What accomplishments are you proud of?

Let me say first that I’ve always wanted to improve the world in some way with my work.  I guess that’s a pretty high bar to set.  Many of my projects don’t seem to clear that bar, at least in terms of actions taken subsequently by the client.  It’s just a fact that modeling projects sometimes fizzle out and clients may not follow through on recommendations.  Even award-winning multi-year work, like what we did for the CDC and Rethink Health, does not always translate into real-world change that I can identify and quantify.  And yet, I often hear later from people (who took part in these projects or read our papers about them) how much they learned from our work.  My longtime collaborator Bobby Milstein says that our modeling and writing have helped to build the intellectual foundation for a growing movement for universal health and well-being.  I must take his word for it because he’s in the trenches more than I am.

What challenges have you experienced?

The frequent fizzling-out and lack of follow-through.

What kinds of SD work would you like to be doing over the next 5 years?

I will continue to work with my favorite clients on important issues but will take on fewer new clients.  I’ve done some of this already, and it has freed me up to do volunteer activities like managing the SD Society’s remote one-on-one mentoring program and doing some mentoring myself.

Are there any specific changes or tweaks you would like to make in how you and your organization approach SD project work?

I would like to have more early conversations with clients about how the model or its results will ultimately be used.  I’ve found that such a conversation can help keep everyone on the project working together toward a longer-term goal.

 

Have other questions or comments? Leave a comment below or reach out to Jack at Homer Consulting.

Recent Posts

How to Sell System Dynamics (Or Anything Else)

How to Sell System Dynamics (Or Anything Else) If it’s so good – why is System Dynamics so hard to sell? The key to selling System Dynamics has nothing to do with what we’ve learned in grad school. Instead, the key is to clarify a fundamental confusion everyone seems...

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

The System Dynamics Conference From the Perspective of a Multi-Method Simulations Developer

The System Dynamics Conference From the Perspective of a Multi-Method Simulations Developer

System Dynamics, Agent-Based and Discrete-Event simulations are three competing and complementary simulation methods that are used to address a wide range of real-world problems. Each one of these has its pros and cons and can be applied better or worse depending on the context of the problem but can also be complementary in order to capture different pieces of the reality we want to simulate.

As System Dynamics seems to be an obscure topic for most discrete-event and agent-based modelers, the opposite is also true. And assuming good practices are in place, contrary to Agent-Based modeling guidelines, in System Dynamics, there is no standardized way in which a section of a model can be reutilized and the definition of what a sub-model or module is, depends largely on the context of the modeler. In other words, while in Agent-Based modeling, the sub-model is the agent itself, in System Dynamics, the sub-model can be a theme, an entity, a set of stocks that look good together, the importance of a sub-system, etc. A solution to this standard modularization problem, both in its qualitative and quantitative forms was discussed during this conference, in particular for Work-in-Progress sessions.

From a qualitative point of view, the presentation “Modelling the Complexity of Large Systems: A Network-aided System Dynamics Approach”, intends to use a method based on graph theory to identify themes within a complex network of causal relationships. Each theme can be approached separately by the subject matter expert that is associated with that theme, while also helping define boundaries for future work to be developed (see Figure 1). This is a great approach because it highlights the themes and transforms eventually this complicated network into a well-designed Causal Loop Diagram, with clear sections that are easy to read and understand. Looking at these themes, to a multi-method simulations developer, it appears that these themes are very closely related to the concept of an agent.

Figure 1: Social network of themes and causal loop diagram of selected themes
(Wang, Zimmermann: Modelling the Complexity of Large Systems: A Network-aided System Dynamics Approach, 2022 International System Dynamics Conference, figure used with permission.)

From a quantitative perspective, in their workshop “Using a Tool to Professionalize Model development” Copernicos showed a tool that attempts to improve the structure of a model by creating entities that represent certain hidden topics in models, mostly looking at dimensions and subscripts (or arrays), and generating modules in Stella or sub-models in Vensim that represent what they call hidden topics. This is done with an Excel plugin that acts as a transformation interface that reads the model and generates a new model that is organized with the concept of entities. The arrays are still there as defined by the modeler, but the way the model is organized in modules (in Stella) or sub-models (in Vensim) becomes very similar to what an agent would be in agent-based modeling or to what an entity would be in Ventity. In my opinion, this is a great approach since it goes in the direction of standardizing the modularization of a big complex model, which is the topic we are discussing in this article.

In the multi-method framework, mostly used by AnyLogic developers, it is common to solve these problems by having Agent-Based/System-Dynamics hybrids, in which modules or arrays are replaced by the concept of agent. From the qualitative side, having agents as part of the conceptual framework greatly helps build a hierarchical network of reusable modules that represent the system that needs to be conceptualized. From the quantitative side, Copernicos’ attempt to generate entities is a great idea to build a standardized model structure, which is what multi-method modelers like me do use the standard multi-method framework present in Software such as AnyLogic.

During the conference, work related to hybrid simulations was sparse, and of course, this is a System Dynamics conference, so it’s maybe expected, but it seems to me from conversations with people during the event, that the interest in relation to multi-method modeling is much higher than what the presented work shows. The presenter of “A Cross-Disciplinary Computational Framework for Hybrid Simulation and Modeling” reported on a systematic literature review on how hybrid modeling has increased in popularity. Only a few authors presented hybrid models, e.g. Portia Mupfumira showed hybrid agent-based/System-Dynamics models in two presentations: “Smart Cities Hybrid Conceptual Modelling” and “Development of Hybrid Smart Energy Distribution Decision Support Model: Case of Zimbabwe” and Al Thibeault used Ventity to present “Agent-based Model for Testing Policy Options for Long-term Stability and Sustainability in the Rare Earth Mineral Sector”. Also, the Software Modelica’s object-oriented and multi-method capabilities were presented in the poster “Hierarchical, Component-Based Modeling Using the Cyber-Physical Modeling Language Modelica”.

During the roundtable “Panel on Careers in System Dynamics”, one of the panel members, with 20 years of experience in the field, talked about Agent-Based as a sexy methodology. And this is true, in particular, because agent-based is significantly more used in the business world (along with discrete events), making it very useful to build a proof of concept models very fast with 2D and 3D animations that can be very beautiful and attractive. But he talked about Agent Based modeling as something that has nothing to do with System Dynamics, expressing a separation when it comes to comparing both methods, instead of a synergy. The panel also talked about the struggle to be taken seriously as a System Dynamics professional and the struggle to get stakeholders to buy into the System Dynamics concepts but isn’t maybe the multi-method idea, that is largely documented in the literature the first step toward a thriving System Dynamics community? I think it might be.

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Data & Uncertainty in System Dynamics

Data & Uncertainty in System Dynamics

Jay Forrester cautioned that “fitting curves to past system data can be misleading”. Certainly, that can be true, if the model is deficient. But we can have our cake and eat it too: a good model that passes traditional System Dynamics quality checks and fits the data can yield unique insights. This talk discusses how data, calibration optimization, Kalman filtering, Markov Chain Monte Carlo, Bayesian inference, and sensitivity analysis work together. The emphasis is on practical implementation with a few examples from a real project, and pointers to resources.

Using all available information, from informal estimates to time series data, yields the best possible estimate of the state of a system and its uncertainty. That makes it possible to construct policies that are robust not just to a few indicator scenarios, but to a wide variety of plausible futures. Even if you don’t use the full suite of available tools, there’s much to be gained from a simple application of eyeball calibration, traditional reference modes as pseudo-data, and exploratory sensitivity analysis.

About the Speaker

Tom Fiddaman is the CTO of Ventana Systems and part of the development team for Vensim and Ventity. He created the Markov Chain Monte Carlo implementation in Vensim that facilitates Bayesian inference in System Dynamics models. He got his start in environmental models and simulation games, and worked on Fish Banks, updates to Limits to Growth, and early versions of C-ROADS and En-ROADS. Tom worked on data-intensive projects in a variety of settings, including consumer goods supply chains, mental health delivery systems, pharmaceutical marketing, state COVID-19 policy, and recently Chronic Wasting Disease in deer.

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

Q&A

Answers by Tom Fiddaman

Before launching into the written items, I’ll mention Jim Hines’ opening question, which was something like,

Q: What are the consequences of “assuming the model is right” when it turns out to be untrue?”

I think it’s nearly certain that a policy model will be wrong to a significant extent (despite Not Models Are Wrong). I think the facile answer here is that no model available to us will be perfect, and “no model” is not an option, so the best we can do is try to improve the models we have – and data comparisons help (at some cost).

I think I failed to give the most important part of the answer. When the model is wrong, hopefully, the problem will reveal itself through the poor fit to data, really wide uncertainty interval results, and other diagnostics. However, data by itself may be a weak test. I think the problem of overparameterized models that can fit anything is vastly overblown when the model is dynamic and nonlinear, but it can certainly happen. This is why other tests – units, extreme conditions tests, conservation laws, etc. – are so important.

Q: How to deal with structural uncertainty? (The uncertainty of how the real world could be modeled by us?) Making 100 model variations would take a lot of time 😉

100 variations would definitely be a lot of work, but it would be really cool if we could automate the generation and selection of these variations. One option would be to specify the behavior of stock-flow chains at a more granular level (in terms of the entities within) and then automatically generate different aggregate descriptions in terms of coflows, aging chains, etc.

We can’t do that yet, but in the CWD project, we did explore a number of variations: infection chains with and without age and sex structure, and with and without spatial detail and diffusion across geographic boundaries. We tried several variations from the 2nd order to the 44th order for the SIR chain. To some extent, you can do this with subscription (or entities in Ventity) – for example, you can build the model with a “county” subscript populated by real detail, but collapse that to an aggregate “all” county for experiments, without rewriting the equations.

Another facet of this question is that reality always contains some structure that we don’t model. This could be systematic (a missing feedback loop) or random (weather effects on the deer population). Particle filtering, including the special case of Kalman filtering, at least partially addresses this by moving the model state toward the data as the simulation progresses.

Q: How did you build a structure in Ventity to assess the evolution and “burn-in” of the parameters with MCMC? 

Ventity doesn’t yet do MCMC, but in Vensim there are at least four options. 1. Use the built-in PSRF diagnostic, which you can watch in the runtime error reports. 2. You can load the _MCMC_sample.tab or _MCMC_points.tab file generated as a dataset, and inspect the trajectories of the parameter values as well as the diagnostics. 3. You can load the same files in other software (Python/pandas, R, Excel, etc.) for inspection, visualization and diagnostics. 4. You can rerun the analysis with a different random number seed and compare samples.

We consider this an area of weakness, where the state of the art (e.g., in Stan) has advanced a lot, and expect to make substantial improvements in the coming year.

Q: How can we choose from different methods? Any criteria?

I think it’s hard to give a general answer to this – the answer depends a lot on the data, time available, existing tools you’re familiar with, and other nontechnical features.

Personally, I have a very definite preferred path:

  1. Build a model-data comparison control panel with some key parameters and experiment by hand.
  2. Start doing preliminary calibrations using loosely defined likelihoods and priors pretty early. At this point, just seek the maximum likelihood or posterior using Powell searches, in the interest of time and simplicity.
  3. As you learn about the model and the data, gradually transition to better likelihood and prior definitions and full exploration of the posterior with MCMC.
  4. Even if you don’t calibrate and use an MCMC sample to assess uncertainty, do multivariate sensitivity runs to see the distribution of outcomes from your proposed policies.

Q: “question of semantics on ‘forecasting’ the alternatives are more explicit but don’t they all involve looking into the future with a modeling approach which is forecasting by another name? Am I missing something here?

I think the short answer is “yes – it’s all forecasting” or perhaps better to say “prediction.”

Traditionally, forecasting implies that you’ll know the state of the system at some point in the future. If your goal is to predict the future and respond to it, that’s an open-loop strategy, with lots of pitfalls JWF warned against, rightly.

I think we’re seeking prediction more broadly. Even if we can’t know the future state of the system, we can make contingent predictions about the response of the state to our policies. Ideally, we’d like to formulate closed-loop decision rules that perform well under a variety of possible futures, i.e. they improve the system state, regardless of what it is.

Q: Were there any initiatives to create rapid tests and protocols for infected deer?  i.e. decrease prions in the field

Rapid tests would be a big improvement. One problem hunters face, for example, is that by the time test results arrive (a week or two currently), they’ve already invested the trouble and expense of moving and processing the deer. This also means prions have moved, and possibly been consumed. We didn’t test this option in the Phase 1 model, but it’s on the list for the next iteration.

An ideal test would let you spot infected deer on the landscape while they’re still alive, but this is probably a long way off.

Q: Regarding the Bayesian approach: Which distributions should be chosen (as a starting point) for discrete and continuous variables?

There are lots of situation-specific options, so it’s hard to give a general answer here. Probably referring to BDA is the best option (http://www.stat.columbia.edu/~gelman/book/ ).

By far the most common things I use are:

  • Normal, i.e. -((param-belief)/belief SD)^2/2 for location parameters that can take mixed pos/neg values, or just for convenience
  • LogNormal, i.e. -LN(param/belief)/belief SD for scale parameters like time constants or fractional rates of change
  • -LN(param) for an improper noninformative prior for scale parameters (a bit lazy usually)
  • Beta for fractions between 0 and 1. The PERT distribution might be an attractive alternative.

You can also use a lookup table to simply draw a distribution.

Q: If we use Mean Absolute Percentage Error (MAPE) as model evaluation/validation in comparing the System Dynamics Model output parameter with the historic data, in what % maximum of MAPE the model is good or valid? are 5% good as a limitation?

I think this can’t be answered in general, because the MAPE depends in part on how much measurement error is embedded in the data. If you predict the next roll of a fair six-sided die as 3.5, means your % error is at best 14% and on average something like 40%. That sounds terrible, but you can’t improve on it without cheating.

It’s possible to estimate the scale of the errors in the data, either a priori or as part of the calibration process. In that case, the uncertainty in your parameter and outcome estimates would reflect the quality of the data.

Generally, I would hesitate to rely on the goodness of fit metrics as the final word on model validity. There might be good reasons for the lack of fit to some features (for example, inessential features that you didn’t model) and it might also be possible for a bad model to nevertheless fit the data reasonably well. Still, it’s certainly a reasonable thing to pay attention to.

Even though there isn’t a general rule, I do use something like a rule of thumb in preliminary calibration work. If I don’t know the scale of errors in the data, I just assume it has a standard deviation of 10%. It can’t be 0%, because nothing is perfect. It probably isn’t 50%, because then no one would bother collecting it. Using 10% as a guess is often good enough for getting started.

Q: How did you stratify the SEQUENCE of actions? e.g. some upstream, preventive measures may have a significant impact on downstream outcomes.

For simplicity, most of the policy packages we simulated for stakeholders were “ballistic” in the sense that they don’t respond to changes. This was partly constrained by the 5-year horizon remaining in the current plan, which is fairly short compared to the disease evolution (we did run out to 2040 though).

There’s one important exception. Among the three representative geographies we simulated, one is a newly infected area, where the disease is present but not yet detected. For that situation, we explicitly model the testing process, tracking the composition and prevalence of harvested deer, and sampling them with random Binomial draws. This makes the discovery of the disease stochastic and dependent on the level of surveillance in the area. Other policies – baiting and feeding bans, accelerated harvest, etc. – only commence with discovery. This makes the effectiveness of surveillance dependent on the subsequent response, and the effectiveness of the response package contingent on the adequacy of surveillance, plus some luck.

There’s also some feedback from perceived disease prevalence to hunter participation in or compliance with control efforts. This isn’t strictly sequencing, but it does affect the future effectiveness of policies.

What role do predators play in the spread or containment of the CVD and how would this be reflected in the model structure?

This was certainly mentioned, but we didn’t model it explicitly. We do have a proxy, which is the ability to selectively harvest infected animals. There’s some reason to think that predators, and also to some extent hunters and sharpshooters, can do this. It’s extremely effective.

I think the basic challenge is that predator management is not a matter of reason, but rather a quasi-religious debate that’s almost untouchable for resource agencies.

Q: Is there any way we can import the algorithm in Vensim or Ventity?

I’m not sure what it would mean to import the algorithm, but there are some other options for doing this kind of work.

Q: Would the Kalman filtering approach just mentioned potentially run the risk of being misleading if the data referenced are lagged, or distorted in some way?

Certainly, this is always a possibility, and not only for the Kalman filter. Any calibration process that moves the model towards the data is subject to problems with the data. Lags are straightforward – you can model the lag explicitly so the model-data comparison is apples to apples. But often distortions will be unknown to the modeler. However, they’re likely to reveal themselves in poor fit or other distortions to model behavior, and bad uncertainty intervals on the parameter estimate. You can examine the residuals to find and reject data points that are particularly problematic, but of course, this requires a little care because it could be the model that is wrong.

Q: The time lags associated with data collection would, I think, create some distortions that would perhaps need to be accounted for or addressed

This is definitely the case. Reporting of deaths from COVID is a good example – they take weeks to months to trickle into the official statistics. So, you might model this with a stock-flow structure that lags the unobservable instantaneous death flow. Using something like a third-order delay is often a reasonable starting point.

CWD is a bit unusual, in that almost all the testing data arrives in one big annual pulse, during hunting season. This of course corresponds with a big spike in deer mortality. Ideally, the model would capture this, along with the spike in births in the spring. However, we currently gloss over these discrete time events and model things continuously.

Q: How did you generate permutations for the 80 action packages?

The stakeholder participants developed the action list, and the state implementation team did the final assembly into packages. Then we developed a spreadsheet that translated the qualitative descriptions of the action packages into model parameters.

The explosion into 80 packages wasn’t ideal – it arose from the curse of dimensionality: 3 geographies x 5 actions x 2 agents, plus some combinations. I think a purely model-driven process would have led to fewer.

Characterizing a large number of policies was a pain, but it did lead to some good discussions: What does “do nothing” really mean? What are the resource tradeoffs involved in implementing the same policy in regions with different characteristics?

Once we had the parameters describing the policies, it was pretty easy to automate running them all, using VenPy with the Vensim DLL (see the last question).

Q: Is there any data collection of SARS-CoV-2 (all subtypes) seropositivity in the white-tailed deer populations that you are testing for CWD positivity?  Do you have any reason to be suspicious of possible co-seropositivity for both covid and CWD in the deer? 

This didn’t come up, but there are certainly reasons to think that CWD-compromised deer would be more susceptible to other diseases.

Q: If you are modeling just one mode of behavior, instead of all of them, can these methods still be used? (E.g. modeling a cycle of a certain period where the real data has cycles of other periods as well as perhaps exponential adjustment type modes, etch). Do you filter the data in some way?

I can think of cases where it might be possible to aggregate or filter some dynamics out of the data. For COVID, for example, a lot of states didn’t test on weekends or at least didn’t report on weekends, so there were big gaps on Sat/Sun and a spike on Mon or Tue. If you aggregate to weekly reporting, that noise goes away, at the expense of introducing half a week of lag on average. For a lot of purposes that would be fine.

Generally, though my preference would be to introduce the unwanted or unmodeled features to the model as parameterized exogenous inputs. That way the model matches the raw data, and it’s easier to attribute what’s going on explicitly to the exogenous and endogenous features of the model.

Q: It would be good to get some videos or briefings about automating the modeling/simulation/policy analysis process with scripts. This is highly interesting but came short at the ISDC.

I’ll put this on my to-do list. There are some examples in the VenPy repository, like the SDM Consequence Model. Some images are here.

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Modeling for Improved Organizational Staff Diversity

Modeling for Improved Organizational Staff Diversity

We cannot all succeed when (more than) half of us are held back.

This slightly modified quote is from Malala Yousafzai, the courageous young woman who stood up for her right to be educated. It summarizes the ethos of presentations at the International Systems Dynamics Conference held in Frankfurt and online in July 2022 which focused on improving diversity within organizations. Systems Dynamics modeling is being used in various ways to understand the mechanisms by which more than half of the world’s populations are being held back, and to support evidence-based solutions for change.

In the first plenary, Jeroen Struben presented a model to explain why women chess players drop out of competitions in their late twenties, never to return. The data from the Netherlands showed that the presence of peers and role models, and the culture of the broader community were major explanatory factors. There is also a project that will look at women chess players with and without children, which is already finding that family caring commitments have a large impact on women’s decisions.

Suzanne Manning (disclaimer: that’s me) also highlighted the impact of caring responsibilities on women, on career progression in a social science research team. In a qualitative model of mechanisms that were holding women back, factors such as family commitments and expertise in ‘softer’ science disciplines like sociology and indigenous knowledge (compared to expertise in more quantitative systems dynamics), were partially career-limiting.

The model of Inge Bleijenberg looked at mechanisms to explain why ‘ingroups’ of white, upper-class men hold a pay advantage over ‘outgroups’ (everyone else) in academia. Her model showed that while the human capital of both groups was quite similar, the ingroups made more and higher wage claims which were more likely to be accepted. This model shows how structural bias is built into our systems.

Several presentations addressed systemic bias, with models that were used as heuristic tools for organizations to make changes to increase staff diversity. A common theme was that organizations needed to be shown the things that were within their control and to realize that business-as-usual was not good enough to make a difference. Systems Dynamics models were key for getting organizations to make these mental shifts. Amin Dehdarian from EDGE had a process for gender targets set within a framework of representation, pay equity, policies and practices, and organizational culture. Systems Dynamics models were used to show how effective the strategies could be. A similar approach was taken by Hugo Herrera, who used microworld simulation models to help organizations develop a coherent suite of strategies for decreasing their gender pay gaps. Ivan Taylor, Takuma Ono, and Saraj Koul presented their case study of a model for increasing diversity in organizations applied to Twitter that have a vision of 25% of their US staff being from disadvantaged groups by 2025. Like the other models mentioned in this post, their data shows that improving fairness and diversity in recruitment and promotion are key aspects for improving diversity in the organization. They do acknowledge that their model does not currently account for the intersectional nature of disadvantage, which is future work for them.

All of these models have been used to gain a greater understanding of why some people in our organizations are held back, not because of their skills, knowledge, and experience, but because of their demographic characteristics and the systemic bias that goes with it. Systems Dynamics has been used in these cases to explain, spark discussions, and generate solutions. With these tools to hand, perhaps we can all succeed in this world, rather than just a select few.

  • Gender segregation dynamics: Women participation and performance in competitive chess in the Netherlands. Presenter: Jeroen Struben.
  • Recognizing systemic gender bias: Career advancement case study in a science team. Presenter: Suzanne Manning.
  • Gender and ethnic pay inequality in academia: A formal systems dynamics mode. Poster: Inge Bleijenbergh.
  • System dynamics modeling to set effective gender targets. Poster: Amin Dehdarian.
  • Tipping the scales: Using microworlds to uncover systemic issues driving organizations’ gender pay gap. Presenter: Hugo Herrera.
  • A System Dynamics Model to assist leaders to increase diversity in their organizations applied to Twitter’s 25/25 vision. Presenters: Ivan Taylor, Takuma Ono, Saroj Koul

 

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

Is System Dynamics the Missing Subject in our Educational System?

Is System Dynamics the Missing Subject in our Educational System?

Is System Dynamics so valuable that we should encourage its inclusion in our educational system? This year, presentations at International System Dynamics Conference (ISDC) supported this hypothesis. The President of System Dynamics Society, Shayne Gary, noted a growing trend in appreciating systems thinking as a useful tool to explain complexities. The ever-expanding requirement to cope with a complex world opens room for increased utilization of System Dynamics in our daily life. The power of the method was proved in the global study “Limits to Growth” fifty years ago. Jorgen Randers reflected on this Anniversary by comparing the initial study results with today’s worldwide situation[1]. The project signals one of the most important features of System Dynamics, the capacity to describe phenomena and present their behavior in time, on a global scale.

“Fish Banks has a long history and has been successfully implemented in learning at pre-college, college, and adult levels.”

A major practical application of System Dynamics is as Interactive Learning Environment (ILE). Without the necessity to build a model, these applications allow direct visualization of the system, game playing, storytelling, or simulation of behavior. Notable are two general formats: desktop and online. Will Fisher used the System Dynamics game Fish Banks to explain environmental economics[2]. The students play actors in the fishery system, trying to maintain it. By changing the system parameters they were able to see how the system responds to the altered policies. Results show an easy understanding of the topic, increased awareness about the system, and ‘enthusiasm’ to play. Fish Banks has a long history and has been successfully implemented in learning at pre-college, college, and adult levels.

In another presentation, Juliette Rooney-Varga and her team studied how the use of the online simulation platform helps to change public opinion about climate change, especially at the level of public decision-makers [3]. In their work-in-progress study, they investigated, at which level readymade online interactive medium EN-Roads helps the transformation of insights and action of the politicians towards climate change. Although at the preliminary stage, the results brought some facts that simulation improves overall attitudes toward climate change.

“Lectures that utilize System Dynamics influence change in students’ thinking, enhance capacity to understand calculus, and increase skill in mathematical modeling.” Diana Fischer

Teaching by applying System Dynamics modeling is another practice. Diana Fisher presented work from her long experience in educating pre-college students[4]. She stressed how lectures that utilize the method influence change in students’ thinking, enhance capacity to understand calculus and increase skill in mathematical modeling. Fisher noted that modeling with System Dynamics is not learned quickly but requires support from the institutions and commitment by the learner.

Another approach to practicing System Dynamics in teaching was reported by Zimmermann[5]. She utilizes the participatory group model building method in her classroom environment and has developed instructions to teach System Dynamics through collaborative methods. The benefits of this approach are both learning the use of System Dynamics and also learning group dynamics and participatory processes.

“System Dynamics enhance understanding of complex macro-economic situations, increases soft skills, and improves analytical thinking with a possible wide range of applications with an overall positive impact at the social level.”

Programs to use System Dynamics have been developed at the country level. In Turkey, an environmental and climate change education program at the middle school level has been developed[6]. Students are taught via the direct application and changes in the System Dynamics model. The results are increased knowledge about the subject as well as a proactive attitude to create actions to fight climate change. The students reported interest in seeking environmental-friendly solutions and wanted to continue with the class during the next term. One activity within this program was an online platform for teachers to learn systems thinking and Systems Dynamics. In another case, David Wheat and the Ukrainian team highlighted experiences from the ten-year-long project to learn economics through System Dynamics[7]. The project developed capacities in Ukraine in cooperation with Bergen University. Activities were done in university settings, at the pre-college level, and at the National Bank of Ukraine. They also organized an annual conference, established a competence center, developed a system for scientific cooperation, and incorporated the program into the Ukrainian educational system. Now, without external aid, project members remain enthusiastic and continue the project. The outcomes proved that the use and teaching of System Dynamics enhance understanding of complex macro-economic situations, increases soft skills, and improves analytical thinking with a possible wide range of applications with an overall positive impact at the social level.

“Postponing the decision to involve System Dynamics in the regular curriculum is a loss of opportunity to improve the education of our children and the population in general.”

The 2022 ISDC showed System Dynamics as a useful tool to improve our teaching process with remarkable potential. This didactic instrument supports the learner to focus systemically on the topic and discover internal relationships that sustain or change behavior, expanding cognitive potential through visualizing the nonlinear problems in an array of feedbacks. Teaching System Dynamics extensively from childhood up through the academic levels could enhance children’s holistic understanding of real-world problems, increase their structural thinking capabilities and develop mathematical modeling skills. Our strategy should be to incorporate System Dynamics as a regular tool in our educational system, utilizing it in different formats and adapting the method to each topic. Holistic penetration of System Dynamics in our society was predicted by its founder Jay W. Forester and the method itself was created with the purpose to describe and explain the behavior of any system. Such a powerful method should be considered among the essential capacities for the new era of human development. Postponing the decision to involve System Dynamics in the regular curriculum is a loss of opportunity to improve the education of our children and the population in general.

 

Presentations: 

[1] Jorgen Randers: “From Limits to Growth to Earth for All – Overshoot and collapse in a 100-year perspective”

[2] W. Fisher: “Teaching the tragedy of open access: a classroom exercise on governing the commons”

[3] J. Rooney-Varga at all: “Can interactive simulation impact what policymakers say and do on climate?”

[4] D. Fisher: “A Model-Building Lesson on Global Warming & Potable Water Availability for a High School Science Class” and “Creating and Building System Dynamics Models From the News (Workshop)”

[5] N. Zimmermann: “Participatory modeling in an introductory systems thinking and System Dynamics class”

[6] M.C. Alibeyoglu et all: “An Educational Program Design: Environmental Education with Systems Thinking and the World Climate Game Project”

[7] David Weat et all: “Learning Economics with Dynamic Modeling in Ukraine, in Collaboration with Norway”

 

 

Changed!

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us

System Dynamics Focuses More on Sustainability Than the Sustainable Development Goals

System Dynamics Focuses More on Sustainability Than the Sustainable Development Goals

Register today to watch all 2022 conference recordings. Available until September 30!

The world is facing major global challenges that result in moving towards or beyond social and ecological tipping points and in the exacerbation of drivers of climate change, but also in the consolidation of simulation modeling and systems thinking to solve those global problems.

As a newcomer to the International System Dynamics Conference in Frankfurt 2022, I was intrigued by the great combination of topics, experts, and complex problems, but mostly by the large community of practitioners hungry to share and learn about Systems Dynamics. Full of energy, the entire conference had the intensity to encourage everyone’s interest and advance everybody’s practice.

Clearly, in the Anthropocene, there is a need to use integrative approaches to support transitions towards sustainability in general and the United Nations 2030 Agenda Sustainable Development Goals (SDGs) in specific. The acceleration and practice of systems thinking and modeling are fundamental for this and an increasing understanding of complex dynamical behaviors is at the roots of applied sustainability science. Sustainable and resilient socioecological systems maintain indefinitely into the future both human development and valued environmental functions.

Systems thinking and modeling are crucial for dealing with the complexity of our living world and its resources, and what better way to learn more about this than from practitioners of System Dynamics and sustainable development, which have progressed alongside for the last 50 years.

Systems Thinking and Modeling for the SDGs

In the Climate Change, sustainable drivers parallel session, the social tipping mechanisms for rapid decarbonization presentation by Sibel Eker, Assistant Professor at Radboud University and Research Scholar at International Institute for Applied Systems Analysis (IIASA), explained the Functional Enviro-economic Linkages Integrated Nexus (FeliX) model. The model simulates complex interactions among 10 global systems: population, education, economy, energy, water, land, food, carbon cycle, climate, and biodiversity and represents the modeling of indicators representing eight SDGs related to sustainable food (SDG 2), health and well-being (SDG 3), quality education (SDG 4), clean energy (SDG 7), economic growth (SDG 8), responsible consumption and production (SDG 12), climate action (SDG 13), and life on land (SDG 15). The FeliX model is one of the very few models of human-natural systems that cover feedback interactions of sustainability in one integrated framework suitable for SDG analysis.

In conversation with Professor Birgit Kopainsky from the University of Bergen, she highlighted the Millennium Institute’s Integrated Sustainable Development Goals (iSDG) model, a policy simulation tool that helps policymakers and stakeholders to make sense of the immense complexities of the SDGs. Due to the highly integrated nature of the model, most policies will impact more than one SDG. The model is categorized into environmental, social, and economic dimensions in computer-generated connectors that show extensively integrated sectors. Rather than SDGs per se, the model explores sustainability aspects, as it can simulate numerous policies simultaneously and map policy impacts across sectors.

Reaching The SDGs

The Sustainable Development Goals (SDGs) are a collection of 17 interlinked global goals designed to “provide a shared blueprint for peace and prosperity for people and the planet, now and into the future.”

The improving a population’s well-being parallel session presentation by Takuma Ono and Ivan Taylor showed how to conceptualize connections between SDGs based on network development by Le Blanc (2015) adapted to causal loop diagrams (CLDs). The SDGs alone give no clear understanding of causal interactions between SDGs, which can create difficulties for interpretation and implementation for any government.

Le Blanc’s work suggests that network modeling helps in identifying “extended” targets that are not necessarily core targets under any of the goals. The existence of the 169 targets turns what could have been a collection of unrelated goals into a system. However, they need to be adjusted to specific contexts and situations to avoid overly generic (but not actionable) statements about trade-offs and synergies. Therefore, the network modeling framework facilitates integrated thinking and policy-making. By using CLDs adapted from Le Blanc’s diagram, the model can include connections among all 17 SDGs, using the scores of the Sustainable Development Report[1]. By doing so, when the score of one SDG goes up relative to its initial value, it influences the target value of all indicators, as targets are collections of quantifiable indicators that underline each SDG. This means that there is an array of opportunities to assess different optimization goals that can be applied to any country in the world, provided an SDG score is available for calibration purposes.

Conclusion

Sustainability-related presentations at this year’s conference seemed to focus more on Sustainability as a societal goal than on specific studies about the Agenda 2030 and the SDGs. This presents the system dynamics community with the opportunity, and responsibility, to join in the global efforts to build a sustainable world. My expectation is towards more collaborations from different sectors and systems thinking approaches. That way our society might navigate the challenging interdisciplinary work required for genuine progress towards global sustainable development.

[1] Sachs, J., Lafortune, G., Kroll, C., Fuller, G., Woelm, F., (2022). From Crisis to Sustainable Development: the SDGs as Roadmap to 2030 and Beyond. Sustainable Development Report 2022. Cambridge: Cambridge University Press. Available at: https://dashboards.sdgindex.org/downloads

 

Recent Posts

How Food and System Dynamics Gave me A Career

How Food and System Dynamics Gave me A Career A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact. Papers mentioned: Transforming Food...

Documenting the Modeling Process

Documenting the Modeling Process Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily...

Upcoming Events

Recent Business cases

Twinings Uses System Dynamics Games to Enhance HR Capability

Twinings Uses System Dynamics Games to Enhance HR Capability “Realistic simulation is a powerful approach to building capability. The business simulation developed [by Dashboard Simulations and Lane4] gave [Twinings staff] an experience that called for them to develop...

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide

RSC Uses System Dynamics to Increase HVACR Sales Against the Tide “Using the proven Strategy Dynamic process focused our limited resources on organizing strategic issues, identifying the critical resources, and developing the insight to more rapidly create intuitive...

Achieving a Polio-Free World Through System Dynamics Simulation

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...

Join us