Adaptive Cycle: Instability Lurks in Stability, Vitality Springs from Decay

In July 1945, democracy advocate Huang Yanpei traveled to Yan’an to meet Mao Zedong. The two had a long conversation in a cave dwelling for an entire afternoon, known as the famous “Cave Dialogue.” As for the specifics of their discussion that afternoon, and whether Mao Zedong made any promises regarding democracy, later generations can only rely on Huang Yanpei’s unilateral account, making confirmation impossible. However, a question Huang Yanpei himself posed during the meeting became a famous quote that has circulated to this day—

Huang Yanpei stated that in his sixty-plus years of life, discounting what he merely heard, what he witnessed firsthand was truly “its rise was swift and vigorous, its demise sudden and abrupt.” Be it an individual, a family, an organization, a locality, or even a nation, few have been able to escape this historical cycle [1].

This is the final lecture in the ‘Evolver’ series. Let’s not discuss how to escape this cycle, but rather the cycle itself. Please note the character “忽” (hū) that Huang Yanpei used.

This is by no means a frivolous remark. The transition of the glorious Tang Dynasty from prosperity to decline occurred precisely at the zenith of its Kaiyuan and Tianbao eras. A large organization often isn’t destroyed in its nascent stages but rather fails during its mature phase, when processes are most refined and division of labor most precise. A forest isn’t destroyed when saplings are sparse but at its peak, when it is lush, verdant, and its canopy covers the sky.

Why is collapse always “sudden”? How does a perfectly functioning system suddenly collapse when it appears most stable, most prosperous, and least likely to encounter problems?

Because it is when systems are at their most mature, most efficient, and most stable that they quietly accumulate their greatest vulnerabilities.

In this lecture, let’s approach this from a macro-narrative perspective, using a mental tool called the “Adaptive Cycle,” which may help you better understand the Buddhist concepts of “formation, abiding, decay, and emptiness.”

The Adaptive Cycle: A System’s Evolutionary Trajectory #

Previously, we discussed the S-curve, primarily illustrating how a venture or technology progresses from inception to maturity, then to its peak, and how one should proactively initiate a second curve before reaching that peak. However, what if a system is too large, or fundamentally lacks autonomy, making it unable to proactively initiate a second curve? In that case, it will witness the outcome Huang Yanpei described.

But here, we will tell a more complete story.

The Adaptive Cycle is an ecological model proposed in 1986 by Canadian ecologist Crawford “Buzz” Holling—whom we mentioned earlier when discussing “slow variables” [2].

Holling subsequently extended this idea to economic and social systems [3]. While merely a heuristic framework and metaphor, it serves perfectly as an insightful framework for observing forests, businesses, cities, institutions, and even an individual’s knowledge system: many complex systems do not progress continuously along a straight line but rather cycle through four phases:

r (exploitation) → K (conservation) → Ω (release) → α (reorganization) → and another round of r (exploitation).

The choice of these letters is not arbitrary; they all stem from ecological concepts—“r” represents fast-reproducing opportunists, “K” is the carrying capacity limit of the environment, while Ω and α are simply borrowed from the last and first letters of the Greek alphabet, one symbolizing an end, the other a beginning. Holling depicted these four steps as ∞, a horizontal figure “8” [4].

Chinese people will immediately associate this with the Buddhist concepts of “formation, abiding, decay, and emptiness.”

• Exploitation (r), is “formation”: new things emerge, seize space, and grow wildly.
• Conservation (K), is “abiding”: capital accumulates, rules solidify, efficiency peaks, and everything stabilizes.
• Release (Ω), is “decay”: old structures disintegrate, and everything accumulated over a lifetime is dispersed.
• Reorganization (α), is “emptiness”—please note, “emptiness” here does not mean nothingness, but refers to the transitional stage where the old structure collapses, a new pattern is yet to be established, and everything is reshuffled.

The S-curve we discussed earlier tracks the growth and peaking process of single indicators such as user count, performance, or output. The adaptive cycle, however, tracks the structural characteristics formed by the system during its growth, behind these indicators. Our concern is no longer the height of indicator growth, but the state evolved by the system itself.

What does this look like?

If you only focus on the peak of the S-curve, you will only see that it has lost its growth rate; but once you gain insight into the system’s structure, you will find that entering the conservation phase (K) means the system begins to… lose options.

It may still believe it’s taking the most effective path, unaware that at this point, it has only one path left.

And people around it often misinterpret “only one path left” as “this path is the most reliable.”

From Exploitation to Conservation: Instability Lurking in Stability #

Let’s first explore the first half of the cycle—from exploitation (r) to conservation (K). The system is progressively stabilizing; however, it is precisely within this stability that seeds of instability are sown.

The exploitation phase relies on diversity: multiple modes of survival are tried in parallel, with the fittest prevailing. The conservation phase, conversely, focuses on standardization: finding the most effective path and dedicating all resources, manpower, and attention to maximize efficiency.

Consider an open space after a forest fire: initially, everything revives, with herbs, shrubs, and young trees competing for growth. Once the forest matures, sunlight and nutrients are dominated by a few tall trees. Companies are similar: during their startup phase, any product or method can be tried; but once they scale up, processes and division of labor become progressively fixed.

This perfectly illustrates the shift from “exploration” to “exploitation.” As exploitation deepens, connections become tighter, efficiency increases, and the system becomes more proficient at handling familiar problems. All of this appears positive.

However, its adaptability to unfamiliar problems simultaneously becomes increasingly dulled.

Tight connections are a double-edged sword. Information, resources, and instructions transmit faster; however, a local failure can also quickly spread throughout the entire system via these tight connections. A highly optimized production chain with only one supplier is incredibly efficient in normal times; yet, once a critical node is interrupted, the entire chain grinds to a halt.

For example, modern supply chains universally uphold “Just-in-Time (JIT)” management as a golden rule, investing years in continuous optimization, pushing inventory and buffers to their limits, achieving miracles of efficiency… However, the impact of the 2020 COVID-19 pandemic instantly transformed this efficiency machine into a fault conveyor belt—a single port shutdown could lead to factories across half the globe ceasing operations while waiting for a single screw.

In machine learning terms, systems in the conservation phase suffer from a kind of “overfitting” sickness—they have memorized past exam papers perfectly, consistently scoring full marks; but once the question format changes, they become helpless.

However, can we truly prevent systems from overfitting? When further efficiency improvements are possible, who would voluntarily forgo such gains? You cannot. The progression from exploitation to conservation is not the result of a single individual’s mistake, but the cumulative effect of countless localized optimizations. The most dangerous aspect of the conservation phase is that the system doesn’t stop optimizing; rather, it cannot stop optimizing.

The Kaiyuan and Tianbao eras marked the peak of conservation for the glorious Tang Dynasty—with vast territories, a booming population, and myriad nations paying tribute, the Great Tang was then the most mature, confident, and seemingly unproblematic system in the world. Yet, An Lushan rose in rebellion.

In the 14th year of Tianbao, which is 755 AD, the official registered households of the Great Tang numbered approximately 8.91 million. After the outbreak of the An Lushan Rebellion, by the 3rd year of Qianyuan under Emperor Suzong, or 760 AD, the countable households dwindled to just 1.93 million [5]. In merely five years, the registered population on the imperial records plummeted by nearly 80%. This not only included a large number of deaths but also reflected the failure of the court’s statistical and control capabilities—the records still existed, yet the court could no longer count its own citizens [6]. That mature system, which once managed everything with pristine clarity, under impact, “suddenly” lost its ability for self-cognition.

Part of the riddle behind the character “忽” (hū) in Huang Yanpei’s words lies precisely here.

Like a pandemic, An Lushan was merely a trigger. The trigger determined the day of collapse, but it was decades of accumulated rigid structures that determined why the collapse was so severe. Did An Lushan truly possess such immense capabilities? It was the prosperous era itself that had long trained itself into a system capable of handling only peaceful times.

This is what is meant by instability lurking in stability.

From Release to Reorganization: Vitality Springs from Decay #

Now let’s look at the latter half of the cycle—from release (Ω) to reorganization (α), from decay to emptiness.

A dynasty falls, a company goes bankrupt, a forest burns down… However, “decay” and “emptiness” are not the end of the story, for things cycle and recur.

One of the most counter-intuitive insights of the adaptive cycle is that many truly new things are precisely conceived and born in the latter half of the cycle. Collapse is not necessarily the opposite of creation; it is often the gateway to it.

If you call it “decay,” it’s a value judgment; but if you call it “release,” it’s merely a factual one. We can also refer to this as “creative destruction,” a term coined by economist Joseph Schumpeter [7]. The capital accumulated by an old system doesn’t necessarily vanish completely in its collapse; much of it is merely released from the old structure: trees burnt in a forest fire return nutrients to the soil; employees of a bankrupt company flow into other organizations; a failed project leaves behind code, data, and an experienced team.

Collapse liberates resources from the shackles of old structures.

If “emptiness” is understood as “reorganization,” its meaning is no longer nothingness but rather the creation of space: as old things dissipate, space becomes available, providing possibilities for new things to grow. Emptiness is not a dead end but an opening. The cleaner the old structure is cleared, the greater the possibilities for a new order.

Therefore, “emptiness” is not the end of the cycle, but the pause that fosters new life before the arrival of the next “formation” (i.e., “exploitation”).

As the saying goes, “Wildfire cannot burn them all; a spring breeze blows, and they are born again.”

This is what is meant by vitality springing from decay.

Panarchy: The Interplay Between Cycles #

Thus far, we have outlined a complete adaptive cycle: exploitation → conservation → release → reorganization. In reality, however, cycles often do not complete and restart independently and cleanly; instead, multiple cycles operate simultaneously, forming a complex pattern where large cycles nest several smaller ones.

For instance, a forest is embedded within regional climate, a company within its entire industry, an individual within their era—cycles of various sizes, speeds, and layers, nested one within another. Holling named this structure “Panarchy” [8].

Between cycles of different scales, there exist some interesting interaction patterns.

One is when a small cycle ignites a larger one. Typically, grander and slower scales constrain smaller and faster ones; however, at critical moments of crisis, the release from a small scale can cascade upwards, breaking through the existing stable state of the larger scale—this is a “lower overcoming higher” phenomenon, which Holling calls “revolt.” A single spark can ignite a massive wildfire, altering an entire mountain forest; a liquidity problem at one financial institution can trigger a run on the entire financial network.

The other half of the answer to the character “忽” (hū) in Huang Yanpei’s words lies precisely here: collapse at a small scale can cascade upwards along the nested structure.

The second is when a large cycle preserves “memory” for the regeneration of smaller cycles. The surrounding older forests that weren’t consumed by fire determine the recovery path of scorched earth after a major blaze; a nation’s laws and culture, in turn, dictate the appearance of a city’s post-disaster reconstruction. This is “memory,” which provides fundamental materials for rebirth without predetermining its specific form.

Understanding this, we realize that collapse is neither an isolated event nor a direct reset to zero; nor does new life begin from nothing. The larger, slower scale you are part of will always preserve some “memory” for you.

This is a great comfort.

Pursuing Resilience, Not Stability #

If “formation, abiding, decay, and emptiness” are unavoidable destiny, if “decay” isn’t entirely bad, and “emptiness” isn’t nothingness, then rather than clinging to the pursuit of stability, it is better to pursue resilience.

“Stability” means not collapsing, maintaining the status quo, and quickly returning to the original state even after slight disturbances. Stability focuses on “how quickly one can bounce back to the starting point.” It’s like a roly-poly toy that, when pushed, sways a few times and then returns to its center. Systems in the conservation phase vigorously pursue this kind of stability—avoiding errors, rapid recovery, and reverting to familiar order [9].

Ecological “resilience,” however, does not presuppose the system remaining unharmed: it allows the system to endure violent shocks and undergo reorganization; as long as it retains its basic functions, structure, identity, and feedback mechanisms after reorganization, the system still possesses resilience [10].

For instance, a clear, shallow lake typically recovers quickly after winds and waves, with water plants, fish populations, and transparency remaining unchanged for years, appearing very stable. However, once nutrient salts slowly accumulate to a critical threshold, the lake might suddenly flip from a clear-water state maintained by water plants to a turbid-water state dominated by algae [11]. This is an example where the local state appears stable, but its resilience is already extremely low.

Consider the capital market. It has endured countless financial crises and market crashes—indices plummeting, institutions failing, trading rules and regulatory frameworks forced to be rewritten. Yet, after repeated reorganizations, though the market has evolved, it still maintains its fundamental characteristics, continuously providing financing, pricing, and capital allocation services to the economy. It did not forcibly pursue rigid stability but preserved its identity and core functions. This is resilience.

Since cycles are unavoidable, rather than pursuing long-term, rigid stability, we should commit to enhancing system resilience.

The Wisdom of Cycles: From Pessimistic Determinism to a Resilience Perspective #

“Formation, abiding, decay, and emptiness.” Ordinary people often interpret these as a downward-sloping line, perceiving the melancholy of “all things will eventually decay, and everything will ultimately become empty,” or lamenting the impermanence of life and seeing through the illusions of the world, often with nihilistic undertones. However, if understood as an adaptive cycle, all of this can be calmly accepted. As the saying goes, “Reversal is the movement of the Dao”—this is simply a universal law of nature.

Furthermore, decay and emptiness are not entirely negative; without decay and emptiness, how could there be formation and abiding?

If you could travel back to the “Cave Dialogue” at this moment, perhaps you could explain this truth to Mr. Huang Yanpei—

From the perspective of the “Adaptive Cycle” (please note, this is an observation from systems theory, not a political science judgment), “escaping the cycle” is not an easily achievable goal for any complex system. It is not by finding the right method, replacing a wise ruler, strengthening supervision, or even establishing a democratic system that a political regime can enjoy lasting prosperity. The adaptive cycle reveals a structural tendency: as a system continuously optimizes, it often progresses from exploitation (r) to conservation (K); once in the conservation phase, it increasingly tends to sacrifice options for efficiency, treat redundancy as waste, and misinterpret “only one path left” as “the most reliable path.” Prosperity itself is the cause of decline. Therefore, “its demise sudden and abrupt” is not an accident but an inevitable structural repayment.

What we should strive for is not escaping the cycle, but building resilience. The Ming Dynasty fell, the Qing Dynasty rose and fell, yet Chinese civilization endures uninterrupted; the Qing Dynasty disintegrated, yet Chinese civilization still stands. This is resilience.

The United States, after 250 years since its founding, has not achieved perpetual peace and stability once and for all; it has likewise experienced countless crises and upheavals. The original intent of democratic systems was not to guarantee the long-term stable rule of any president, but rather a cycle of order and disorder with lower intensity and shorter periodicity.

There’s no need to hope that all things will never decay; what we should do instead is embed redundancy, leave room, and preserve some “seeds that cannot be burned away” within systems—so that when the next round of “formation” arrives, there is a place for them to take root and sprout.

【A Gatha to Prove It】

Towering to the sky, its roots decay unnoticed, One fiery blaze returns the mountain to dust. No enduring form exists in this world, Yet within the ashes, traces of spring are already conceived.

Notes

[1] Huang Yanpei: Yan’an Return, Chongqing: Guoxun Bookstore, 1945, pp. 64–65.

[2] Holling, C. S. “The Resilience of Terrestrial Ecosystems: Local Surprise and Global Change.” In Sustainable Development of the Biosphere, edited by William C. Clark and R. E. Munn, 292–317. Cambridge: Cambridge University Press, 1986.

[3] Holling, C. S. “Understanding the Complexity of Economic, Ecological, and Social Systems.” Ecosystems 4, no. 5 (2001): 390–405.

[4] Holling, C. S., and Lance H. Gunderson. “Resilience and Adaptive Cycles.” In Panarchy: Understanding Transformations in Human and Natural Systems, edited by Lance H. Gunderson and C. S. Holling, 25–62. Washington, DC: Island Press, 2002.

[5] (Tang) Du You (compiled), Wang Wenjin et al. (collated and punctuated): Tongdian, Vol. 7, “Food and Money Seven: Historical Rise and Fall of Households,” Beijing: Zhonghua Book Company, 1988.

[6] Dong Guodong: History of Chinese Population, Vol. 2, “Sui, Tang, and Five Dynasties Period,” Shanghai: Fudan University Press, 2002, pp. 101–102.

[7] Schumpeter, Joseph A. Capitalism, Socialism and Democracy. New York: Harper & Brothers, 1942, chap. 7, 81–86.

[8] Holling, C. S., Lance H. Gunderson, and Garry D. Peterson. “Sustainability and Panarchies.” In Panarchy: Understanding Transformations in Human and Natural Systems, edited by Lance H. Gunderson and C. S. Holling, 63–102. Washington, DC: Island Press, 2002.

[9] Holling, C. S. “Resilience and Stability of Ecological Systems.” Annual Review of Ecology and Systematics 4 (1973): 1–23.

[10] Walker, Brian, C. S. Holling, Stephen R. Carpenter, and Ann P. Kinzig. “Resilience, Adaptability and Transformability in Social–Ecological Systems.” Ecology and Society 9, no. 2 (2004): 5.

[11] Scheffer, Marten, S. H. Hosper, Marie-Louise Meijer, Brian Moss, and Erik Jeppesen. “Alternative Equilibria in Shallow Lakes.” Trends in Ecology & Evolution 8, no. 8 (1993): 275–279.