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961 downloads. Trust evidence available

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agentCLAWHUBUnverified

Hindsight Bias

Activate when: someone says 'I knew it all along' or 'we should have seen it coming'; a post-mortem is blaming someone for not predicting an outcome; a team... Skill: Hindsight Bias Owner: deciqai Summary: Activate when: someone says 'I knew it all along' or 'we should have seen it coming'; a post-mortem is blaming someone for not predicting an outcome; a team... Tags: latest:1.0.4 Version history: v1.0.4 | 2026-07-16T18:02:19.835Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/hindsight-bias.json) v1.0.3 | 2026-07-08T11:05:08.651Z | use

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

961

Updated

Oct 12, 2026

Version

1.0.4

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 961 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 961 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.4release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.4release
Description tail link + agents machine-readable metadata line (deciqai.com/s/hindsight-bias.json)
Observed Jul 16, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:hindsight-bias

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-hindsight-bias/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

143,474 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: hindsight-bias
description: "Activate when: someone says 'I knew it all along' or 'we should have seen it coming'; a post-mortem is blaming someone for not predicting an outcome; a team is reviewing a past decision and the outcome is coloring the judgment; a decision-maker is being evaluated on what happened rather than what was knowable at the time.
Do NOT activate when: contemporaneous pre-decision records exist and match current memory (bias is bounded); the goal is explicitly pattern-recognition from outcomes rather than evaluating the original decision-maker. More: deciqai.com/c/hindsight-bias"
---

# Hindsight Bias

## Overview

**Hindsight bias** — the "I knew it all along" effect — is the tendency, *after* learning an outcome, to misremember your prior judgment as having been closer to that outcome than it actually was. Fischhoff (1975) demonstrated three distinct components: **memory distortion** ("I said it would happen"), **inevitability** ("it had to happen"), and **foreseeability** ("anyone should have seen it"). Each requires a different countermeasure; the structural fix for all three is pre-commitment documentation.

Composes with `probabilistic-thinking`, `premortem`, `confirmation-bias`, and `survivorship-bias`.

## When to Use

- A post-mortem is becoming an exercise in blame for an "obviously foreseeable" outcome
- Someone says "I knew this would happen" without contemporaneous records
- A decision is being evaluated against its outcome rather than the information available when made
- An investor, judge, or jury evaluates a past decision with knowledge of how it turned out
- Someone calls a market move "obvious in retrospect" — e.g. the AI boom, AI capex buildout, or frontier-AI valuations were "clearly inevitable" / "anyone should have seen the AI adoption wave coming"

**Not when:** pre-commitment documentation exists and matches current memory; the goal is pattern-recognition from outcomes; the failure to predict was a genuine process failure with clear pre-outcome signals.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a specific post-outcome narrative → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line: before judging a past decision by its outcome, ask what was actually knowable in advance — check the contemporaneous record, not your current memory.
2. Check fit against When to Use / When NOT to use. If records exist and match memory, bias is bounded.
3. Elicit the specific outcome and the claim of foreseeability. What outcome happened? Who is being credited/blamed?
> **[WAIT — do not advance until user responds]**
4. Run The Process one step at a time: reconstruct the pre-outcome information set, decompose the three components, evaluate decision process vs. outcome.
> **[WAIT — do not advance unt

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "hindsight-bias",
  "version": "1.0.4",
  "publishedAt": 1784224939835
}

references/sources.md

# Sources — hindsight-bias

> *Primary sources for the [hindsight-bias](../SKILL.md) skill.*

## Sources

- Fischhoff, B. (1975). "Hindsight ≠ foresight: The effect of outcome knowledge on judgment under uncertainty." *Journal of Experimental Psychology: Human Perception and Performance*, 1(3), 288-299. The foundational paper.
- Fischhoff, B., & Beyth, R. (1975). "'I knew it would happen': Remembered probabilities of once-future things." *Organizational Behavior and Human Performance*, 13(1), 1-16. The Nixon-China study.
- Slovic, P., & Fischhoff, B. (1977). "On the psychology of experimental surprises." *Journal of Experimental Psychology: Human Perception and Performance*, 3(4), 544-551.
- Christensen-Szalanski, J. J. J., & Willham, C. F. (1991). "The hindsight bias: A meta-analysis." *Organizational Behavior and Human Decision Processes*, 48(1), 147-168.
- Hawkins, S. A., & Hastie, R. (1990). "Hindsight: Biased judgments of past events after the outcomes are known." *Psychological Bulletin*, 107(3), 311-327.
- Kamin, K. A., & Rachlinski, J. J. (1995). "Ex post ≠ ex ante: Determining liability in hindsight." *Law and Human Behavior*, 19(1), 89-104.
- Caplan, R. A., Posner, K. L., & Cheney, F. W. (1991). "Effect of outcome on physician judgments of appropriateness of care." *JAMA*, 265(15), 1957-1960. Outcome bias in expert medical peer review.
- Baron, J., & Hershey, J. C. (1988). "Outcome bias in decision evaluation." *Journal of Personality and Social Psychology*, 54(4), 569-579.
- Roese, N. J., & Vohs, K. D. (2012). "Hindsight bias." *Perspectives on Psychological Science*, 7(5), 411-426. The canonical review.
- Tetlock, P. E., & Gardner, D. (2015). *Superforecasting: The Art and Science of Prediction.* Crown. ISBN 978-0804136693.
- Allspaw, J. (2012). "Blameless PostMortems and a Just Culture." Code as Craft (Etsy engineering blog).
- OpenAI (2022). "Introducing ChatGPT." OpenAI blog, November 30, 2022 (contemporaneous "research preview" framing of the launch; used in the 2022–2026 AI-boom hindsight example).
- Nvidia Corporation (2023–2025). Quarterly financial reports and investor relations disclosures on data-center revenue, plus contemporaneous reporting of market-capitalization milestones (Reuters, Bloomberg, Financial Times). Basis for the ex-ante-vs-ex-post valuation record in the AI-boom hindsight example.

examples/ai-boom-obviously-coming-hindsight-2022-2026.md

# Method in Action: "The AI Boom Was Obviously Coming" — Retrospective Inevitability in the ChatGPT/Nvidia Era (2022–2026)

> *Example for the [hindsight-bias](../SKILL.md) skill.*

Fischhoff's lab work and the Caplan-Posner-Cheney medical study both isolated hindsight bias in controlled settings. The 2022–2026 AI boom is the same distortion playing out at civilizational scale and in real time: after ChatGPT's launch and Nvidia's run, a dominant narrative emerged that the generative-AI wave and the compute buildout were "obviously coming" — a claim that, if believed, corrupts how investors, operators, and policymakers calibrate the *next* set of bets.

**Step 1 — State the outcome and the claim.** The outcome: OpenAI released ChatGPT on November 30, 2022; it reached a consumer adoption curve widely reported at the time as among the fastest on record (roughly 1M users in its first days and, by contemporaneous estimates, ~100M monthly users within about two months). Over 2023–2025, Nvidia's data-center GPU business drove its market capitalization from well under \$1 trillion into the multi-trillion-dollar range, briefly the most valuable company in the world, and hyperscalers announced enormous AI-infrastructure capital expenditure. The foreseeability claim, heard constantly by 2024–2025: *"This was obvious — transformers had been around since 2017, scaling laws were published, of course LLMs and the GPU buildout were inevitable; anyone paying attention should have seen it coming."* Who is credited/blamed: those who bought Nvidia early or funded AI look like visionaries; those who didn't are cast as having missed the obvious. Time elapsed: roughly 1–3 years — short enough that contemporaneous records are abundant, which is exactly what makes this case testable rather than merely arguable.

**Step 2 — Reconstruct the pre-outcome information set.** What was genuinely knowable in mid-2022: the transformer architecture existed (Vaswani et al., 2017); OpenAI's scaling-law papers and GPT-3 (2020) were public; Nvidia GPUs dominated ML training. What was genuinely uncertain: whether a chat interface would trigger mass *consumer* adoption; whether enterprises would pay at scale; whether capability would keep improving with scale or plateau; whether Nvidia's margins and lead would hold against custom silicon and competitors. The contemporaneous consensus is the tell. Before the launch, generative AI was a niche within ML; the prior consumer-AI cycles (chatbots, IBM Watson, voice assistants) had underdelivered relative to hype, priming informed observers toward skepticism. OpenAI itself reportedly launched ChatGPT as a low-key "research preview," not expecting the response — a strong signal that the people closest to the technology did not treat the outcome as inevitable. *Nixon test:* if you had polled informed contemporaries in October 2022 on "a chatbot will reach ~100M users within two months and a chip vendor will become the world's most valuable company

examples/baruch-fischhoffs-nixon-china-trip-study-1972-1975.md

# Method in Action: Baruch Fischhoff's Nixon-China Trip Study, 1972-1975

> *Example for the [hindsight-bias](../SKILL.md) skill.*

The empirical foundation for hindsight bias as a formal cognitive phenomenon is **Baruch Fischhoff**'s doctoral work at Hebrew University, published in two landmark 1975 papers:

> Fischhoff, B. (1975). "Hindsight ≠ foresight: The effect of outcome knowledge on judgment under uncertainty." *Journal of Experimental Psychology: Human Perception and Performance*, 1(3), 288-299.
>
> Fischhoff, B., & Beyth, R. (1975). "'I knew it would happen': Remembered probabilities of once-future things." *Organizational Behavior and Human Performance*, 13(1), 1-16.

The Nixon-Beyth study (often called the "Nixon-China study") had a clean design. In February 1972, before President Richard Nixon's two-week diplomatic tour of China (Feb 21-28) and the Soviet Union (May 22-30), Fischhoff and Beyth presented Israeli undergraduate subjects with a list of 15 possible outcomes:

- Nixon would meet Chairman Mao Zedong
- The U.S. would establish a permanent diplomatic mission in Peking
- The U.S. and China would issue a joint communiqué
- Nixon would announce a U.S. policy of two-Chinas (recognizing both Taipei and Peking)
- A group of American POWs from Vietnam would be released
- ... (10 more)

Subjects estimated the probability of each event, on a 0-100% scale. The trips then occurred. Some events happened (Nixon-Mao meeting, joint communiqué), others did not (no permanent mission established, no two-Chinas announcement, no POW release).

Then, two weeks, two months, and four months after the trips, Fischhoff asked the same subjects to *recall* their original probability estimates. He compared the recalled estimates with the actual original responses.

The findings, in Fischhoff and Beyth's own words:

> "Subjects' memory for the probabilities they had originally assigned was systematically biased in the direction of the actual outcome. For events that had occurred, subjects recalled having predicted higher probabilities than they actually had recorded. For events that had not occurred, they recalled lower probabilities than they had recorded. The mean absolute error in recall was 9.4 percentage points at two weeks, rising to 11.5 percentage points at four months. The direction of the shift was consistent with hindsight bias in 70% to 85% of cases, depending on the event and the time delay."
>
> — Fischhoff & Beyth (1975), p. 8.

The effect was not eliminated by:
- High confidence in memory (subjects who were *most* sure they remembered correctly were just as biased).
- Interest in the political subject matter (engaged subjects were biased just as much as uninterested ones).
- A short time delay (the bias was already substantial at two weeks).

In a companion study, Fischhoff's 1975 *JEP:HPP* paper used the British-Gurkha War of 1814 — a historical event most subjects had no prior knowledge of. He gave subjects a 150-word description of the

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