Forecast

If the stock market sometimes feels like a tornado—sudden, chaotic, and completely unpredictable—what if that feeling is more accurate than economists ever admitted? In Forecast, physicist Mark Buchanan invites you to rethink everything you’ve been told about why markets move. He argues that economic systems aren’t calm and self-regulating machines at all. They’re turbulent natural ecosystems governed by feedback loops and instability—much like the planet’s weather.

For generations, Buchanan explains, economics has been trapped in an equilibrium delusion: the belief that markets naturally balance themselves, returning smoothly to normal after any disturbance. This comforting idea, born in the eighteenth century from Adam Smith’s famous ‘invisible hand,’ has shaped how governments regulate markets, how investors manage risk, and even how citizens think about fairness. But reality, says Buchanan, looks much closer to meteorology than to mathematics. Just as we can’t predict exactly when a thunderstorm will form, we can’t forecast a crash—but we can understand the conditions that make one more likely.

The Physics of Everyday Chaos

Buchanan takes the reader back to Overland Park, Kansas, where the book opens with a vivid image of tornadoes whipping across the plains. Tornadoes, he says, are not freak events—they’re inevitable consequences of how warm air interacts with cold air under rotation and gravity. In the same way, financial crises—like the ‘flash crash’ of 2010 or the meltdown of 2008—aren’t caused by one fat-fingered trader or rogue algorithm. They’re produced by the feedback loops built into the system itself. Each panic, each bubble, stems from the same physics of escalation: small fluctuations multiplying through mutual reactions until they explode.

Economists as Weather Forecasters

The heart of Buchanan’s indictment is simple: economists have been trying to forecast storms without understanding them. They design models that assume markets move toward stability instead of watching how real participants—people, firms, banks, and algorithms—interact dynamically. This worldview blinds them to the role of positive feedback, the process that makes small changes grow larger: a few traders selling stocks can trigger panic selling, just as one gust of wind can birth a storm.

For Buchanan, the failure is not just scientific but moral. Because economists convinced politicians that markets regulate themselves, governments tore down safeguards, deregulated financial systems, and let leverage spiral out of control. They used elegant mathematics to justify inequality and excess. And when those formulas failed, they claimed it was an ‘exception.’ As former Fed chairman Alan Greenspan famously said, market crises were ‘notably rare exceptions.’ Buchanan’s response: they are the rule.

Why Physics Teaches Us More than Finance

Physics, Buchanan reminds us, is the science of systems out of balance. Things move, collide, and evolve. Physicists learned centuries ago that equilibrium is the death of curiosity—you don’t study a cloud by pretending it’s frozen. So when physicists turned their tools toward life, biology, and the networks of human behavior, they found the same math behind earthquakes, evolution, and recessions: power laws, fractals, and self-organized criticality. These patterns reveal a world running on disequilibrium, where minor events—like Waddell and Reed’s $4 billion sell order—can release enormous forces just waiting for a trigger.

Buchanan argues that the heart of economic progress lies not in stability but in the continual churn of turbulence and adaptation. Understanding these rhythms requires abandoning old ideas like ‘market efficiency’ and embracing models of feedback, thresholds, and complexity. It means treating traders and algorithms not as perfect rational actors but as evolving creatures responding to one another, just as species compete for resources in nature. This isn’t fatalism—it’s realism.

The New Science of Financial Weather

Throughout the book, Buchanan chronicles how scientists from physics, ecology, and computer science have begun to map the patterns of economic turbulence. They’ve discovered power laws governing everything from earthquake magnitudes to price fluctuations, showing that extreme events are never truly rare—they’re part of the same fabric as ordinary days. He compares market spikes to the fractal geometry of coastlines, each ripple echoing the same underlying shape. This new science replaces dreams of perfect prediction with probabilistic forecasting—understanding what’s possible instead of pretending we can control what’s inevitable.

Ultimately, Buchanan’s message is unsettling but empowering: you can’t eliminate volatility, but you can learn its grammar. Economic storms aren’t mistakes; they’re the natural result of a complex, interacting system. It’s not a matter of if turbulence will strike but when—and whether we choose to design institutions able to withstand it.

Core Takeaway

Markets are not efficient, self-correcting machines—they’re chaotic ecosystems governed by feedback loops. To navigate them safely, you need to think more like a meteorologist than an accountant, studying instability instead of denying it. The future of economics, Buchanan shows, lies in embracing uncertainty and understanding the physics of change.

Buchanan begins with a striking metaphor: economists are like weather forecasters who don’t understand storms. They worship equilibrium—the idea that markets always return to balance—without recognizing that nature itself rarely does. The chapter opens with the story of the 2010 ‘flash crash,’ when U.S. markets lost nearly 9 percent of their value in minutes before rebounding. Investigators blamed a single sell order for triggering chaos, but Buchanan argues the real cause was systemic instability built into modern finance.

Positive vs. Negative Feedback

In physics, negative feedback stabilizes systems—think of a thermostat moderating temperature. Positive feedback, however, amplifies change. It’s the process that makes compound interest, population growth, and market panics spiral upward or downward faster than intuition allows. Economists, says Buchanan, have ignored this force. Their theories assume rational actors and smooth corrections, even though history—from tulip mania to the 2008 crisis—proves markets can self-destruct through amplification.

The Myth of Perfect Markets

The “invisible hand,” Adam Smith’s metaphor, became an article of faith: markets guide themselves toward optimal efficiency. Buchanan dismantles this by showing how the doctrines of Smith, Walras, and Arrow built a world run on mathematical symbols divorced from reality. In their systems, people aren’t emotional beings—they’re equations solving for profit. Yet real markets operate like ecosystems, not machines. Traders influence each other’s decisions, herd like animals, and cause unpredictable chain reactions. As he puts it, if economists can’t model tornadoes, they shouldn’t pretend to model finance.

Why the Delusion Persists

Despite repeated crises, economists cling to their equilibrium because it offers comfort and control. It supports a worldview in which the market is always right—useful for politicians, regulators, and financiers whose careers depend on claiming stability. Buchanan points to the 2008 meltdown and Alan Greenspan’s comment that self-correction would prevent collapse. The events proved otherwise, yet academia continued to teach the same efficient-market models, much like medieval doctors reapplying broken theories of humors while patients bled.

The chapter leaves you with one critical insight: equilibrium is a fantasy built for quiet times. The real economy lives in disequilibrium, where constant change drives growth, instability, and surprise.

In one of the book’s central chapters, Buchanan examines the long lineage of economists who believed the market was the ultimate self-correcting machine. He traces this dream from Adam Smith’s ‘invisible hand’ through 19th-century French mathematician Léon Walras and eventually to Kenneth Arrow and Gérard Debreu, whose 1954 theorem mathematically proved that an ideal market must reach equilibrium. That proof became the philosophical foundation for Western capitalism.

The Physics Envy Problem

Economists borrowed their confidence from physics—Newton’s equations of balanced forces and Cournot’s and Walras’s ‘equilibrium equations’ for supply and demand. It sounded rigorous but ignored uncertainty, chaos, and timing. Buchanan explains how equilibrium became an intellectual idol: economists reduced markets to symmetrical equations even though those systems include millions of emotional actors, imperfect data, and unpredictable events.

The Rise of the Efficient Market Hypothesis

Arrow and Debreu’s work led economists like Paul Samuelson and Eugene Fama to devise the Efficient Market Hypothesis—the belief that prices fully reflect all available information, making it impossible to consistently beat the market. Buchanan reconstructs how this theory evolved from Holbrook Working’s ‘randomness principle’ and Samuelson’s elegant math, then took root on Wall Street. If markets were efficient, every price was ‘just right,’ even when bubbles grew colossal.

Examples like the 2011 downgrade of U.S. government credit ratings and Goldman Sachs’s instant billion-dollar loss reveal that markets don’t act rationally—they react nervously. Buchanan’s thought experiment—the ‘5% problem’—shows why predictable markets contradict logic: if everyone expects a stock to rise, it rises immediately, erasing predictability. Therefore, predictability dies in its own proof. Efficiency becomes an elegant tautology rather than truth.

From Theory to Deregulated Reality

Buchanan details how economists extended efficiency to justify deregulation. Financial theorists Robert Merton and Zvi Bodie claimed markets move ‘spirally toward completion,’ implying perpetual improvement through derivatives and computerized trading. This belief fueled the 1980s–1990s deregulation wave and spawned complex instruments like credit default swaps—the same tools that later magnified the 2008 crash. As Buchanan dryly notes, the pistons of this ‘marvelous machine’ were firing faster right up until the explosion.

Behind all the math lies a dangerous mistake: equating unpredictability with perfection. The efficient market hypothesis doesn’t prove wisdom—it only proves randomness. And randomness isn’t efficiency; it’s chaos with a Ph.D.

This chapter bridges financial dynamics and geology, showing eerie parallels between market collapses and earthquakes. Buchanan recounts the Japanese Tōhoku earthquake of 2011 and explains how geophysicists have never successfully predicted quakes—it’s impossible. Yet beneath unpredictability lie regularities described by power laws: small tremors are common, big ones rare, but both follow the same mathematical pattern. The same rule, he shows, governs market shocks.

Power Laws and Fat Tails

Data from millions of trades reveal that the distribution of price changes follows an inverse power law similar to Gutenberg-Richter’s law for earthquakes. Markets, like geological systems, are dominated by extremes—fat tails indicating rare but massive events. Buchanan mocks corporate risk models, recounting Goldman Sachs CFO David Viniar’s absurd claim that the 2008 crisis involved several ‘25-standard deviation events’ in a row: statistically, this should happen once every 10^135 years, longer than the age of the universe. Reality shattered mathematics.

He connects these patterns to Nassim Taleb’s The Black Swan, noting that most danger lies in what economists discount as improbable. In both earthquakes and markets, vast movements result not from giant causes but the accumulation of tiny instabilities. The 1987 crash and the subprime collapse were financial fault-line ruptures, not random cosmic accidents.

Memory and Feedback

Another phenomenon connects market and nature: long-term memory. Economist Clive Granger’s autocorrelation studies showed that price volatility can remain predictable for years, meaning the market remembers its past. Buchanan likens this to landscapes shaped by geological accidents—once changed, always changed. Like Omori’s law describing earthquake aftershocks, market turbulence declines in proportion to time since the main event. Calm hides continuity, not recovery.

Buchanan’s radical suggestion: financial systems are living seismographs. They accumulate stress silently, then release it explosively. Stability isn’t peace—it’s pressure waiting to break.

To build better theories, Buchanan argues, we must begin with human behavior as it is, not as Milton Friedman imagined it. Economists, he says, built models assuming unrealistic perfection—rational actors maximizing utility. Friedman even claimed good theories should have ‘wildly inaccurate assumptions,’ a logical trick Buchanan calls the F-twist. The result was the fantasy of ‘Homo economicus,’ a creature absent from any known species.

Why Unrealistic Assumptions Fail

Buchanan dismantles Friedman’s philosophy with analogies from physics: assuming the Earth is flat doesn’t make it easier to navigate; it makes you lost. Newton’s laws didn’t work because they were descriptively false—they worked because they were descriptively simple and mostly accurate. By championing falsity as a virtue, Friedman gave economists permission to ignore reality. That error birthed elegant but empty theories like William Sharpe’s capital asset pricing model, which assumed all investors could borrow at the same rate. Real markets shattered those illusions.

Learning and Adaptation

People, Buchanan emphasizes, are not rational calculators but adaptive learners. They use heuristics and evolve strategies just as species evolve traits. He cites Brian Arthur’s El Farol Bar experiment and the later ‘minority game’ by Yi-Cheng Zhang and Damien Challet, in which simulated agents learn to outsmart each other by anticipating crowd psychology. These models show how markets self-organize into turbulent equilibria—small predictabilities wiped out by collective adaptation. They mimic the real feedback-driven volatility of modern trading far better than any rational expectations model.

Homo sapiens, Not Homo economicus

Using insights from psychology and neuroscience, Buchanan replaces Homo economicus with Homo sapiens—creatures with emotion, bias, and social contagion. People imitate others, feel overconfident, and make decisions that contradict pure logic. Markets therefore behave like living brains, wired for rhythm and panic. The assumption of perfect rationality doesn’t simplify complexity—it erases it, leaving economists blind to how collective learning actually drives bubbles and crashes.

Economic models work only when they treat humans as evolving, adapting, and occasionally irrational beings. In short: we need to model curiosity, not perfection.

Here Buchanan expands on the idea that markets are ecosystems of competing beliefs. He introduces Frank Knight’s distinction between risk (measurable probabilities) and uncertainty (the unknown unknowns)—and shows how economists ignored the latter by adopting ‘rational expectations.’ John Muth’s original idea—that people’s forecasts will on average be unbiased—led to two generations of models where uncertainty was banished from theory, leaving a world that could never boom or bust.

Learning Over Time

Drawing on cognitive science, Buchanan explains how real people learn like chess players: through pattern recognition and experience. Chess masters ‘see’ the game as clusters of meaningful shapes rather than endless calculations. Economists, he says, should study learning and history the same way. Brian Arthur’s and Blake LeBaron’s agent-based market simulations show how diversity of expectations creates rich dynamics—market weather emerging from adaptive players, not omniscient planners.

The Minority Game Revolution

Physicists Challet and Zhang simplified Arthur’s bar game into the ‘minority game,’ demonstrating how markets can be both efficient and chaotic. When too many traders crowd the same strategy, predictability vanishes; when diversity expands, patterns reappear. Buchanan’s metaphor: markets flip between phases like water turning to ice. Stability and instability are natural oscillations, not anomalies. The takeaway—crowded intelligence creates dumb results.

A Human Ecology of Finance

Economist Alan Kirman called markets ‘epidemics of opinion.’ Buchanan agrees, describing financial systems as living ecosystems in which ideas mutate and spread through imitation, gossip, and algorithmic trading. Understanding finance means studying this ecological web—how beliefs fuel self-fulfilling prophecies and collapses. The challenge isn’t to find equilibrium, but to monitor evolution.

Buchanan shifts focus to the digital frontier: high-frequency trading (HFT). He tells the story of Mike McCarthy, an ordinary investor whose $15,000 evaporated during the 2010 flash crash as algorithms traded thousands of times per second. In these new markets, speed has replaced understanding. Physicists like Richardson once asked if the wind even possess a velocity—markets today move faster than human reaction times, turning financial air into turbulence.

Liquidity Illusions

Economists celebrate HFT for improving liquidity, lowering bid-ask spreads, and making markets more efficient. Buchanan acknowledges these benefits but warns that liquidity vanishes during stress—exactly when it’s needed. During the flash crash, algorithmic market makers like GETCO stopped trading en masse, turning smooth liquidity into a vacuum. As one trader said, “there was a complete evaporation of liquidity.” Efficiency, once again, bred instability.

The Physics of Financial Turbulence

Drawing comparisons to Harold Hurst’s studies of river floods and the chaotic flow of plasma, Buchanan explains how modern trading generates fractal turbulence across timescales. Neil Johnson’s data on ‘fractures’ and ‘spikes’ under one-second durations shows thousands of miniature flash crashes each year. The faster the trading, the more crowded and fragile the system becomes—markets dominated by machines react faster than humans can blink, creating a new phase of algorithmic disorder.

High-frequency trading doesn’t eliminate risk—it compresses it. The future of financial stability depends on understanding feedbacks between speed, liquidity, and panic. As Buchanan warns, we are building tornadoes out of fiber optic cables.

In the final chapter, Buchanan turns from diagnosis to prediction. Just as meteorologists learned to forecast storms, economics must learn to forecast crises—not to prevent them entirely but to anticipate conditions ripe for disaster. He recalls how Lewis Richardson’s early weather models failed until computers made simulation possible. The same revolution, he argues, must come for finance: large-scale models that trace feedbacks, thresholds, and risk networks across the global economy.

Forecasting the Unpredictable

Exact prediction, Buchanan admits, is impossible. Even in physics, chaos limits precision—pinball paths and hurricanes defy perfect foresight. But approximate forecasting is achievable. Just as weather models today issue warnings of likely storms, financial ‘weather centers’ could track leverage, interbank dependencies, algorithmic pressures, and psychological indicators to issue early alerts. He imagines an international ‘Center for Financial Forecasting’ operating like CERN for markets.

Human Nature and Feedback

One obstacle remains: human reflexivity. As George Soros argued, knowledge changes behavior. Forecasts can become self-fulfilling prophecies. Buchanan sees this as a challenge, not a disqualification. Reflexivity is part of the system to model, not an excuse for ignorance. He calls for a data revolution—real-time monitoring of financial networks, physiological measures of trader stress, and ensemble simulations of millions of scenarios to reveal patterns.

Why Understanding Trumps Control

He closes with humility: no amount of science will remove human greed or error, but better models can expose fragility before it erupts. Like the weather, markets will always storm, but knowing the physics changes survival odds. Avoiding catastrophe, Buchanan writes, requires vigilance, transparency, and scientific honesty—the courage to admit uncertainty instead of pretending it doesn’t exist.

His closing warning is simple: the equilibrium delusion made us blind. Disequilibrium thinking could make us prepared.