Which cognitive biases were evident in Terraform Labs's collapse?

5 bias patterns are documented in this case, led by Overconfidence Bias. The full set: Overconfidence Bias, Bandwagon Effect, Groupthink, Planning Fallacy, Authority Bias. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

Did these cognitive biases cause Terraform Labs's collapse?

Decision Intel identifies reasoning-risk indicators correlated with poor outcomes — it does not claim to establish causation. That honesty is the procurement weapon: a CSO can defend a score that names the risk indicators a committee should pressure-test, not one that pretends to prove the cause. Overconfidence Bias and the other documented patterns were detectable in the reasoning before the outcome was known; whether they caused it is a question no audit can settle — too many other factors (market, timing, execution) are in play. A reasoning audit names the risk indicators a committee should pressure-test, not a verdict it should accept.

What is Decision Intel's reasoning audit?

Decision Intel runs a Recognition-Rigor Framework (R²F) audit on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

Was the risk in the Terraform Labs decision visible before the outcome?

Yes, in part. Do Kwon, CEO of Terraform Labs predates the decision and already described the situation in its own words. Read against that document alone, 4 structural conditions are identifiable without any knowledge of what followed: Algorithmic stablecoin mechanism untested at scale; 20% Anchor yield funded by reserves not sustainable revenue; Founder publicly acknowledging death spiral risk while marketing stability; $10B+ TVL with circular collateral dependency. This is a reading of a dated source, not a prediction, and every line can be checked against the quoted passage.

All case studies

Catastrophic failureFinancial Services2022

Terraform Labs

Terra/LUNA Cryptocurrency Collapse

Estimated impact: $60B in market value destroyed

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

The TerraUSD algorithmic stablecoin and its sister token LUNA collapsed in days, erasing $60B in value. The mechanism relied on arbitrage incentives to maintain a $1 peg, but a bank-run dynamic caused a death spiral when confidence broke. Founder Do Kwon dismissed critics as "poor."

Decision context

Whether the algorithmic peg mechanism could withstand large-scale redemption pressure without collateral backing, and whether Anchor Protocol's 20% yield was sustainable.

Before the outcome was known

What the record already said

The document below predates the decision. Everything after it is read from that document alone, so a reader can check each line against the words above it rather than take the reading on trust.

Press release · 2021

2021 public statement by Do Kwon: '95% of [stablecoins] are going to die, but there's also a chance UST dies too' — paired with Anchor Protocol offering 20% 'stable' yield

Source: Do Kwon, CEO of Terraform Labs

Visible at the time · 4 items

What a reader could have found without knowing the ending

Algorithmic stablecoin mechanism untested at scale
20% Anchor yield funded by reserves not sustainable revenue
Founder publicly acknowledging death spiral risk while marketing stability
$10B+ TVL with circular collateral dependency

Interpretation, not evidence

A decision intelligence tool would have flagged the fundamental contradiction between marketing UST as 'stable' while the founder publicly acknowledged its potential death. The 20% Anchor yield with no sustainable revenue source would have triggered Ponzi-scheme pattern matching and overconfidence bias warnings.

Written after the outcome was known. Nothing here can be falsified, which is why it sits below the document rather than above it.

Decision anatomy

Monetary stakes

Very high

Dissent suppressed

Time pressure

Present

Unanimous consensus

Yes

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Biases present in the decision

★Overconfidence Bias

critical

Bandwagon Effect

medium

Groupthink

high

Planning Fallacy

medium

Authority Bias

high

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

Terraform Labs · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Echo Chamber

Group members reinforce each other's views, systematically filtering out dissenting signals.

!Optimism Trap

Overconfidence and availability bias create a systematically positive outlook that ignores base rates.

The curated library this case sits in

Across all 155 curated case studies in our library, by documented outcome:

123

documented failures

partial / mixed

documented successes

The library is curated toward documented failures, so this split describes the library, not the base rate of deals. The case above is one row in it.

■ Failure■ Partial■ Success

Lessons learned

Algorithmic stablecoins without exogenous collateral are inherently fragile to reflexive death spirals
Bandwagon effect in crypto communities creates echo chambers that silence legitimate technical criticism
Unsustainable yields (20% on Anchor) are a red flag for hidden risk subsidization

Source: SEC Complaint against Terraform Labs and Do Kwon (2023); Chainalysis Crypto Crime Report 2023 (SEC Filing)

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