I’ve heard students and colleagues say that learning system dynamics transformed their thinking–it gave them ways to understand complex problems in their field from a fresh perspective. “I can’t think any other way,” they’ll say. Practices like system dynamics are considered a threshold concept. Threshold concepts have been described by Meyers and Land (2008) as:
- Transformative – it changes the way a student views a discipline.
- Troublesome – especially when the concepts are counter-intuitive or conceptually difficult.
- Irreversible – difficult to unlearn.
- Integrative – once learned, bring together previously unrelated concepts.
- Reconstituted – shifts learner subjectivity in oscillations/wrestling with conceptual domains, often depicted by messy journeys back and forth and across conceptual terrains (Cousin, 2006).
- Liminal – leaves the learner in a suspended state of partial understanding (“stuck places”), in which understanding is based on mimicry or a lack of authenticity–it’s an uncomfortable shift that invokes questions of identity and or paradoxically a sense of loss. Similar to adolescence, one does not feel that they have arrived–not yet adults and no longer children.
Those of us who have started this journey know there is no end to learning and a sense of imposter syndrome that comes with trying. It’s not something you simply do over the summer and get your stamp of authenticity. Becoming a system dynamicist is a commitment to lifelong learning. Practicing. Feeling inadequate. The skill decay rate is high. And yet, the insights are transformational.
To those of us who are starting this journey of learning, we must wrestle with this liminality. I suspect that few of us feel that we have “made it” to the end of system dynamics learning. When compared to some well-established fields, such as physics or biology, ours is relatively new–a mere child in the many disciplines of methodological inquiry. There are many questions to be pursued and innovations to be encountered. It is intimidating to put oneself out there to grow, so with that, I offer some ideas that I’ve done or seen others do in the field:
- Seek mentorship. Find people whose work you admire, read their papers/projects and talk to them frequently. Ask questions. Take notes.
- Attend SD conferences. The best way to learn a new language is to be immersed in the field and everyday language: the annual conference. There are few places where you don’t have to explain causal loop diagrams or feedback (not in reference to a suggestion) to have a deeper conversation on practice.
- Find peers and ask “dumb” questions together. At my first SD conference, peers sat me down to conduct a gentle intervention prior to my first presentation. They said, “Saras, it’s System. Dynamics. Not SystemS Dynamics. One system. Many dynamics.” Novice crisis averted (somewhat). Thanks, Jill and Mary Jo!
- Read the classics and model them. The System Dynamics Review is a great place to start going deeper. Additionally, reading seminal texts and rebuilding models can help hone your skills. It’s especially illuminating to read “conversations” between folks in the field to understand what debates have persisted over time.
Are you at the threshold? Reach out and lean into this space of learning. For more seasoned practitioners, what worked for you? Use the comments below to share your wisdom!
Sustainable Water Management in Laikipia District (Kenya)
Sustainable management of natural resources is a vital concern in most countries and regions worldwide. In Laikipia District in Kenya, located at the foothill zone of Mt. Kenya, water is required in the upper zone for irrigation agriculture, horticultures and livestock production as well as for urban areas. In the lower zone water is required for wildlife and natural habitats.
In an earlier study (Gallati 2008) a system dynamics model has been developed to better understand possible dynamics in collective irrigation management focusing on the feedbacks between social mechanisms of collective action and the performance of the irrigation practices. In Laikipia, however, it turned out that this model was not applicable due to the fact that large immigration had taken place in the last decades preventing inhabitants from developing close relations of exchange and reciprocity, which had been key preconditions of this model.
A stakeholder workshop in 2009 revealed that the transition towards new water management practices is one of the key concerns in the area.
Based on these insights a system dynamics model has been developed to demonstrate the effect of new water management practices in different zones along the river reflecting the fact of varying rainfall and agricultural options from uphill to downhill zone and down to the plains. In particular the users can experiment with different options such as storage capacity, increase of water use efficiency, use of flood flow, adaptation of agricultural practices, etc.
in order to analyze the effect of these practices on overall production and income. As such it is envisaged to support local participants in adopting a river (basin) perspective. The usefulness of the model is being evaluated in a second stakeholder workshop in 2010. Based on this experience further model development will be evaluated. One option is to further develop the model into a tool for broader use in capacity building and training for sustainable water management in collaboration with local or international institutions.
The project is developed in collaboration with CETRAD (Centre for Training and Integrated Research in Arid and Semi-Arid Areas Development; www.cetrad.org) in Nanyuki, Kenya and is part of a larger research initiative on sustainable natural resources management. It is supported by NCCR North-South in Switzerland, which is funded by the Swiss Development Agency and the Swiss National Fund.
Contact and further information: Justus Gallati, Lucerne University of Applied Sciences email@example.com.
|Gallati J. 2008. Towards an improved understanding of collective irrigation management: a System Dynamics approach. [PhD Dissertation]. Berne Switzerland: University of Berne.|
Climate Change and Energy
|Client||National Commission on Energy Policy, Environmental Defense Fund, WAI|
|Authors/Consultants||Bassi AM, Yudken JS|
Four industries – iron and steel, aluminum, paper and pulp, and chemicals – account for nearly half of the energy consumed by U.S. manufacturing industries and over 10 percent of total U.S. energy consumption, making them highly vulnerable to volatile energy prices. Millennium Institute and High Road Strategies collaborated on three connected study commissioned by the National Commission on Energy Policy, the Environmental Defense Fund and AFL-CIO Working for America Institute (WAI) and developed with support from industry association organizations, to examine how increased energy prices associated with comprehensive and mandatory cap-and-trade climate policy proposals currently being considered by the U.S. Congress would affect the competitiveness of these industries in the long term. The studies also examined the industries’ capabilities and opportunities to mitigate adverse cost impacts and improve their economic performance under different climate policy scenarios.
In short, the findings strongly suggest that over the long-run, technologies are available to enable energy-intensive industries to achieve sufficient efficiency gains to offset and manage the additional energy costs arising from a climate policy. However, the authors also strongly believe that the industries analyzed will need additional measures that both mitigate these cost impacts in the short-to-medium term, and policies that encourage and facilitate the transition of energy-reliant companies to a low-carbon future, while enhancing their competitiveness in global markets.
Findings of these studies being circulated starting from Aril 2009 are substantially contributing to the debate on the introduction of climate regulations, both in the US and abroad.
|Yudken J.S. and Bassi A.M. (2009). Climate change and US Competitiveness. Issues in Science and Technology, Fall Issue.|
|Bassi A.M. and Yudken J.S. (2009). Potential challenges faced by the U.S. chemicals industry under a carbon policy. Sustainability 1: 592-611. Special issue on Energy Policy and Sustainability.|
|Yudken J.S. and Bassi J.S. (2009). Climate policy and energy-intensive manufacturing: the competitiveness impacts of the American energy and security act of 2009. High Road Strategies and Millennium Institute, February 2010, Washington DC, USA. Prepared for the Environmental Defense Fund (EDF).|
Energy Policy Analysis in Mauritius
|Client||Ministry of Renewable Energy and Public Utilities, Mauritius|
|Authors/Consultants||Bassi AM, Bainac K, Bokhoree C, Deenapanray P|
Under the leadership of the Ministry of Renewable Energy and Public Utilities of the Republic of Mauritius and with support from UNDP, the Millennium Institute (MI) has carried out an assignment on supporting formulation and evaluation of Mauritius’ longer term energy policy framework. The goal of this project is to empower the Ministry of Renewable Energy and Public Utilities, and the Government of Mauritius, with a flexible, integrated, dynamic and user-friendly uniquely customized simulation model that allows for the evaluation of energy policy proposals to make informed decisions on longer term policy planning. This model was jointly developed with a team of experts, including representatives from Ministry of Public Infrastructure, Land Transport and Shipping, the Central Electricity Board (CEB), the Electrical Services Division (ESD), the Mauritius Sugar Industry Research Institute (MSIRI), the Central Statistics Office (CSO), the Maurice Ile Durable (MID) Fund, the University of Technology Mauritius (UTM).
Because of its flexibility and ease of use, in addition to its integrated and dynamic nature, the Mauritius Model allows for a cross sectoral analysis of the impacts of the energy policy provisions, with simulations running from 1990 to 2025.
This is important when operating in such a rapidly changing environment and volatile time.
The project included continuous group modeling sessions and daily exchanges with key stakeholders, to end with a two-day workshop and with a presentation to the Deputy Prime Minister of the Republic of Mauritius.
Results of the analysis proved to be of considerable value to the Ministry of Renewable Energy and Public Utilities, and led to an update of the longer-term energy policy document later approved. The utilization of an integrated, cross sectoral, national development model also served to bring together several ministries, the private sector and universities to jointly analyze results, both opportunities and challenges, arising from the implementation of the energy strategy.
More information on this case can be found in Bassi A.M. (2009). Systems modeling of long term energy policy, Mauritius. Prepared for the Ministry of Renewable Energy and Public Utilities, Republic of Mauritius, and UNDP Country Office Mauritius and Seychelles, Port Louis.
Spatial Planning in Indonesia
|Client||Ministry of Human Settlements and Regional Infrastructures Development, Indonesia|
|Authors/Consultants||Radianti J, Tasrif M, Rostiana E|
In pioneering spatial planning management for metropolitan cities, the Ministry of Human Settlements and Regional Infrastructures Development of the Republic of Indonesia, started a project to build a computer simulation on spatial planning. The pilot project was situated in Semarang, the capital city of Central Java, Indonesia.
The aim of the project was to understand the impact of metropolitan growth and spatial planning on essential indicators of urban life such as economic growth, land usages and industry development. The impact of population growth was the central focus in this project. Population growth was explored to predict to which extent additional facilities such as lands for industry development and transportation infrastructure were needed.
The model showed that almost all policies directed at raising economic growth also increased the population number.
The conclusion of the project is that any policy in the field of spatial planning should anticipate effects on the population sector and mitigate planning accordingly.
The Dynamics of Climate Change: Understanding and influencing the planet’s future (October 8, 2013)
Presented by Andrew Jones, Co-Director, Climate Interactive
Presentation slides: Dynamics of Climate Change slides
Description: Learn how world leaders are using C-ROADS in global climate negotiations C-ROADS is an award-winning computer simulation that helps people understand the long-term climate impacts of policies designed to reduce greenhouse gas emissions. World leaders are using the model in global climate negotiations. In this interactive session, Andrew Jones, Co-Director of Climate Interactive, introduces participants to C-ROADS and describes how it can be used by others to understand and test their own scenarios or conduct real-time policy analysis. This webinar is the first in the Big Data, System Dynamics, and XMILE webinar series jointly sponsored by IBM, isee systems, and the OASIS XMILE Technical Committee.
The Official Website
climateinteractive.org is the official website that covers all information about this brilliant project including the latest news, simulators and learning tools, videos, etc.
The Issue You Tackled
Negotiations have failed even though scientiﬁc understanding of climate change and the risks it poses ha s never been stronger. The failure of global negotiations can be traced to the gap between the strong scientiﬁc consensus on the risks of climate change and widespread confusion, complacency and denial among policymakers, the media and the public.
What You Actually Did
The C-ROADS model is designed to address these issues and build shared understanding of climate dynamics in a way that is solidly grounded in the best available science and rigorously non-partisan, yet understandable by and useful to non-specialists, from policymakers to the public.
tracks GHGs, including CO2, CH4, N2O, SF6, halocarbons, aerosols and black carbon;
distinguishes emissions from fossil fuels and from land use and forestry policies;
allows users to select different business-as-usual (BAU) scenarios, or to deﬁne their own;
enables users to capture any emissions reduction scenario for each nation portrayed;
reports the resulting GHG concentrations, global mean temperature change, sea-level rise, ocean pH, per capita emissions and cumulative emissions;
allows users to assess the impact of uncertainty in key climate processes;
How to Work With The Model?
Video tutorials are available online to guide use
|Climate interactive: the C-ROADS climate policy model.||Download|
|Management flight simulators to support climate negotiations||Download|
|Communicating climate change risks in a skeptical world||Download|
|The Climate Scoreboard shows the progress that national contributions (INDCs) to the UN climate negotiations will make assuming no further action after the end of the country’s pledge period (2025 or 2030).||Scoreboard|
|World climate: a role-play simulation of climate negotiations||Download|
Did You Know?
A Big Boost for the Climate Summit
An editorial in the New York Times about the climate summit in Paris, mentions C-ROADS team analysis of Intended Nationally Determined Contributions (INDC). Please follow this link to read this article in the NYT.
Offers for Paris Climate Talks Would Reduce Warming by 1°C
Climate Interactive’s Climate Scoreboard analysis, produced in partnership with the Massachusetts Institute of Technology Sloan School of Management (MIT Sloan), shows that the intended nationally determined contributions (INDCs) put forward in advance of the UN climate talks this December make a sizeable contribution towards curbing global emissions and limiting warming. However, the offers need to be paired with further action if warming is to be kept below the 2°C target, avoiding the worst impacts of catastrophic climate change. Please see the full news release of their new analysis of the expected impact of the emissions pledges nations have made in the run up to Paris. The climate scoreboard is an embeddable widget that people can embed on their sites, blogs, etc. and will automatically update as analysis is revised when new pledges come in. The New York Times and in Science Magazine Science Insider (dated September 28, 2015) have pick up this story so far.
Climate Interactive announced the World Climate Project at a Back-to-School Climate Education Event at the White House.
The World Climate Exercise is a role-playing simulation game that puts teams, classrooms, and communities into the role of international climate negotiators to create a pathway to solutions that limit global warming. Through these simulation games, Climate Interactive plans to reach more than 10,000 people by December 2015, when nations will come together to finalize a global agreement on climate change in Paris. (Aug 2015)
Professor John Sterman and Climate Interactive featured in film “Disruption”
The film Disruption features incredible and informative interviews from scientists, activists and leaders—including Climate Interactive partner John Sterman of MIT. The film was released in advance of the People’s Climate March, the largest climate march in history, in the streets of New York City on September 21, 2014. (September 2014)
System Dynamics Application Award
The System Dynamics Applications Award is presented by the Society every other year for the best “real world” application of system dynamics. The Society awarded its 2013 Applications Award to John Sterman, Thomas Fiddaman, Travis Franck, Andrew Jones, Stephanie McCauley, Philip Rice, Elizabeth Sawin and Lori Siegel for their work Climate Interactive: The C-ROADS Climate Policy Model. To see the citation that was made by Brad Morisson at the conference, please follow this link. (Jul 2013)
Professor John Sterman wrote an article in Climate Progress
It’s a great short article by John Sterman articulating why it is crucial to “hold our feet to the fire” WRT +2C maximum global warming target (i.e., to promote carbon emissions mitigation), while being careful, skeptical and perhaps even averse to climate resilience initiatives (i.e., to avoid engaging in adaptation to climate change). This article is contemporary, and more relevant as each day passes by without a global commitment to limit climate damage to a level that adaptation becomes pertinent. Please follow this link to find the article. (Jul 2013)
|Name||Structural and Behavioral Effects on the Recycling Rate|
|Modelers||John Egil Nilssen and Maren Sylthe|
|Client/Participant||Waste Agency of Oslo, Norway|
The Issue You Tackled
One of the goals for the waste policy in the municipality of Oslo is to achieve a recycling rate at least 50 % from the household waste by 2018. In 2015 the recycling rate was 38 %. The result in 2016 was 38 % and this raises the question whether the objective of 50 % is realistic given the portfolio of means and actions that is used in the municipality today. The Agency of Waste Management in Oslo carried out a study to identify new ways and means to increase the recycling rate based on international published studies. The purpose of this work was to use recommendations from the modeling work, to implement these in different curbside collection schemes, and to quantify the effects on the recycling rate. The main aim is to give the Agency in Oslo sufficient information to conclude about the most cost-effective collection schemes and if it is relevant to change today’s scheme.
What You Actually Did
The work consists of five main parts.
First five different curbside collection schemes were designed and developed . These schemes consisted of different combinations of waste fractions and containers for recycled fractions and residual waste. The schemes were developed so they were mutually exclusive. From a decision point of view, the intention was that the decision maker has to choose one scheme, not a combination of schemes.
A system dynamics model was developed that could simulate a future recycling rate, based on data collected. This model consists of a traditional waste value chain and a structural and behavioral section that takes into account how the various schemes affect the public’s waste sorting behavior. Three structural components in the model are populated with data reflecting the scheme that is simulated. These data were collected during group work with specialists in the waste agency.
The collection schemes were simulated over a period of 15 years. The schemes were ranked by recycling rate and sensitivity analyses were run. The net increase in recycling was identified by taking the schemes’ simulated recycling rate and subtracting the recycling rate in 2015.
Then, as part four, the schemes were costed. Cost and income drivers were identified and the different schemes’ cash flow calculated. Cash flows were transformed into a yearly totals – subtracting the cost base in 2015 identified the additional cost for each collection scheme.
Finally, the simulated recycling rate and annuity were combined in two cost-efficiency figures. These figures gave the final ranking between the schemes. The cost-efficiency is the net increase in recycling rate compared with the additional cost.
The collection scheme ranked number 1 achieved a 50,9% recycling rate for the lowest costs. However, this scheme requires another combination of bins and waste fractions compared to the scheme Oslo uses today, and would require a major logistic change which will challenge the households and the waste agency.
The collection scheme most similar to the scheme Oslo uses today was ranked as number 5 and achieved a 46,8% recycling rate to the highest costs. This scheme is a gradual development of today’s scheme.
Sensitivity analysis shows that ranking of the collection schemes due to cost-effectiveness is inelastic and the simulated ranking between the schemes did not change within the structural sensitivity parameters that were used (+/- 50%).
This work also shows that an implementation of a new collection scheme needs new sets of managerial information that the Agency does not currently have. If Oslo finds it relevant to modify today’s collection scheme, 4 recommendations have to be agreed before the Agency starts the process of changing the collection scheme.
How to right-size your model
I once developed a relatively compact System Dynamics model to help a major chemical company restructure their plastics business. The model was well received by senior management but was not considered granular enough for accurately projecting the financial consequences of a strategic shift. The company had recently developed a rich dataset on the detailed costs of their operations, and they wanted these data incorporated into the model.
I spent several months expanding and deepening the model so that it could take these operational data into account—reflecting all the additional elements of structure the new data logically implied. I presented the expanded model again to the management team, who seemed overwhelmed by all its detail and clearly did not trust it as much as they did the original smaller model. This outcome surprised me: weren’t they the ones who had asked for the greater granularity in the first place?
In SD practice, we always face the question of how large and detailed a model should be. We cannot depend on clients to answer this question, because, as in the case above, they typically do not understand beforehand how excessive model detail can interfere with clarity and comprehensibility.
Moreover, a bigger and more complete model is not necessarily a more reliable and trustworthy model, unless much more time and effort are invested to develop the necessary supporting evidence and do adequate sensitivity testing. Doubling the number of variables in a model may lead, for example, to tripling or quadrupling the number of parameter values that need to be estimated and the amount of sensitivity testing that needs to be done.
How, then, can we “right-size” our models? After 40 years of doing SD, I still sometimes wrestle with this question, an indication that there is no simple or uniform answer. But I’ll try to explain my usual approach.
Let’s first recognize that size has two components: model breadth or boundary, and level of aggregation. I find that model boundary setting is usually not so hard. When first developing a model, I set the initial model boundary based mostly on what the clients have to say about potential interventions as well as outcome measures of interest. The boundary may change naturally over the course of a modeling project, growing larger as more issues are addressed, or sometimes shrinking as some variables are dropped for lack of relevancy or significance. Such changes in boundary are made with little trouble in most cases.
Level of aggregation, or the degree of “lumping”, on the other hand, is a more permanent decision, typically touching more parts of the model—and always causes me more concern. How many demographic categories should I consider in modeling population health and disease? How many sectors in modeling a national economy? How many product or service lines in modeling a company?
Clients are often used to seeing data broken out by multiple categories (as on a spreadsheet) and may push the modeler in the direction of greater disaggregation. But SD modelers need to think for themselves.
They need to ask whether there is any strategic distinction among the multiple categories; that is, any way in which the interventions under consideration may cause the various categories to change in their relative proportions or move in different directions. If the answer is yes, then disaggregation may be warranted; if no, then you can safely lump the categories together and deal in terms of weighted averages.
Data availability should also play a role in the aggregation decision. I often find that data are available in disaggregated form for a few of the key variables but not most of them. In this case, a decision to disaggregate will introduce more uncertainty about parameter values and could cast doubt on model results.
In sum, you should disaggregate only when it serves the model’s strategic purpose and when the disaggregation is broadly supported by evidence. If you get the right level of aggregation, the right size model will usually follow.