Markets were meant to coordinate financial information. Their evolution parallels advances in information technology—yet today prices often diverge from the cash flows they’re meant to signal. As Ludwig von Mises observed, a market is “neither right nor wrong; it merely reflects what everyone thinks” (Mises 1949, p. 45). The modern debate asks: is decentralized discovery more efficient, or should a central authority impose “false prices” to steer outcomes? Although “animal spirits” are invoked to justify intervention, this chapter reframes downturns as episodes of systemic information loss. When false prices proliferate—via central-bank policy, fractional-reserve banking, or collective misperception—the market’s signal-to-noise ratio collapses, triggering the sudden, clustered errors we call recessions.

10.1 A Clustering of Errors

When all participants trust the same distorted signals, errors align (Bikhchandani et al. 1992). Crashes aren’t drawn-out repricings but abrupt mass revisions that occur the moment false prices collapse.

To see how these collective shocks unfold, we turn to the market’s pulse: volatility.

10.2 Volatility as Information Processing

Volatility measures shifts between “price-discovery states,” quantifying the flow of new information through markets. Under Bachelier’s framework, these states might appear independent (Bachelier 1900); in practice they form path-dependent chains, with each jump conveying fresh data about underlying value.

If volatility is information change, we can literally equate “time” in markets to price movement.

10.3 Trading Time

In physics, time equals spatial change; in finance, “time” is price change. Volatility thus becomes the rate of information processing. Central-bank interventions stretch calm periods only to intensify rebounds once the peg breaks.

But volatility alone doesn’t capture self-reinforcing loops—that’s reflexivity.

10.4 Reflexivity

Reflexivity is markets reacting to their own reactions. This feedback loop drives moves beyond fundamentals: each wave of volatility begets more, compounding convexity (see 10.6) and turning stumbles into panics.

10.5 2001 — Dot-Com Bust

The late-1990s tech frenzy peaked in March 2000; by October 2001, the Nasdaq Composite had fallen roughly 78 % from its high (Shiller 2000). Initial sell-offs spurred margin calls, triggering further declines in a classic reflexive cascade.

10.6 Monetary Entropy

Claude Shannon’s entropy—the measure of disorder in information—applies directly to money. As authorities flood the system with currency, each unit conveys less precise economic data. Hyperinflation thus resembles static noise overwhelming the true signal of scarcity.

As entropy rises, purchasing power—and price clarity—erode.

10.7 The Monetary Unit

Purchasing power equals the ratio of goods and services to currency supply. As supply outpaces goods, power—and the information content of money—falls. When money ceases to transmit reliable scarcity signals, it becomes noise, undermining markets.

10.8 Fractional-Reserve Banking

Banks create “new money” by lending beyond reserves, multiplying claims on the same base. Each additional loan note dilutes market clarity, injecting false prices and pre-seeding future misallocations.

10.9 Central Banking vs. Market Planning

If central planning outperformed markets, the Soviet economy would have triumphed—a counterfactual that never played out. Partial planning still warps price signals, replacing decentralized discovery with top-down mandates.

10.10 The Black Swan—The Inside Joke

Taleb’s “Black Swan” mocks finance’s reliance on normal distributions to model fat tails. The 2008 collapse wasn’t an anomaly but the inevitable outcome of treating non-ergodic systems like casino games. Believing in the false map of Gaussian risk invites systemic surprises.

10.11: What is seen and what is unseen

Frédéric Bastiat, in his essay “That Which is Seen and That Which is Unseen,” explains the concept of opportunity cost through the broken window fallacy. If a window is broken, the economy may be stimulated by the demand to repair that window — that which is seen. However, if the window hadn’t been broken, the shopkeeper could have spent the money on other capital goods — that which is unseen (the counterfactual).

Both Bastiat’s “unseen” and our computational-irreducibility insight point to the same blind spot: empirical data only ever records what happened, never what might have happened. In Bastiat’s broken-window example, you observe the glazier’s wage (the seen) but can never measure the forgone factory equipment or new business he didn’t buy (the unseen). Likewise, even a flawless pricing model only compresses supply–demand forces into tidy equations; it can’t conjure the counterfactual order flows, microstructure quirks, or reflexive feedback loops that determine real-world prices. Thus policy-makers who lean purely on economic statistics mistake the map for the territory—treating the seen snapshot as if it captured the full causal web. To respect the unseen, you must allow the system itself to run—letting markets generate the missing data via price discovery—just as sound policy must rest on theoretical counterfactuals as much as on recorded outcomes.

Under our information‐theoretic, Anti‐Symbolisation framework, any central‐bank intervention must pass two critical tests: does it preserve genuine price‐discovery, or does it merely plaster over reality with false signals? And can its visible benefits justify the opportunity costs and second‐order effects that lie forever beyond empirical measurement? Viewed through these lenses, non-intervention stands out as the least‐worst policy.

First, markets are themselves vast information processors, distilling every trader’s “unease” into prices. When central banks peg interest rates or unleash rounds of quantitative easing, they impose artificial floors under asset values. That dampens volatility—and with it the flow of new information—until the peg finally breaks and markets convulse in a far more damaging correction. By contrast, letting volatility run its honest course preserves the market’s pulse, guiding capital organically to its highest‐value uses. Second, each fresh tranche of money printing erodes the information content of the currency, raising what we call monetary entropy. Abstaining from intervention means each unit of money remains a sharper signal of scarcity and real risk, rather than a noise-drowned illusion.

Beyond these points, non-intervention respects the computational irreducibility of real economies: the cascade of leverage shifts, liquidity droughts, and cross-border flows that follow any policy move cannot be compressed into a tidy rule without losing critical “what-if” scenarios. It also breaks the reflexive lesson of ever-larger back-stops—what Greenspan dubbed the “put”—which encourages ever-riskier betting and fatter‐tailed crises. Finally, as Bastiat taught, true cost lives in the unseen counterfactual: when losses are masked today, we forgo the capital reallocation, balance-sheet repair, and entrepreneurial renewal that would have occurred. By stepping back, central banks surface that counterfactual, forcing an honest reckoning with trade-offs.

In sum, standing aside transforms central banks from over-ambitious architects of macro‐equilibria into humble stewards of the monetary medium. Unanchored markets, however uncomfortable, best preserve the informational integrity of prices, minimize unintended systemic risks, and honor the Anti-Symbolisation Principle’s warning that no finite policy scheme can fully map the uncountable complexity of economic reality.