Society is reaching a tipping point. The future remains not only uncertain but also seemingly unpredictable, however, using the science of self-organised criticality, the phenomenon describing how small events can create large ripples in networks, this may no longer be the case. In this piece, Dan Braha presents his physics-informed model of civil unrest and shows not only how we can use it to forecast riots and violent disorder, but how in using the ideas of self-organized criticality smaller movements can better work to topple oppressive regimes.
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Civil unrest has indelibly influenced history, whether erupting as a protest, riot, or revolution. These events, while profound, often appear as erratic responses to unique circumstances. But what if they could be predicted, much like the weather? New research suggests that the seemingly chaotic nature of unrest may, in fact, follow patterns that can be deciphered through the lens of physics.
Consider the moment when water freezes into ice or boils into steam—these are phase transitions, where a substance suddenly changes state after crossing specific thresholds. These transformations are not just physical phenomena; they occur in society as well. Stock market crashes, viral social media trends, and, most dramatically, outbreaks of civil unrest all share this “phase transition” characteristic: abrupt and sweeping shifts in behavior that ripple through a population.
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In self-organized criticality, tipping points are moments when a system on the brink of stability suddenly collapses into significant, unpredictable changes—often without any obvious warning.
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Picture society as a complex web of interconnected factors—economic pressures, political tensions, social networks, and communication channels—that bind individuals and regions across a nation. When a single element in this intricate web shifts, it can send ripples across the entire system, pushing society from a state of calm to one of intense disorder, where small protests rapidly escalate into large-scale unrest and violence.
The new study explores this theory through an extensive analysis of 170 countries over 70 years, looking for patterns in civil unrest that mimic these phase transitions. The findings were compelling: civil unrest does follow patterns similar to those seen in physical systems. These events are not random but instead are governed by identifiable rules that can be measured and, in some cases, predicted.
Central to this analysis is the theory of self-organized criticality, another concept borrowed from physics. This theory suggests that complex systems naturally evolve toward a tipping point where even minor disturbances can have outsized effects. In self-organized criticality, tipping points are moments when a system on the brink of stability suddenly collapses into significant, unpredictable changes—often without any obvious warning. Imagine a forest quietly accumulating dry leaves until a single spark ignites a wildfire, or tectonic plates slowly building stress over centuries, only to release it all in a massive earthquake. These scenarios illustrate how systems reach a critical threshold where even a small disturbance can lead to dramatic change. The sandpile model vividly illustrates this phenomenon: as grains of sand are added one by one, the pile becomes increasingly unstable until, eventually, the addition of just one more grain causes an avalanche. This model exemplifies how systems naturally evolve toward a critical state where they are susceptible to sudden shifts.
This behavior extends beyond natural environments. Financial markets, for example, can hover in a delicate balance, suddenly spiraling into turmoil from a rumor or modest policy change, causing severe disruptions and widespread economic instability. Similarly, ecosystems under the strain of human activities and climate change can reach tipping points where minor disturbances trigger widespread extinctions.
In societal contexts, factors like economic inequality or political repression can build over time until a tipping point is reached, sparking widespread unrest. A notable example is the Arab Spring, where long-standing grievances against authoritarian regimes, economic hardship, and social inequality reached a boiling point. The self-immolation of Mohamed Bouazizi, a Tunisian street vendor protesting police corruption and brutality, acted as the spark that ignited a wave of uprisings across the Arab world. These examples highlight the fragile nature of complex systems, demonstrating how, at their tipping points, even the smallest disruptions can lead to profound and far-reaching consequences.
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To simulate the spread of unrest, a model inspired by self-organized criticality was developed. The model conceptualizes a country as a square grid, divided into urban clusters connected by a network of social links established randomly across the grid. These connections enable unrest to propagate between clusters, capturing the intricate dynamics of social interactions. The model illustrates the recurring transitions from calm to disorder within a society, driven by probabilities: the likelihood that a peaceful region will become vulnerable to unrest, the chances that unrest will break out, and the speed at which it spreads to neighboring areas. By adjusting these probabilities, the model can replicate real-world patterns where long periods of calm are occasionally shattered by sudden and intense social disorder.
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This model uncovers a fascinating pattern: civil unrest is both predictable and cyclical... while some nations flare up roughly every two years, others take much longer to reach that critical boiling point.
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To track these shifts over time across the 170-country dataset, the study introduces a macro-level statistical model powered by a Hidden Markov Model—a sophisticated algorithm that estimates the likelihood of transitions between phases of unrest, each tied to varying intensities. This model uncovers a fascinating pattern: civil unrest is both predictable and cyclical. Societies oscillate between periods of low and high unrest, crossing critical stress thresholds in a manner consistent with the self-organized criticality model. The model even pinpoints the timing of these cycles, revealing that while some nations flare up roughly every two years, others take much longer to reach that critical boiling point.
One of the most intriguing outcomes from this macro-level statistical model is the development of a “civil unrest magnitude scale,” a tool designed to quantify a nation’s long-term susceptibility to unrest, similar to how the Richter scale measures earthquake intensity. When visualized on a world map, this scale provides a striking visual representation of unrest magnitudes, illustrating how these disturbances cluster geographically, spreading like a contagion across borders. This challenges the traditional view of uprisings as isolated incidents, instead suggesting that civil unrest is often a regional or even global phenomenon, driven by shared underlying conditions and magnified by the ripple effects of media and social networks. The ability to map and compare these magnitudes across different countries allows for the identification of regions at the highest risk. For example, the study highlights that nations in North-Central Africa and the Middle East exhibit markedly higher levels of civil unrest compared to more stable regions like Western Europe or Oceania, as in Fig. 1, offering vital insights for policymakers and global observers alike.
Fig. 1. The map displays the significance (p-value) of the local concentration of civil unrest in each country. Low p-values (p-value ≤ 0.1) indicate statistically significant levels of civil unrest in a country and its neighboring countries, while high p-values (p-value ≥ 0.9) indicate statistically significant low levels of civil unrest in a country and its neighbors.
Imagine forecasting societal upheaval with the same precision as predicting the weather. By analyzing subtle shifts in social, economic, and political conditions that precede detected phase transitions, these models could be adapted to function as an early warning system, alerting us when a society is nearing a breaking point. Governments and international organizations could monitor these indicators in real-time, identifying the first signs of unrest before they escalate into full-blown chaos. Think of it as a seismic monitor for social stability, providing the opportunity to intervene proactively rather than reactively.
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This research highlights the critical need to address the root causes of unrest—economic disparities, political disenfranchisement, social injustices—before they reach a tipping point.
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This predictive power isn’t just about stopping riots before they start. Authorities can use these forecasts to craft strategies that defuse potential crises by addressing vulnerabilities within social systems before they’re exploited. It’s about moving from firefighting to foresight, ensuring that low-intensity disputes don’t flare up into high-intensity conflicts.
But the implications go deeper. This research highlights the critical need to address the root causes of unrest—economic disparities, political disenfranchisement, social injustices—before they reach a tipping point. Ignoring these issues is like ignoring a ticking time bomb; it creates a deceptive calm that can be shattered unexpectedly, leading to more severe and widespread disruptions.
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In situations where authoritarian regimes suppress dissent, cultivating interconnected social networks can deliberately trigger phase transitions that challenge oppressive systems.
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International bodies like the United Nations and the OSCE could leverage these insights to assess and compare the resilience of different regions, guiding timely and targeted interventions to maintain global stability. By using the civil unrest magnitude scale, they can better understand where and when to focus their efforts, potentially preventing the spread of unrest across borders.
Interestingly, these models are not only useful for predicting unrest but also offer a framework for fostering positive social change. In situations where authoritarian regimes suppress dissent, cultivating interconnected social networks can deliberately trigger phase transitions that challenge oppressive systems. This approach enables civil society to harness the dynamics of unrest for progressive and transformative purposes. By understanding these dynamics, communities can turn unrest into a calculated tool, using controlled dissent to tackle deep-rooted issues before they blow up into bigger, more destructive conflicts.
However, do we really want to develop an algorithmic science like Isaac Asimov’s psychohistory or Steven Spielberg’s Minority Report capable of predicting societal upheavals or crime? While these tools might help guide humanity toward a more stable future, they come with serious risks. Governments could exploit these predictive technologies to interfere with social dynamics before they even unfold, ramping up surveillance, monitoring citizens, controlling communication networks, spreading disinformation, and enforcing strict preemptive policing. To counter these dangers, we need strong ethical frameworks and regulatory measures that ensure transparency, accountability, and robust data privacy protections.
Applying physics concepts to social phenomena provides a powerful lens for uncovering the hidden patterns that drive collective human behavior. The versatility of societal phase transition modeling is opening new avenues for understanding a wide spectrum of behaviors—from civil unrest to economic instability and the spread of viral disinformation on social media to the dynamics of terrorism—helping us navigate the complexities of our world more effectively.
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