Schrödinger Asset Valuation Model
Price high-uncertainty assets by probability-weighting their two extreme outcome narratives
The Schrödinger Asset Valuation Model treats high-uncertainty assets as existing simultaneously in two extreme states: maximum success and complete failure. Rather than picking a camp, the investor explicitly assigns probability percentages to each extreme narrative, computes a probability-weighted expected value, and compares it to the current market price. Volatility is reframed not as inherent risk to avoid but as evidence of ongoing narrative disagreement—and therefore as a signal that asymmetric upside still exists. As consensus forms over time, disagreement compresses, volatility falls, and the outsized opportunity closes. The model is dynamic: probabilities must be updated as structural events shift each camp's credibility.
- Neither extreme narrative is likely correct; assign explicit probability to both
- Volatility is born from narrative disagreement, not from asset quality
- High volatility in early-stage assets signals high remaining upside, not danger
- As consensus narrows, volatility compresses and so does the asymmetric opportunity
- A probability-weighted model yields a more defensible fair value than narrative allegiance
- Persistent disagreement between credible parties is evidence you are still early
- Identify and articulate both extreme narratives honestlyResearch and write out the maximum bull case (e.g., global reserve asset worth millions per unit) and the maximum bear case (e.g., government-banned and worthless). Both sides must be represented by their most credible advocates, not strawmen.Pro tipFind the single most serious representative of each camp—a rigorous bull and a rigorous bear—to ensure the extremes are legitimate, not caricatures.WarningDo not filter the extremes through your personal belief. The goal is to capture the market's actual narrative poles, not your preferred version of each side.
- Assign explicit probability percentages to each extremeGive each extreme a probability weight that sums meaningfully across the outcome space. Start simply: what probability do you assign to massive success vs. complete failure? Force yourself to put a number on each.Pro tipRun a brief survey of knowledgeable peers before assigning your own probabilities to calibrate against outside views.WarningAssigning 90%+ to your preferred outcome defeats the model. If serious, credible people hold the opposing view, give it at least 10–20% weight.
- Calculate probability-weighted expected valueMultiply each extreme outcome's implied asset value by its probability and sum the results to arrive at a fair-value estimate. Compare this directly to the current market price to identify over- or undervaluation.Pro tipRun the calculation at multiple probability splits (e.g., 60/40, 70/30, 80/20) to build a sensitivity table and understand how much your answer depends on your assumptions.
- Reframe volatility as a disagreement signal, not a risk signalRecognize that ongoing price swings reflect the market oscillating between your two narrative camps. High volatility means high remaining disagreement, which means the asymmetric upside is still intact and the opportunity has not yet been priced away.Pro tipTrack whether mainstream media is simultaneously publishing strongly bullish and strongly bearish content. When both camps are loud, you are still early.
- Update probability weights on structural events, not price movesRevisit and adjust your probability assignments when genuine structural developments occur—regulatory decisions, major adoption milestones, competitive threats, or macro regime changes. Do not update on day-to-day price volatility.Pro tipSet a quarterly calendar reminder for a formal probability review. Keep a log of what changed structurally and why that shifts each camp's credibility.WarningOver-reacting to short-term price movements when updating probabilities will bias the model toward momentum and destroy its value as an independent valuation tool.
The speaker watched Michael Saylor predict Bitcoin at hundreds of millions per coin while Dan Penna called it worthless on the same day. Rather than choosing a side, he built a probability-weighted model: assigning roughly 60–70% probability to Bitcoin succeeding as a global monetary asset and 30–40% to failure or government banning. The weighted expected value exceeded the market price at the time, producing a clear undervaluation signal.
A venture analyst applies the model to a Series A fintech: bull case is dominant market leader worth $10B with 30% probability, bear case is shutdown at zero with 40% probability, middle scenario is modest acquisition at $500M with 30% probability. The probability-weighted expected value comes out at ~$3.15B—far above the $500M valuation ask.
Developed by the podcast guest appearing on Robin Seyr's channel after watching Michael Saylor predict Bitcoin at hundreds of millions and Dan Penna call it worthless on the same day—realizing both absolutes were likely wrong and that probability-weighting both extremes was the rigorous path to a fair value.