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Helpful prior learning and learning objectives
Helpful prior learning and learning objectives
Helpful prior learning:
Section 1.1.1 The economy and you which explains what an economy is and how it is relevant to students’ lives
Section 1.1.2 The embedded economy, which explains the relationship between the economy and society and Earth’s systems.
Section S.1 What are systems?, which explains what a system is, the importance of systems boundaries, the difference between open and closed systems and the importance of systems thinking
Section S.2 Systems thinking patterns, which outlines the core components of systems thinking: distinctions (thing/other), systems (part/whole), relationships (action/reaction), and perspectives (point/view)
Section S.3 Systems diagrams and models, which explains the systems thinking in some familiar information tools as well as the symbols used to represent parts/wholes, relationships and perspectives.
Learning objectives:
Distinguish network models from stock/flow, causal loops, and agent-based models
Explain how social networks shape human knowledge, cooperation, influence, opportunities and social tipping points
Explain the significance of a network’s structure for the speed of transmission, power relationships, and system resilience
Discuss the importance of networks and network models for regenerative economies
Have you ever wondered why some TikTok trends take over the internet while others disappear? A meme, dance challenge, or political message can suddenly be everywhere—shared and adapted by people worldwide. The short video describes a viral trend from 2010 called 'planking'.
But how does this happen? The key lies in social networks. Some people, like influencers, have thousands of followers, spreading ideas quickly. Others connect smaller groups, helping trends move from one community to another. Networks shape not just trends but also knowledge, cooperation, risks, and opportunities.
What is a network model?
What is a network model?
A network model represents how parts of a system—people, organisations, or ideas—connect. In social network models, nodes (often circles) represent individual people or groups, and links show relationships—friendships, work ties, or shared beliefs. Figure 1 shows a friendship network. Notice that the lines linking people are unlabeled, as network diagrams typically represent the same relationship among nodes, in this case friendship.
Some networks are dense, with strong, frequent interactions. Others are loose, with weaker ties between groups. Bridging nodes—people who link different groups—play a crucial role in spreading knowledge, opportunities, and influence as well as making networks more resilient to disruption. In Figure 1, can you spot the people who connect friendship clusters?
Figure 2 shows another network model, where countries are nodes, and trade relationships link them. Like social networks, economic networks shape how resources, products and ideas spread.
Figure 1. Typical social network diagram where nodes, representing people, form dense relationship clusters. You can hover over the diagram to see the relationship clusters.
(Credit: Flourish)
How do networks shape knowledge, cooperation, and influence?
How do networks shape knowledge, cooperation, and influence?
Humans don’t just learn through direct experience. We share knowledge across generations and cultures through networks. Ancient trade routes spread technologies, scientists collaborate globally, and social movements like Fridays for Future (Figure 3) use networks to amplify their message.
Strong networks make cooperation easier. Shared beliefs, trust, and norms don’t emerge in isolation—they develop through interaction. A discovery in one place spreads, is refined, and then influences new communities.
But knowledge and influence don’t spread evenly. Some people are central hubs, reaching many other people directly. Others work at the edges of networks, connecting diverse groups and introducing new ideas. Both roles shape how movements and innovations grow.
Figure 3. The Fridays for Future student protests are an example of how networks contribute to shared ideas and movements for change.
(Credit: licensed from AdobeStock)
Connections, influence, and social tipping points
Imagine each person in your class is asked to choose two people to sit next to. A 1930s study did this with children and mapped their choices (Figure 4). Some students were chosen by many, forming central nodes (white, large). Others had fewer or no connections (blue).
This study shows how network centrality affects influence (Figure 5). Well-connected people—like influencers—spread ideas faster. But trust matters too. A recommendation from a friend carries more weight than one from a stranger.
Figure 4. Who wants to sit next to whom in the classroom network? Lighter circles represent students most chosen by peers, dark blue circles represent those not chosen by anyone.
(Credit: Martin Grandjean, adapted from Jacob Moreno’s sociogram, CC BY-SA 4.0)
But centrality alone doesn’t explain how influence spreads. Network density, or how closely connected people are, also plays a role. In a high-density network (Figure 6a), where most people know and interact with each other, news and ideas move quickly. In contrast, in a low-density network (Figure 6b), where only a few connections exist between groups, information takes longer to spread and may not reach everyone.
Centrality and density are important, but some of the biggest changes to systems actually come from the edges. Small shifts in norms, if spread widely, can trigger social tipping points—moments when a new behaviour or belief spreads rapidly through society. For example, ideas about climate responsibility, once seen as fringe concerns, are now influencing business and government decisions worldwide.
Figure 5. Network structure with high centrality.
Figure 6. Two network structures, a) with high density and b) with low density
The strength of weak ties and the power of the periphery
Close friends (strong ties) provide support and trust. But weak ties—acquaintances or distant colleagues—bring new information and opportunities (Figure 7).
Social scientists found that people are more likely to find jobs through weak ties rather than close friends. Why? Strong ties mostly connect people who already know the same things, while weak ties bridge different groups, introducing fresh ideas.
The power of the periphery extends this idea. People at the edges of networks often drive innovation and change. Unlike those in the center, who reinforce existing structures, peripheral thinkers engage with diverse groups and experiment with new approaches.
Figure 7. Strong ties connect you deeply to a small group, but weak ties link you to new networks, expanding your access to information and opportunities.
Scientific breakthroughs often emerge from small research teams working at disciplinary margins. Many social movements start at the periphery, where marginalised voices introduce radical ideas that later shape mainstream thinking.
These shifts can lead to social tipping points—moments when a small but growing trend suddenly becomes unstoppable. A local regenerative business model, for example, may start as an experiment but, if widely adopted, can transform an industry.
Why is the structure of the relationships in networks important?
Why is the structure of the relationships in networks important?
Network structure determines how fast information spreads, who holds power, and how resilient systems are to change.
Small world networks (Figure 8a): These networks balance local and global connections, allowing information to spread quickly while keeping tight-knit groups. Think of how most of your friends know each other, but one or two have connections to different schools, cities, or online communities. These bridging nodes help link otherwise separate clusters, making the system both adaptive and efficient.
Scale-free networks (Figure 8b): In these networks, a few highly connected hubs hold most of the power, while the majority of nodes have only a few links. Social media platforms work this way—most users have a small audience, while influencers with millions of followers shape trends and discussions. This structure makes networks efficient but also unequal—if a key hub disappears, the system can be disrupted.
Random networks (Figure 8c): Here, connections form by chance, with no clear pattern. These networks are less predictable, meaning ideas spread unevenly. While they may seem inefficient, they allow for unexpected breakthroughs because there are no rigid power structures controlling information flow.
Hierarchical networks (Figure 9): These networks are structured top-down, where power and decision-making flow from higher levels to lower ones. Governments, corporations, and schools often follow this model. While this structure creates stability and order, it slows down communication and limits how quickly new ideas or feedback can move upward.
Figure 9. Example of an organisation that is structured hierarchically. This hierarchy has three levels: a top level, a middle (management) level and a bottom level.
Modular networks (Figure 10): Some networks form clusters, where people interact mostly within their own group but are connected to other clusters through bridging nodes. A sports team, a workplace, or an online fan community might function this way. If bridges exist between clusters, information and resources can flow efficiently, but without them, the network remains fragmented.
Figure 10. An example of a modular network with three bridging nodes.
Why do network models matter for regenerative economies?
Why do network models matter for regenerative economies?
Network structures shape who has access to knowledge, resources, and decision-making. In today’s degenerative economic systems, wealth and influence concentrate in central hubs, leaving many disconnected from essential opportunities.
To transition to regenerative economies, we need strong, cooperative networks that distribute wealth, income, influence and opportunities more evenly to support resilience. Change moves through networks—starting in smaller clusters and gaining momentum as ideas spread. Bridging nodes—people and organizations connecting different communities—expand new economic models from local initiatives to global movements.
Some existing network structures already support regeneration:
Cooperative networks—worker-owned businesses and fair trade organisations distribute power and resources more equitably.
Open-source networks—Wikipedia and Creative Commons enable free knowledge-sharing without centralised control.
Local trading networks—community currencies keep wealth circulating within regions rather than being extracted by large corporations.
Understanding how network structures and social tipping points interact helps us design economies that restore rather than exploit. By strengthening the bridges between communities, we can accelerate the spread of regenerative ideas and practices, ensuring they scale beyond isolated efforts to transform entire systems.
Activity S.7
Activity S.7
Concept: Systems
Skills: Thinking skills (transfer and critical thinking)
Time: Varies, depending on option
Type: Individual, pairs or small groups
Option 1: Has your school reached a social tipping point on smartphones?
Time: 30 minutes, or longer if you invite a member of the school administration
Assess the shift: In small groups, discuss whether attitudes and rules around mobile phones in your school have changed in recent years. Have policies become stricter? What triggered these changes?
Map the social network: Consider how different stakeholder groups—students, teachers, parents, administrators—interact on this issue. Who has the most influence? Have social networks (e.g. peer pressure, media discussions, policy shifts) played a role in shaping opinions?
Identify a social tipping point: Did a particular event or trend accelerate the change? What made this moment significant? How did resistance or support from different groups shape the outcome?
Engage the school community: If possible, invite a member of the school administration to discuss the decision-making process. What evidence was used to justify policy changes? Did student voices influence the decision?
Reflection: What insights does this case offer about how social tipping points occur? How do social networks and influence play a role in broader societal changes?
Option 2: What’s the role of the fringe in social movements?
Time: 30 minutes
Read the following article about the controversy over the role and effectiveness of radical protest actions for social movements. The article has multiple links to more detailed explanations of various arguments and social movement theories, if you are interested.
Throwing soup at the problem: are radical climate protests helping or hurting the cause?
To what extent do you think radical protest actions help or harm social movements?
Consider how the arguments on both sides of this issue are related to social networks. Discuss with a partner, in a small group or as a class.
Option 3: Identifying the system model
Time: 20-25 minutes
Figure 11 is the circular flow of income model. It is often used to represent the economy, though it ignores critical information about the embeddedness of the economy in social and ecological systems (Section 1.1.2).
Consider how the parts are distinguished, whether there are system parts/wholes shown, what relationships you see, and from what perspective this model has been drawn (DSRP framework).
How would you classify this model—stock/flow, causal loop with feedback, agent-based model or network model, or some combination? Why? Is there anything about the way it is drawn that makes it difficult to classify it?
To what extent does considering the type of model (which involves thinking carefully about relationships) give you some new insight on the economy?
Figure 11. The circular flow of income, a commonly-used basic model of the economy
(Credit: Irconomics)
Option 4: Identifying and interpreting the system model
Time: 40 minutes
The diagram in Figure 12 comes from the 2025 report from the World Economic Forum. The information about the risks was taken from surveys of business leaders. Here the colour and size of the nodes/parts have meaning, as do the thickness of the lines. The colours indicate different categories of risks: economic, environmental, social, geopolitical, and technological. Large nodes indicate a risk factor with an influence on many other risk factors. Thick lines indicate a strong relative relationship between the nodes.
Considering the system models you have learned about (stock/flow, causal loops with feedback, agent-based models, and network models), how would you classify this model? Why?
Given what you know about modelling, is there something that could be added to the diagram that would improve the viewer’s understanding of the information?
What can you learn from this model? Consider distinctions, system part/wholes, relationships, and perspectives. Develop three ideas and share them with a partner or in a small group. Discuss the diagram as a whole class afterwards, if possible.
Figure 12. The World Economic Forum’s 2025 Global Risks report model, showing the interconnected global risks.
(Credit: World Economic Forum, Global Risks Report 2025)
Checking for understanding
Checking for understanding
Further exploration
Further exploration
Leadership lessons from the Dancing Guy, by Derek Sivers - a fund 3 min video showing how one dancing person can lead to many, with commentary from a longer TED Talk about social networks and social tipping points. Difficulty level: easy.
Collective Learning at a Global Scale | Unit 6: Big History Project - a short video that explains the concept of collective learning (which involves social networks and powerful language) and why it’s so important for human societies. Part of the Big History Project materials. Difficulty level: easy
How to Citizen - a podcast exploring the perspectives and practices people are using to wield (networked) collective power to build a better future. Difficulty level: easy
Slow Ideas – An article in The New Yorker magazine exploring why some important innovations spread quickly while others take decades to gain traction. Through real-world examples, it examines how social interaction, trust, and human behaviour influence the adoption of new ideas. A thought-provoking read on systemic change and resistance to innovation. Difficulty level: medium.
The Evolution of Trust – An interactive game by Nicky Case that explores how trust forms and breaks in human interactions. Through simple simulations, it shows how cooperation, betrayal, and reciprocity shape relationships over time. A great way to understand how social rules influence cooperation. Difficulty level: medium.
What are Social Tipping Points - a short video explaining social tipping points and the importance of individual action in setting off positive social tipping points. Difficulty level: easy
How false news can spread - Noah Tavlin - A TED-Ed video about circular reporting and how it contributes to the spread of false news and misinformation.
Visible Lab Networks - a (social) system mapping template that students could use to map a network. Difficulty level: easy
The Systems Thinking Playbook – A practical guide by Linda Booth Sweeney and Dennis Meadows, offering hands-on exercises to develop systems thinking skills through understanding feedback loops, delays, and interconnected systems in an engaging way. It is widely used in education, leadership training, and sustainability studies. Difficulty level: medium.
Sources
Sources
Cabrera, D., & Cabrera, L. (2018). Systems thinking made simple: New hope for solving wicked problems (2nd ed.). Odyssean Press.
Centola, D. (2021). Change: How to make big things happen. Little, Brown Spark.
Christian, D. (2018). Origin story: A big history of everything. Allen Lane.
Elsner, M., Atkinson, G., & Zahidi, S. (2025). Global risks report 2025. World Economic Forum. https://www.weforum.org/publications/global-risks-report-2025/digest/
Grandjean, M. (2015, March 16). Social network analysis and visualization: Moreno’s sociograms revisited. Martin Grandjean. https://www.martingrandjean.ch/social-network-analysis-visualization-morenos-sociograms-revisited
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. https://doi.org/10.1086/225469
Harari, Y. N. (2015). Sapiens: A brief history of humankind. Harper Perennial.
Huang, C.-Y., Sun, C.-T., & Lin, H.-C. (2005). Influence of local information on social simulations in small-world network models. Journal of Artificial Societies and Social Simulation, 8(4). https://www.jasss.org/8/4/8.html
Sundell, A. (2022, February 11). Visualizing countries grouped by their largest trading partner (1960–2020). Visual Capitalist. https://www.visualcapitalist.com/cp/biggest-trade-partner-of-each-country-1960-2020/
Terminology (in order of appearance)
Terminology (in order of appearance)
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