Quantum Strategies for a Newtonian World

This article draws from Gerd Gigerenzer's "Two Kinds of Bias" published in Mind & Society. The key insight: in environments of deep uncertainty, simple heuristics often outperform complex optimization.

Key ideas in brief

• Traditional finance operates on "Newtonian" assumptions that don't hold in network-based economies
• The information economy has made it harder to ascertain "true value" of assets
• In a "Quantum" world of uncertainty, heuristics beat optimization
• Adaptability matters more than precision

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Why This Paper Resonated

Coming from a software background rather than traditional DCF-model finance, I'd had thoughts rattling around for a while that I couldn't quite articulate. Something felt off about the precision with which financial models claimed to value network-based businesses. The spreadsheets looked rigorous, but the assumptions underneath felt disconnected from how technology actually creates and captures value.

Gigerenzer's paper gave me the vocabulary to crystallize those intuitions. The distinction between Small World (where optimization works) and Large World (where heuristics win) mapped perfectly onto what I'd observed: traditional finance treating inherently uncertain, network-driven phenomena as if they were calculable physics problems.

The paper validated an instinct—that in environments of genuine uncertainty, simple rules often beat complex models. Not because simple is always better, but because complexity breaks down when the underlying system is unknowable.

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The Paper: Two Kinds of Bias

Gigerenzer distinguishes two meanings of "bias" in social sciences:

1. Bias-is-Error View (Traditional)

• Bias = deviation from a "true" rational answer
• Assumes there's always an optimal solution that can be calculated
• Deviations from this optimum are mistakes to be corrected

2. Functional Bias View (Ecological)

• Bias = necessary constraint that enables perception and decision-making
• The brain can't directly see reality—it uses expectations to make sense of sensory chaos
• These "biases" are features, not bugs

The paper introduces the "bias bias"—the tendency to see systematic biases in behavior even when there's only noise or no verifiable error at all.

Small World vs. Large World

This is the crucial distinction:

The problem: Traditional finance assumes we live in a Small World. DCF models, mean-variance optimization, P/E ratios—all assume the relevant variables are known and calculable.

The reality: We increasingly live in a Large World. Network effects, attention economies, liquidity flows—these create environments where "true value" is fundamentally unknowable.

Ecological Rationality

A heuristic is ecologically rational to the degree it's adapted to the structure of its environment.

Key insight: Heuristics are not irrational or always second-best to optimization. Research has identified situations where "less is more"—where heuristics make more accurate decisions with less effort than complex models.

Why? In Large World environments:

• Complex models overfit to noise (high variance error)
• Simple heuristics ignore noise (high bias, but zero variance)
• The bias-variance tradeoff favors simplicity under uncertainty

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The Analogy: Newtonian Finance vs. Quantum Economics

The intuition: we're moving from "particle-based" (Newtonian) to "field-based" (Quantum) valuations. This holds up when examined through the lens of uncertainty and intractability.

The mismatch: Traditional finance is still running "Newtonian" software (DCF, P/E ratios, Mean-Variance Optimization) in a "Quantum" world.

The edge comes from acknowledging intractability and prioritizing adaptability over precision.

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Strategy A: Trade the "Field," Not the "Particle"

The Trap: Traditional investors (Newtonian) look for "intrinsic value" (bias-is-error view), assuming a stock has a "true" price. They get crushed when a stock with "bad fundamentals" keeps rising due to network effects or liquidity flows.

The Reframe: Adopt the functional bias view. In a network economy, price is the signal.

Instead of asking "Is this overvalued?" (comparing to a static true state), ask: "What is the current bias enabling?"

If the market is valuing "infinite agentic consumption," don't short it because it violates Newtonian P/E rules. The physics have changed.

Heuristic: Follow the flow (momentum/network growth) rather than the "particle mass" (book value). In a Quantum world, the wave function (adoption curve) matters more than the particle's weight.

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Strategy B: Use Low-Variance Heuristics in High-Uncertainty Zones

The Trap: When the world gets weird (Quantum uncertainty), traditional finance builds more complex models to capture the "noise" (stochastic volatility models, factor zoo, ML ensembles).

The paper warns: this increases Variance Error—the models begin to overfit random noise.

The Reframe: When uncertainty is high (e.g., valuing a new AI agent economy), simplify the rules.

Use "fast and frugal" heuristics:

• 1/N rule: Equal weight allocation instead of optimized weights
• Simple trend-following: Price above moving average = long
• Fixed thresholds: Binary rules instead of continuous optimization

These have "high bias" (they ignore complex data) but "zero variance" (they don't overreact to noise).

Why this works: In a Large World of unknowable risks, a crude stable map is safer than a precise hyper-sensitive GPS that recalculates every second.

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Strategy C: Detect Regime Shifts (Relativity)

The Trap: Newtonian models assume the laws of physics are constant (Time Invariance). Financial models assume "stationarity"—that past averages (Base Rates) predict the future.

The Reframe: In a Relativistic economy, space-time curves. A structural break renders the Base Rate dangerous.

Gigerenzer uses the "Crocodile" example: if a crocodile enters the river, the historical safety record (base rate) is irrelevant. The environment has fundamentally changed.

Application: If an asset class shifts from "tool" to "agent" (e.g., AI), ignore its historical valuation multiples. The physics of that sector has changed.

Practice Base Rate Neglect when:

• New technology creates step-function changes in economics
• Regulatory shifts alter market structure
• Network effects create winner-take-all dynamics that didn't exist before

The past is not prologue when the rules have changed.

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Strategy D: Resilience via Ecological Rationality

The Trap: Optimization. Newtonian mechanics allows calculating the perfect trajectory. Traditional finance seeks the optimal portfolio.

The Reframe: Resilience. In Quantum mechanics, you can't know position and momentum simultaneously. In modern markets, you can't know risk and return perfectly.

Don't optimize for the "most likely" future. Build a portfolio that survives the unforeseeable.

This means holding assets that act as insurance against breakdown of the Newtonian model:

• Long volatility positions (insurance against model failure)
• Uncorrelated assets (gold, crypto)
• Optionality (asymmetric payoffs)

Concept: This is Ecological Rationality—adapting to an environment of deep uncertainty where you cannot calculate the optimal course.

The goal isn't to be right about the future. The goal is to survive being wrong.

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Summary

The traditional world is trying to measure the position (Fair Value) of particles that are actually behaving like waves (Network Effects).

The alpha: Stop trying to measure the particle better.

The method: Accept that "true value" doesn't exist in network economies. Use simple, robust heuristics to navigate the waves without capsizing from model complexity.

In a Small World, optimization wins. In a Large World, heuristics win.

The information economy has pushed us firmly into the Large World. Act accordingly.

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