Thinking Beyond Science

Prices sometimes soar far beyond what can be logically justified. In many manias people buy not because the asset’s fundamentals make sense, but because they expect someone else will pay more later. This is the “greater fool” idea: you might know an asset is overpriced, but you buy anyway because you believe a greater fool will take it off your hands at a higher price.

Pyramid schemes are a stark example of that logic taken to its extreme. Varied in form, all pyramids rest on the same premise: early participants profit only if they recruit enough later investors to pay them. The structure is easy to model. Start with ten people who each recruit ten more; that yields 100 new participants. If each of those recruits ten more, the scheme needs 1,000 additional people, then 10,000, then 100,000, and so on. Very quickly the numbers explode until there simply aren’t enough potential recruits left. Because of this built‑in impossibility, pyramid schemes are inherently unsustainable and usually illegal. Yet the lure of rapid money for those at the top keeps scammers trying, especially where a large pool of potential participants exists; in China, for example, some schemes have grown to recruit more than a million people.

Financial bubbles are often less rigid than pyramids and therefore harder to dissect, but they too follow recognisable stages. Economist Jean‑Paul Rodrigue describes four main phases. First comes a stealth phase: specialist investors back a new idea. Next, the awareness phase brings a broader investor base; early entrants may take profits and cause an initial sell‑off. As the idea gains traction, media attention and public enthusiasm push prices into a mania. Finally the bubble peaks and falls in a “blow‑off” phase, sometimes with brief recoveries as optimism flares.

Those stages map neatly onto the lifecycle of an outbreak: spark, growth, peak, decline. One defining feature of bubbles is rapid, accelerating growth—what analysts term super‑exponential growth. Not only does buying activity increase, the acceleration of buying itself speeds up. Each price rise pulls in more investors, which further amplifies demand. Like an infection, the faster a bubble spreads, the sooner it consumes the remaining pool of susceptible people.

That comparison highlights a key difficulty in understanding bubbles: we rarely know how many people remain susceptible to the idea. Epidemiologists face the same problem in early outbreaks: if most infections go unreported, visible case counts understate the true spread and suggest fewer susceptible people remain; if most infections are reported, many remain at risk. Serological surveys (testing blood samples for past infection) can tell us how much of the population is already immune, constraining the outbreak’s potential. In finance, however, estimating susceptibility is trickier. Leverage lets investors borrow to buy more, obscuring how many people have truly “joined” the mania and making it harder to judge how far through the cycle we are.

Sometimes the signals of unsustainability are blatant. During the late‑1990s dot‑com boom, companies justified astronomical valuations with claims that internet traffic was doubling every 100 days. That narrative helped value infrastructure firms at hundreds of billions and encouraged wild investment in internet providers. But researchers like Andrew Odlyzko showed the internet was actually doubling roughly every year—an order of magnitude slower—so the growth stories underpinning those valuations were nonsense. Outlandish claims, when checked against the size of the population that could plausibly adopt the trend, often reveal fatal limits: there simply aren’t enough susceptible people to sustain the proclaimed growth.

Bitcoin provides a modern illustration of these dynamics. Since its 2009 debut, Bitcoin has experienced several sharp rallies and crashes. In December 2017, one Bitcoin neared $20,000 before falling to less than a fifth of that value a year later. Each peak involved a progressively larger pool of susceptible investors: tech‑savvy early adopters, then a wider investor class, then the mass market as newspapers and transit ads amplified awareness. The pattern resembled an outbreak moving from a village to a town and then a city. Interestingly, the gaps between Bitcoin’s historical peaks suggest the idea did not spread continuously and efficiently across groups; if the susceptible populations had been tightly connected, you would expect a single, larger epidemic peak rather than a sequence of smaller ones.

Practical lessons follow from viewing bubbles as epidemics. First, check the size of the population that must adopt an idea for current price levels to be rational. If valuations imply near‑universal uptake in a short time, that’s a red flag. Second, beware exponentially framed narratives—claims that usage or demand is doubling on implausibly short timetables often mask hype. Third, consider leverage: borrowed money can sustain a bubble longer and make its collapse sharper. Finally, note that media attention and visible price momentum are not evidence of sustainability; they’re signs the contagion is spreading.

Understanding bubbles as social‑contagion phenomena doesn’t let us predict exact peaks, but it does sharpen our sense of limits. Whether the story is a rigid pyramid or a looser financial mania, the core constraint is the same: there are only so many people left to persuade. When the susceptible pool runs out, the growth phase ends—and the greater fools who were expected to buy at ever‑higher prices are nowhere to be found.

Source : The Rules of Contagion: Why Things Spread – and Why They Stop by Adam Kucharski

Goodreads : https://www.goodreads.com/book/show/52949562-the-rules-of-contagion

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