In this issue of Systems & Stories, we introduce CoModel.io, an AI-powered causal loop diagram (CLD) creator developed by SKIP: Strategic Design for Complex Systems. CoModel aims to accelerate and refine the process of building CLDs by leveraging AI to integrate insights from both expert knowledge and publicly available data. The tool is still in beta, but its potential for enhancing participatory systems thinking is already promising.
All CLDs in this issue were created using CoModel, showcasing its ability to generate meaningful insights into complex challenges. We invite you to try it yourself, experiment with modeling different systemic issues, and share your findings with us. Below, we provide a step-by-step script for using CoModel and five suggested starting points to guide your exploration.
Getting Started with CoModel
To help you begin, we have prepared a step-by-step guide (here) on how to use CoModel to create your own CLDs.
Best Practices for Problem Definition (Repenning, 2017):
Clearly define the gap: Identify the difference between the current state and the desired outcome, ensuring it is quantifiable.
Maintain neutrality: Avoid assumptions about causes or solutions.
Scope it appropriately: Keep it manageable so that meaningful insights can be developed quickly.
Use behavior-over-time graphs: These can visually illustrate the key variables and highlight systemic trends.
Five Starting Points for Your Own CLDs
Each of these topics presents a systemic issue with broad implications. Use CoModel to explore the feedback loops, drivers, and unintended consequences involved. We provide a few initial guiding questions to spark your investigation. Get the file here.
1. A Country’s Path to Independence
Many regions struggle to achieve full political and economic independence despite formal sovereignty. The gap between reliance on external support and self-sustaining governance creates challenges in financial stability, resource management, and political agency. This issue is influenced by trade agreements, historical dependencies, and economic policies that shape the long-term sustainability of independence.
What are the reinforcing and balancing loops that shape economic and political independence?
How do international trade agreements and debt influence sovereignty?
What role do cultural and historical narratives play in self-determination?
2. Forest Fires and Urban Resilience
The increasing frequency and intensity of forest fires pose significant risks to urban infrastructure, public health, and ecosystems. The gap between fire prevention measures and the growing impact of climate change exacerbates vulnerabilities in city planning and emergency response systems. Addressing this issue requires an understanding of the interactions between land management policies, climate dynamics, and socio-economic resilience.
How do deforestation, climate change, and urban expansion interact to influence fire risk?
What feedback loops exist between government policies, emergency response systems, and community preparedness?
How does air pollution from forest fires affect long-term urban health and migration patterns?
3. Water Shortages and Population Dynamics
Many regions face severe water shortages due to a mismatch between population growth and sustainable water resource management. The gap between water demand and available supply is widening, exacerbated by climate change, inefficient infrastructure, and competing sectoral needs. Understanding the systemic drivers of water scarcity can help develop strategies for conservation, policy reform, and equitable distribution.
How do urbanization, climate change, and agricultural demand contribute to water stress?
What balancing mechanisms (policy changes, conservation efforts) mitigate water shortages?
How does access to water drive regional migration and socio-economic shifts?
4. DeepSeek AI’s Effect on Wall Street
The increasing use of AI-driven trading systems, such as DeepSeek AI, is transforming financial markets, creating a gap between traditional market stability mechanisms and the rapid, automated decision-making of AI systems. The systemic risks of algorithmic trading include market volatility, reduced human oversight, and regulatory challenges. Investigating these feedback loops is essential for anticipating unintended consequences and ensuring financial resilience.
How might AI-driven trading strategies reinforce market volatility or stability?
What systemic risks emerge when AI outpaces human decision-making in financial markets?
How does AI impact employment, regulations, and innovation in finance?
5. Space Exploration and Innovation Cycles
The rapid acceleration of space exploration is creating new economic and technological opportunities, yet a gap exists between the rate of innovation and the long-term governance structures needed to ensure sustainable and equitable expansion. Competition among private enterprises, government agencies, and international actors drives both progress and potential conflicts, requiring systemic thinking to navigate these dynamics.
How do investments in space technology feed back into scientific advancements and economic growth?
What are the reinforcing loops between private-sector competition, government funding, and public interest?
How might space exploration shift geopolitical power structures?
Join the Experiment
We invite you to experiment with CoModel by modeling one of the above topics—or any other systemic challenge that interests you. Share your findings, diagrams, and insights with us, and let’s explore together how AI-driven CLD creation can enhance systems thinking.
Tag your CLDs with #SystemsThinking #CLDs #CoModel and connect with us for feedback and discussion!
Happy modeling!
Comments