Complexity and Constraints
How and Why Societies Fail
The historian Joseph Tainter spent decades studying why complex societies collapse. His answer was thermodynamic, but he did not use that language. Societies collapse, he found, when the returns on complexity fall below the cost of maintaining it (Tainter, 1988).
Complexity is expensive. Courts, hospitals, universities, supply chains, standing armies, and regulatory agencies all require enormous, continuous energy input to maintain. They are worth maintaining because they generate returns that benefit societies: security, health, education, trade, and coordination. When the returns on complexity begin to fall, as they always eventually do, the society faces a choice: simplify or find new energy inputs to sustain the existing complexity.
Throughout history, the second option of finding new energy was always chosen when available. The Roman Empire expanded until expansion was no longer profitable. Medieval European complexity expanded through agriculture and trade until those returns diminished. Industrial civilization expanded through fossil fuels. Each expansion solved the complexity problem temporarily by providing new energy subsidy.
We are now in the phase where the energy subsidy is declining faster than complexity is being reduced (Hall & Klitgaard, 2012; King & Hall, 2011). This is why maintaining existing systems such as roads, water infrastructure, healthcare, and education requires an ever-increasing share of shrinking surplus (Turchin & Nefedov, 2009). It is why the cost of everything keeps rising. It is why institutions that once seemed robust feel strained and brittle.
In other words, societies collapse when the returns on complexity fall below the cost of maintaining it. This is the physics of institutional decay.
The choices made in the next decade or two will determine whether the outcome is a managed descent toward a simpler but sustainable equilibrium, or an unmanaged one.
The distinction between managed and unmanaged matters significantly. The difference is not measured in whether decline happens, but in how steep the slope is, how much human suffering accompanies it, and what kind of civilization exists on the other side.
-
Hall, Charles A.S. and Klitgaard, Kent A. Energy and the Wealth of Nations: An Introduction to Biophysical Economics. Springer, 2012.
King, Carey W. and Hall, Charles A.S. “Relating Financial and Energy Return on Investment.” Sustainability 3, no. 10 (2011): 1810-1832.
Tainter, Joseph A. The Collapse of Complex Societies. Cambridge University Press, 1988.
Turchin, Peter and Nefedov, Sergey A. Secular Cycles. Princeton University Press, 2009.
Exploring Joseph Tainter’s Collapse of Complex Societies Further
What is Complexity?
Complex Systems In General
Complex systems are networks of many interacting components that produce emergent behaviors. Emergent behaviors are patterns and properties that cannot be predicted by examining the parts in isolation (Holland, 1995). Unlike simple or complicated systems, complex systems are characterized by nonlinearity, feedback loops, and sensitivity to initial conditions. A small change in one part of the system can cascade through the network in unexpected ways, producing outcomes disproportionate to their causes.
These systems exist across scales: weather patterns, ecosystems, immune systems, markets, brains, cities, and civilizations all exhibit the same fundamental dynamics. They self-organize without central control (Kauffman, 1993). They adapt to changing conditions. They exist far from equilibrium, requiring constant energy throughput to maintain their structure (Prigogine & Stengers, 1984). And crucially, they demonstrate path dependency, which means their history constrains their future possibilities (Arthur, 1994).
The Research Foundation
The formal study of complex systems emerged from multiple disciplines in the mid-20th century, coalescing into a coherent framework by the 1980s. Cybernetics and general systems theory laid early groundwork through the work of Norbert Wiener, Ludwig von Bertalanffy, and Ross Ashby, establishing that similar organizing principles operate across biological, mechanical, and social systems (Wiener, 1948; von Bertalanffy, 1968). Ilya Prigogine’s work on dissipative structures demonstrated how systems maintain order by continuously dissipating energy, a principle that would prove foundational for understanding all complex adaptive systems (Prigogine & Stengers, 1984).
The Santa Fe Institute, founded in 1984, became the intellectual center for complexity science, bringing together physicists, biologists, economists, and computer scientists. This interdisciplinary convergence produced key insights: Stuart Kauffman’s work on self-organization and fitness landscapes (Kauffman, 1993), John Holland’s genetic algorithms and adaptive agents (Holland, 1995), and Brian Arthur’s application of increasing returns and path dependency to economic systems (Arthur, 1994). Per Bak introduced self-organized criticality, which is the tendency of complex systems to evolve toward critical states where small perturbations can trigger avalanches of any size (Bak, 1996).
Network science, pioneered by researchers like Albert-László Barabási and Duncan Watts, revealed that many complex systems share common topological features: scale-free distributions where a few nodes have disproportionate connections, small-world properties where most nodes are connected through surprisingly few steps, and vulnerability to targeted attacks on highly connected hubs (Barabási & Albert, 1999; Watts & Strogatz, 1998). These structural features have profound implications for how systems respond to stress.
Ecological research contributed crucial concepts of resilience and regime shifts. C.S. Holling’s work on ecosystem dynamics introduced the adaptive cycle—a four-phase pattern of growth, conservation, release, and reorganization that appears across scales (Holling, 1973; Gunderson & Holling, 2002). Systems can persist in stable configurations for long periods before crossing critical thresholds, after which they rapidly reorganize into new states (Scheffer et al., 2001). These transitions are often irreversible; the pathway back requires conditions far different from those that triggered the collapse.
The energy basis of complexity became explicit through the work of Howard Odum, who developed energy systems language to map the flows sustaining complex organization (Odum, 1996). Maximum power principle and emergy analysis revealed that all self-organizing systems are fundamentally thermodynamic structures—their complexity is purchased with energy dissipation. This connects directly to the work of ecologist Charles Hall and systems ecologist Cutler Cleveland, who quantified how energy return on investment (EROI) constrains the complexity societies can support (Hall et al., 1986).
More recently, research on critical transitions and early warning signals has formalized how complex systems behave near tipping points. Critical slowing down, which is the tendency of systems to recover more slowly from perturbations as they approach thresholds, can serve as an indicator of declining resilience (Scheffer et al., 2009). Increased variance, flickering between states, and rising autocorrelation all signal that a system is losing its ability to absorb shocks (Dakos et al., 2012).
Complex Systems Under Stress
When complex systems face sustained stress, whether through resource depletion, environmental change, or internal contradictions, they exhibit predictable patterns. Initially, they often respond through intensification: increased connectivity, tighter coupling, more sophisticated coordination mechanisms, and higher information processing demands (Tainter, 1988). This adaptive response can work temporarily, but it carries costs.
Highly optimized systems become brittle. They lose redundancy and slack capacity (Perrow, 1984). They develop vulnerabilities to novel perturbations. Their maintenance requirements increase even as their ability to generate surplus declines. Eventually, the returns on additional complexity diminish and then turn negative. This is what researchers call decreasing marginal returns or the complexity trap (Tainter, 1988).
At this point, systems face a fundamental constraint: they cannot maintain their existing structure without energy subsidies they no longer command. The pathway forward splits. Some systems find new energy sources or reorganize at lower complexity with higher efficiency. Others enter cascading failure as subsystems that depend on each other collapse in sequence, their interdependencies transforming from strengths into transmission mechanisms for breakdown (Homer-Dixon, 2006).
This is the landscape of complex systems dynamics, which is the territory Joseph Tainter mapped specifically for the rise and fall of civilizations (Tainter, 1988).
For an accessible entry to complex systems dynamics, see Meadows, Donella H. Thinking in Systems: A Primer. Edited by Diana Wright. White River Junction, VT: Chelsea Green Publishing, 2008.
-
Arthur, W. Brian. “Increasing Returns and Path Dependence in the Economy.” University of Michigan Press, 1994.
Bak, Per. How Nature Works: The Science of Self-Organized Criticality. Copernicus, 1996.
Barabási, Albert-László and Albert, Réka. “Emergence of Scaling in Random Networks.” Science 286, no. 5439 (1999): 509-512.
von Bertalanffy, Ludwig. General System Theory: Foundations, Development, Applications. George Braziller, 1968.
Dakos, Vasilis, et al. “Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data.” PLoS ONE 7, no. 7 (2012): e41010.
Gunderson, Lance H. and Holling, C.S., eds. Panarchy: Understanding Transformations in Human and Natural Systems. Island Press, 2002.
Hall, Charles A.S., Cleveland, Cutler J., and Kaufmann, Robert. Energy and Resource Quality: The Ecology of the Economic Process. University Press of Colorado, 1986.
Holland, John H. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley, 1995.
Holling, C.S. “Resilience and Stability of Ecological Systems.” Annual Review of Ecology and Systematics 4 (1973): 1-23.
Homer-Dixon, Thomas. The Upside of Down: Catastrophe, Creativity, and the Renewal of Civilization. Island Press, 2006.
Kauffman, Stuart A. The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, 1993.
Meadows, Donella H. Thinking in Systems: A Primer. Edited by Diana Wright. Chelsea Green Publishing, 2008.
Odum, Howard T. Environmental Accounting: EMERGY and Environmental Decision Making. Wiley, 1996.
Perrow, Charles. Normal Accidents: Living with High-Risk Technologies. Basic Books, 1984.
Prigogine, Ilya and Stengers, Isabelle. Order Out of Chaos: Man’s New Dialogue with Nature. Bantam Books, 1984.
Scheffer, Marten, et al. “Catastrophic Shifts in Ecosystems.” Nature 413 (2001): 591-596.
Scheffer, Marten, et al. “Early-Warning Signals for Critical Transitions.” Nature 461 (2009): 53-59.
Tainter, Joseph A. The Collapse of Complex Societies. Cambridge University Press, 1988.
Watts, Duncan J. and Strogatz, Steven H. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393 (1998): 440-442.
Wiener, Norbert. Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press, 1948.