My argument is that business cycles are best understood though the framework of Batesian mimicry, an endogenous mechanism for booms and busts thru a misallocation in the horizontal structure of production. In ecosystems, Batesian mimicry is typified by a situation where a harmless species (the mimic) evolves to imitate the warning signals of a harmful species (the model) directed at a common predator (the dupe). For example, venomous coral snakes have red, yellow, and black bands, while the non-venomous scarlet king snake has the same colors in a different order. Animals afraid of venomous snakes would do well to avoid 4 foot long snakes with red, yellow and black stripes, in the process avoiding the scarlet king snake (alternatively, one could remember the rule “Red on yellow, kill a fellow; red on black, friend of Jack”).
The process has been observed in insects, reptiles, mammals, and plants, and sometimes occurs between species. By parasitizing the true warning signal of the protected species, the Batesian mimic gains the same advantage without having to go to the biological expense of maintaining a poison. The species being mimicked, on the other hand, is disadvantaged, along with the dupe who misses out on tasty mimic meals. If imposters appear in high numbers, positive experiences by the predator with the mimic may result in the model species losing the benefits of signaling its poison.
Atsushi Yamauchi has shown that when there are density effects on the model species, there is no stable equilibrium. Nonlinear dynamics make the system’s aggregate features unpredictable in specifics, but most importantly, it is not a stable equilibrium to have no mimics over long periods of time: the gains are large to the mimic because predators obey the model’s high-quality signal.
While it’s conceivable one could generate a formal economic model with these qualitative results, note that the ecological literature mainly looks at comparative statics for one species, noting what assumptions generate stable equilibria, and which do not. There is no attempt to generate a dynamic model of the mimic or models success over time, presumably because the highly nonlinear, recursive system is so sensitive to results would merely be qualitative, like the comparative statics.
In an expansion investors are constantly looking for better places to invest their capital, while entrepreneurs are always overconfident, hoping to get capital to fund their restless ambition. Sometimes, the investors (dupes) think a certain set of key characteristics are sufficient statistics of a quality investment because historically they were. Mimic investors seize upon these key characteristics that will allow them to garner funds from the duped investors. The mimic entrepreneurs then have a classic option value, which however low in expected value to the investor, has positive value to the entrepreneur. The mimicry itself may involve conscious fraud, or it may be more benign, such as naïve hope that they will learn what works once they get their funding, or sincere delusion that the characteristics are the essence of the seemingly promising activity. The mimicking entrepreneurs are a consequence of investing based on insufficient information that is thought sufficient, but they make things worse because they misallocate resources that eventually, painfully, must be reallocated.
Once the number of mimics is sufficiently high, their valueless enterprises become too conspicuous and they no longer pass off as legitimate investments. Failures caused by insufficient cash create a tipping point, notify investors that certain assumptions were incorrect. Areas that for decades were very productive, are found to often contain exceptional levels of fraud, or operate with no conceivable expectation of a profit. Everyone outside the industry with excessive mimics marvels at how such people—investors, entrepreneurs, and their middlemen–could be so short-sighted, but the key is that the mimics and duped investors chose those business models that seemed most solid based on objective, identifiable characteristics that were, historically, correlated with success. An econometric analysis would have found these ventures a good bet, which is why investors did not thoroughly vet their business models (banks, up through 2007, were one of the best performing industries since industry data has been available in the US, and performed well in the 2001 recession).
In the 1990’s tech firms in general and internet firms in specific were doing very well. The internet bubble was filled with a naïve lack of skepticism that allowed otherwise absurd business ventures to get funding. Using hindsight there were so many businesses with doomed business models, you wondered how they could have been taken seriously, but investors were looking primarily at a few key criteria—net presence, branding—and these did work well for several years until the March 2000 crash, especially using the criteria of their stock price. Consider that Enron was able to engage in negative cash flow activities for at least 5 years while their stock price kept climbing, highlighting that if you hit the key signals investors are naively prioritizing, they can be fooled, just not forever.