scout-mindset-bias-check

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Scout Mindset & Bias Check

Scout Mindset与偏差检查

What is Scout Mindset?

什么是Scout Mindset？

Scout Mindset (Julia Galef) is the motivation to see things as they are, not as you wish them to be. Contrast with Soldier Mindset, which defends a position regardless of evidence.

Core Principle: Your goal is to map the territory accurately, not win an argument.

Why It Matters:

Forecasting requires intellectual honesty
Biases systemically distort probabilities
Emotional attachment clouds judgment
Motivated reasoning leads to overconfidence

Scout Mindset（由Julia Galef提出）是一种追求看清事物本来面目的驱动力，而非将事物看作你期望的样子。与之相对的是Soldier Mindset，即无论证据如何，都坚持捍卫自身立场。

核心原则： 你的目标是精准描绘真实情况，而非赢得争论。

重要性：

预测需要理智诚实
偏差会系统性扭曲概率判断
情感依赖会模糊判断力
动机性推理会导致过度自信

When to Use This Skill

何时使用此工具

Use this skill when:

Prediction feels emotional - You want a certain outcome
Stuck at 50/50 - Indecisive, can't commit to probability
Defending a position - Arguing for your forecast, not questioning it
After inside view analysis - Used specific details, need bias check
Disagreement with others - Different people, different probabilities
Before finalizing - Last sanity check

Do NOT skip this when stakes are high, you have strong priors, or forecast affects you personally.

在以下场景使用此工具：

预测带有情绪化倾向 - 你内心期望某个特定结果
陷入50/50的决策僵局 - 犹豫不决，无法确定概率
在捍卫某个立场 - 为自己的预测辩护，而非质疑它
完成内部视角分析后 - 已使用具体细节分析，需要进行偏差检查
与他人存在分歧 - 不同人给出不同的概率判断
最终确定前 - 最后的合理性检查

当风险较高、你有强烈的先验立场，或预测结果与你个人利益相关时，绝不能跳过此步骤。

Interactive Menu

交互式菜单

What would you like to do?

你想要执行什么操作？

Core Workflows

核心工作流

1. Run the Reversal Test - Check if you'd accept opposite evidence

Detect motivated reasoning
Validate evidence standards
Expose special pleading

2. Check Scope Sensitivity - Ensure probabilities scale with inputs

Linear scaling test
Reference point calibration
Magnitude assessment

3. Test Status Quo Bias - Challenge "no change" assumptions

Entropy principle
Change vs stability energy
Default state inversion

4. Audit Confidence Intervals - Validate CI width

Surprise test
Historical calibration
Overconfidence check

5. Run Full Bias Audit - Comprehensive bias scan

All major cognitive biases
Systematic checklist
Prioritized remediation

6. Learn the Framework - Deep dive into methodology

Read Scout vs Soldier Mindset
Read Cognitive Bias Catalog
Read Debiasing Techniques

7. Exit - Return to main forecasting workflow

1. 运行反转测试 - 检查你是否会接受相反的证据

检测动机性推理
验证证据标准
揭露特殊抗辩

2. 检查范围敏感性 - 确保概率随输入规模合理变化

线性缩放测试
参考点校准
量级评估

3. 测试现状偏差 - 挑战"无变化"的假设

熵原理
变化vs稳定所需的能量
默认状态反转

4. 审核置信区间 - 验证置信区间宽度

意外测试
历史校准检查
过度自信检查

5. 运行全面偏差审计 - 全面扫描认知偏差

所有主要认知偏差
系统化检查清单
优先级排序与修正

6. 学习框架 - 深入了解方法论

阅读Scout vs Soldier Mindset
阅读Cognitive Bias Catalog
阅读Debiasing Techniques

7. 退出 - 返回主预测工作流

1. Run the Reversal Test

1. 运行反转测试

Check if you'd accept evidence pointing the opposite direction.

Reversal Test Progress:
- [ ] Step 1: State your current conclusion
- [ ] Step 2: Identify supporting evidence
- [ ] Step 3: Reverse the evidence
- [ ] Step 4: Ask "Would I still accept it?"
- [ ] Step 5: Adjust for double standards

检查你是否会接受指向相反方向的证据。

反转测试进度：
- [ ] 步骤1：陈述当前结论
- [ ] 步骤2：识别支持性证据
- [ ] 步骤3：反转证据
- [ ] 步骤4：自问"我是否仍会接受它？"
- [ ] 步骤5：修正双重标准

Step 1: State your current conclusion

步骤1：陈述当前结论

What are you predicting?

Prediction: [Event]
Probability: [X]%
Direction: [High/Low confidence]

你正在预测什么？

预测事件：[事件]
概率：[X]%
置信度方向：[高/低]

Step 2: Identify supporting evidence

步骤2：识别支持性证据

List the evidence that supports your conclusion.

Example: Candidate A will win (75%)

Polls show A ahead by 5%
A has more campaign funding
Expert pundits favor A
A has better debate ratings

列出支持你结论的证据。

示例：候选人A将获胜（75%）

民调显示A领先5%
A拥有更多竞选资金
专家评论员更看好A
A的辩论评分更高

Step 3: Reverse the evidence

步骤3：反转证据

Imagine the same evidence pointed the OTHER way.

Reversed: What if polls showed B ahead, B had more funding, experts favored B, and B had better ratings?

想象同样的证据指向相反方向。

反转场景： 如果民调显示B领先、B拥有更多资金、专家更看好B且B的辩论评分更高，会怎样？

Step 4: Ask "Would I still accept it?"

步骤4：自问"我是否仍会接受它？"

The Critical Question:

If this reversed evidence existed, would I accept it as valid and change my prediction?

Three possible answers:

A) YES - I would accept reversed evidence ✓ No bias detected, continue with current reasoning

B) NO - I would dismiss reversed evidence ⚠ Warning: Motivated reasoning - you're accepting evidence when it supports you, dismissing equivalent evidence when it doesn't (special pleading)

C) UNSURE - I'd need to think about it ⚠ Warning: Asymmetric evidence standards suggest rationalizing, not reasoning

关键问题：

如果存在这些反转的证据，我是否会认可其有效性并改变我的预测？

三种可能的答案：

A) 是 - 我会接受反转的证据 ✓ 未检测到偏差，继续当前推理

B) 否 - 我会否定反转的证据 ⚠ 警告： 存在动机性推理 - 你在证据支持自己时接受它，在证据反对自己时否定同等的证据（特殊抗辩）

C) 不确定 - 我需要再想想 ⚠ 警告： 不对称的证据标准表明你在合理化，而非理性推理

Step 5: Adjust for double standards

步骤5：修正双重标准

If you answered B or C:

Ask: Why do I dismiss this evidence in one direction but accept it in the other? Is there an objective reason, or am I motivated by preference?

Common rationalizations:

"This source is biased" (only when it disagrees)
"Sample size too small" (only for unfavorable polls)
"Outlier data" (only for data you dislike)
"Context matters" (invoked selectively)

The Fix:

Option 1: Reject the evidence entirely (if you wouldn't trust it reversed, don't trust it now)
Option 2: Accept it in both directions (trust evidence regardless of direction)
Option 3: Weight it appropriately (maybe it's weak evidence both ways)

Probability adjustment: If you detected double standards, move probability 10-15% toward 50%

Next: Return to menu

如果你选择了B或C：

自问： 为什么我会在一个方向上否定该证据，却在另一个方向上接受它？是有客观原因，还是受个人偏好驱动？

常见的合理化理由：

"这个来源有偏见"（仅在它与我意见相左时这么说）
"样本量太小"（仅针对不利的民调）
"数据是异常值"（仅针对我不喜欢的数据）
"背景很重要"（选择性地提出这一点）

修正方法：

选项1：完全拒绝该证据（如果你在反转场景下不信任它，那现在也不要信任它）
选项2：在两个方向上都接受它（无论方向如何，都信任该证据）
选项3：适当加权（可能它在两个方向上都是弱证据）

概率调整： 如果检测到双重标准，将概率向50%方向调整10-15%

下一步： 返回菜单

2. Check Scope Sensitivity

2. 检查范围敏感性

Ensure your probabilities scale appropriately with magnitude.

Scope Sensitivity Progress:
- [ ] Step 1: Identify the variable scale
- [ ] Step 2: Test linear scaling
- [ ] Step 3: Check reference point calibration
- [ ] Step 4: Validate magnitude assessment
- [ ] Step 5: Adjust for scope insensitivity

确保你的概率随量级合理变化。

范围敏感性检查进度：
- [ ] 步骤1：识别变量规模
- [ ] 步骤2：测试线性缩放
- [ ] 步骤3：检查参考点校准
- [ ] 步骤4：验证量级评估
- [ ] 步骤5：修正范围不敏感性

Step 1: Identify the variable scale

步骤1：识别变量规模

What dimension has magnitude?

Number of people (100 vs 10,000 vs 1,000,000)
Dollar amounts ($1K vs $100K vs $10M)
Time duration (1 month vs 1 year vs 10 years)

哪个维度存在量级差异？

人数（100人 vs 10,000人 vs 1,000,000人）
金额（1000美元 vs 100,000美元 vs 10,000,000美元）
时长（1个月 vs 1年 vs 10年）

Step 2: Test linear scaling

步骤2：测试线性缩放

The Linearity Test: Double the input, check if impact doubles.

Example: Startup funding

If raised $1M: ___%
If raised $10M: ___%
If raised $100M: ___%

Scope sensitivity check: Did probabilities scale reasonably? If they barely changed → Scope insensitive

线性测试： 将输入翻倍，检查影响是否也翻倍。

示例：初创企业融资

融资100万美元：___%
融资1000万美元：___%
融资1亿美元：___%

范围敏感性检查： 概率是否合理缩放？如果几乎没有变化 → 存在范围不敏感性

Step 3: Check reference point calibration

步骤3：检查参考点校准

The Anchoring Test: Did you start with a number (base rate, someone else's forecast, round number) and insufficiently adjust?

The fix:

Generate probability from scratch without looking at others
Then compare and reconcile differences
Don't just "split the difference" - reason about why estimates differ

锚定测试： 你是否从某个数字（基准率、他人的预测、整数）开始，且调整不足？

修正方法：

不参考他人的预测，从头生成自己的概率
然后比较并调和差异
不要只是"折中" - 要分析估计值不同的原因

Step 4: Validate magnitude assessment

步骤4：验证量级评估

The "1 vs 10 vs 100" Test: For your forecast, vary the scale by 10×.

Example: Project timeline

1 month: P(success) = ___%
10 months: P(success) = ___%
100 months: P(success) = ___%

Expected: Probability should change significantly. If all three estimates are within 10 percentage points → Scope insensitivity

"1 vs 10 vs 100"测试： 针对你的预测，将规模调整10倍。

示例：项目 timeline

1个月：成功概率 = ___%
10个月：成功概率 = ___%
100个月：成功概率 = ___%

预期结果： 概率应显著变化。如果三个估计值的差异在10个百分点以内 → 存在范围不敏感性

Step 5: Adjust for scope insensitivity

步骤5：修正范围不敏感性

The problem: Your emotional system responds to the category, not the magnitude.

The fix:

Method 1: Logarithmic scaling - Use log scale for intuition

Method 2: Reference class by scale - Don't use "startups" as reference class. Use "Startups that raised $1M" (10% success) vs "Startups that raised $100M" (60% success)

Method 3: Explicit calibration - Use a formula: P(success) = base_rate + k × log(amount)

Next: Return to menu

问题： 你的情感系统对类别做出反应，而非对量级做出反应。

修正方法：

方法1：对数缩放 - 使用对数尺度辅助直觉判断

方法2：按规模划分参考类别 - 不要将"初创企业"作为参考类别，而是使用"融资100万美元的初创企业"（10%成功率） vs "融资1亿美元的初创企业"（60%成功率）

方法3：显式校准 - 使用公式：成功概率 = 基准率 + k × log(金额)

下一步： 返回菜单

3. Test Status Quo Bias

3. 测试现状偏差

Challenge the assumption that "no change" is the default.

Status Quo Bias Progress:
- [ ] Step 1: Identify status quo prediction
- [ ] Step 2: Calculate energy to maintain status quo
- [ ] Step 3: Invert the default
- [ ] Step 4: Apply entropy principle
- [ ] Step 5: Adjust probabilities

挑战"无变化"是默认情况的假设。

现状偏差测试进度：
- [ ] 步骤1：识别现状预测
- [ ] 步骤2：计算维持现状所需的能量
- [ ] 步骤3：反转默认假设
- [ ] 步骤4：应用熵原理
- [ ] 步骤5：调整概率

Step 1: Identify status quo prediction

步骤1：识别现状预测

Are you predicting "no change"? Examples: "This trend will continue," "Market share will stay the same," "Policy won't change"

Status quo predictions often get inflated probabilities because change feels risky.

你是否在预测"无变化"？ 示例："此趋势将持续"、"市场份额将保持不变"、"政策不会改变"

现状预测的概率往往被高估，因为变化会带来风险感。

Step 2: Calculate energy to maintain status quo

步骤2：计算维持现状所需的能量

The Entropy Principle: In the absence of active energy input, systems decay toward disorder.

Question: "What effort is required to keep things the same?"

Examples:

Market share: To maintain requires matching competitor innovation → Energy required: High → Status quo is HARD
Policy: To maintain requires no proposals for change → Energy required: Low → Status quo is easier

熵原理： 在没有主动能量输入的情况下，系统会向无序状态衰变。

问题： "维持现状需要付出什么努力？"

示例：

市场份额： 维持市场份额需要匹配竞争对手的创新 → 所需能量：高 → 现状难以维持
政策： 维持政策需要没有变革提案 → 所需能量：低 → 现状更容易维持

Step 3: Invert the default

步骤3：反转默认假设

Mental Exercise:

Normal framing: "Will X change?" (Default = no)
Inverted framing: "Will X stay the same?" (Default = no)

Bias check: If P(change) + P(same) ≠ 100%, you have status quo bias.

思维练习：

常规框架： "X会变化吗？"（默认=不会）
反转框架： "X会保持不变吗？"（默认=不会）

偏差检查： 如果P(变化) + P(不变) ≠ 100%，则存在现状偏差。

Step 4: Apply entropy principle

步骤4：应用熵原理

Second Law of Thermodynamics (applied to forecasting):

Ask:

Is this system open or closed?
Is energy being input to maintain/improve?
Is that energy sufficient?

热力学第二定律（应用于预测）：

自问：

这个系统是开放的还是封闭的？
是否有能量输入来维持/改善系统？
这些能量是否足够？

Step 5: Adjust probabilities

步骤5：调整概率

If you detected status quo bias:

For "no change" predictions that require high energy:

Reduce P(status quo) by 10-20%
Increase P(change) correspondingly

For predictions where inertia truly helps: No adjustment needed

The heuristic: If maintaining status quo requires active effort, decay is more likely than you think.

Next: Return to menu

如果检测到现状偏差：

对于需要高能量维持的"无变化"预测：

将P(现状)降低10-20%
相应提高P(变化)

对于惯性确实有帮助的预测： 无需调整

启发法： 如果维持现状需要主动努力，那么衰变的可能性比你想象的更大。

下一步： 返回菜单

4. Audit Confidence Intervals

4. 审核置信区间

Validate that your CI width reflects true uncertainty.

Confidence Interval Audit Progress:
- [ ] Step 1: State current CI
- [ ] Step 2: Run surprise test
- [ ] Step 3: Check historical calibration
- [ ] Step 4: Compare to reference class variance
- [ ] Step 5: Adjust CI width

验证你的置信区间宽度是否反映真实的不确定性。

置信区间审核进度：
- [ ] 步骤1：陈述当前置信区间
- [ ] 步骤2：运行意外测试
- [ ] 步骤3：检查历史校准
- [ ] 步骤4：与参考类别方差比较
- [ ] 步骤5：调整置信区间宽度

Step 1: State current CI

步骤1：陈述当前置信区间

Current confidence interval:

Point estimate: ___%
Lower bound: ___%
Upper bound: ___%
Width: ___ percentage points
Confidence level: ___ (usually 80% or 90%)

当前置信区间：

点估计：___%
下限：___%
上限：___%
宽度：___个百分点
置信水平：___（通常为80%或90%）

Step 2: Run surprise test

步骤2：运行意外测试

The Surprise Test: "Would I be genuinely shocked if the true value fell outside my confidence interval?"

Calibration:

80% CI → Should be shocked 20% of the time
90% CI → Should be shocked 10% of the time

Test: Imagine the outcome lands just below your lower bound or just above your upper bound.

Three possible answers:

A) "Yes, I'd be very surprised" - ✓ CI appropriately calibrated
B) "No, not that surprised" - ⚠ CI too narrow (overconfident) → Widen interval
C) "I'd be amazed if it landed in the range" - ⚠ CI too wide → Narrow interval

意外测试： "如果真实值落在我的置信区间之外，我会真正感到震惊吗？"

校准标准：

80%置信区间 → 应该有20%的概率感到震惊
90%置信区间 → 应该有10%的概率感到震惊

测试： 想象结果刚好低于你的下限或刚好高于你的上限。

三种可能的答案：

A) "是的，我会非常震惊" - ✓ 置信区间校准适当
B) "不，不会那么震惊" - ⚠ 置信区间过窄（过度自信）→ 扩大区间
C) "如果结果落在区间内，我会很惊讶" - ⚠ 置信区间过宽 → 缩小区间

Step 3: Check historical calibration

步骤3：检查历史校准

Look at your past forecasts:

Collect last 20-50 forecasts with CIs
Count how many actual outcomes fell outside your CIs
Compare to theoretical expectation

CI Level	Expected Outside	Your Actual
80%	20%	___%
90%	10%	___%

Diagnosis: Actual > Expected → CIs too narrow (overconfident) - Most common

回顾你过去的预测：

收集最近20-50个带有置信区间的预测
统计实际结果落在置信区间之外的数量
与理论预期值比较

置信水平	预期超出比例	你的实际超出比例
80%	20%	___%
90%	10%	___%

诊断： 实际超出比例 > 预期 → 置信区间过窄（过度自信）- 最常见情况

Step 4: Compare to reference class variance

步骤4：与参考类别方差比较

If you have reference class data:

Calculate standard deviation of reference class outcomes
Your CI should roughly match that variance

Example: Reference class SD = 12%, your 80% CI ≈ Point estimate ± 15%

If your CI is narrower than reference class variance, you're claiming to know more than average. Justify why, or widen CI.

如果你有参考类别数据：

计算参考类别结果的标准差
你的置信区间应大致匹配该方差

示例： 参考类别标准差=12%，你的80%置信区间≈点估计值±15%

如果你的置信区间比参考类别方差窄，意味着你声称自己比平均水平更了解情况。请证明合理性，否则扩大置信区间。

Step 5: Adjust CI width

步骤5：调整置信区间宽度

Adjustment rules:

If overconfident: Multiply current width by 1.5× to 2×
If underconfident: Reduce width by 0.5× to 0.75×

Next: Return to menu

调整规则：

如果过度自信： 将当前宽度乘以1.5到2倍
如果自信不足： 将当前宽度乘以0.5到0.75倍

下一步： 返回菜单

5. Run Full Bias Audit

5. 运行全面偏差审计

Comprehensive scan of major cognitive biases.

Full Bias Audit Progress:
- [ ] Step 1: Confirmation bias check
- [ ] Step 2: Availability bias check
- [ ] Step 3: Anchoring bias check
- [ ] Step 4: Affect heuristic check
- [ ] Step 5: Overconfidence check
- [ ] Step 6: Attribution error check
- [ ] Step 7: Prioritize and remediate

See Cognitive Bias Catalog for detailed descriptions.

Quick audit questions:

全面扫描主要认知偏差。

全面偏差审计进度：
- [ ] 步骤1：确认偏差检查
- [ ] 步骤2：可得性偏差检查
- [ ] 步骤3：锚定偏差检查
- [ ] 步骤4：情感启发式检查
- [ ] 步骤5：过度自信检查
- [ ] 步骤6：归因错误检查
- [ ] 步骤7：优先级排序与修正

详细说明请参见Cognitive Bias Catalog。

快速审计问题：

1. Confirmation Bias

1. 确认偏差

Did I seek out disconfirming evidence?
Did I give equal weight to evidence against my position?
Did I actively try to prove myself wrong?

If NO to any → Confirmation bias detected

我是否主动寻找了反驳自己的证据？
我是否对反对我立场的证据给予了同等权重？
我是否积极尝试证明自己是错的？

如果有任何一个问题答案为否 → 检测到确认偏差

2. Availability Bias

2. 可得性偏差

Did I rely on recent/memorable examples?
Did I use systematic data vs "what comes to mind"?
Did I check if my examples are representative?

If NO to any → Availability bias detected

我是否依赖了近期/容易记住的例子？
我是否使用了系统性数据而非"浮现在脑海中的内容"？
我是否检查过我的例子是否具有代表性？

如果有任何一个问题答案为否 → 检测到可得性偏差

3. Anchoring Bias

3. 锚定偏差

Did I generate my estimate independently first?
Did I avoid being influenced by others' numbers?
Did I adjust sufficiently from initial anchor?

If NO to any → Anchoring bias detected

我是否先独立生成了自己的估计值？
我是否避免了受他人数字的影响？
我是否从初始锚点进行了充分调整？

如果有任何一个问题答案为否 → 检测到锚定偏差

4. Affect Heuristic

4. 情感启发式

Do I have an emotional preference for the outcome?
Did I separate "what I want" from "what will happen"?
Would I make the same forecast if incentives were reversed?

If NO to any → Affect heuristic detected

我对结果是否有情感偏好？
我是否将"我想要什么"与"会发生什么"分开了？
如果激励措施反转，我会做出相同的预测吗？

如果有任何一个问题答案为否 → 检测到情感启发式偏差

5. Overconfidence

5. 过度自信

Did I run a premortem?
Are my CIs wide enough (surprise test)?
Did I identify ways I could be wrong?

If NO to any → Overconfidence detected

我是否进行了事前验尸？
我的置信区间是否足够宽（意外测试）？
我是否识别出了自己可能出错的方式？

如果有任何一个问题答案为否 → 检测到过度自信

6. Fundamental Attribution Error

6. 基本归因错误

Did I attribute success to skill vs luck appropriately?
Did I consider situational factors, not just personal traits?
Did I avoid "great man" narratives?

If NO to any → Attribution error detected

我是否适当区分了成功源于技能还是运气？
我是否考虑了情境因素，而非仅关注个人特质？
我是否避免了"伟人叙事"？

如果有任何一个问题答案为否 → 检测到归因错误

Step 7: Prioritize and remediate

步骤7：优先级排序与修正

For each detected bias:

Severity: High / Medium / Low
Direction: Pushing probability up or down?
Magnitude: Estimated percentage point impact

Remediation example:

Bias	Severity	Direction	Adjustment
Confirmation	High	Up	-15%
Availability	Medium	Up	-10%
Affect heuristic	High	Up	-20%

Net adjustment: -45% → Move probability down by 45 points (e.g., 80% → 35%)

Next: Return to menu

对于每个检测到的偏差：

严重程度： 高/中/低
方向： 推高还是拉低概率？
影响幅度： 估计的百分点影响

修正示例：

偏差	严重程度	方向	调整幅度
确认偏差	高	推高	-15%
可得性偏差	中	推高	-10%
情感启发式	高	推高	-20%

净调整： -45% → 将概率降低45个百分点（例如，80% → 35%）

下一步： 返回菜单

6. Learn the Framework

6. 学习框架

Deep dive into the methodology.

深入了解方法论。

Resource Files

资源文件

📄 Scout vs Soldier Mindset

Julia Galef's framework
Motivated reasoning
Intellectual honesty
Identity and beliefs

📄 Cognitive Bias Catalog

20+ major biases
How they affect forecasting
Detection methods
Remediation strategies

📄 Debiasing Techniques

Systematic debiasing process
Pre-commitment strategies
External accountability
Algorithmic aids

Next: Return to menu

📄 Scout vs Soldier Mindset

Julia Galef的框架
动机性推理
理智诚实
身份与信念

📄 Cognitive Bias Catalog

20+种主要偏差
它们如何影响预测
检测方法
修正策略

📄 Debiasing Techniques

系统化去偏差流程
预承诺策略
外部问责
算法辅助工具

下一步： 返回菜单

Quick Reference

快速参考

The Scout Commandments

Scout准则

Truth over comfort - Accuracy beats wishful thinking
Seek disconfirmation - Try to prove yourself wrong
Hold beliefs lightly - Probabilistic, not binary
Update incrementally - Change mind with evidence
Separate wanting from expecting - Desire ≠ Forecast
Check your work - Run bias audits routinely
Stay calibrated - Track accuracy over time

Scout mindset is the drive to see things as they are, not as you wish them to be.

真相优先于舒适 - 准确性胜过一厢情愿
寻找反驳证据 - 尝试证明自己是错的
轻持信念 - 用概率而非二元视角看待事物
逐步更新认知 - 根据证据改变想法
区分欲望与预期 - 渴望≠预测
检查你的工作 - 定期运行偏差审计
保持校准 - 随时间跟踪准确性

Scout Mindset是看清事物本来面目的驱动力，而非将事物看作你期望的样子。

Resource Files

资源文件

📁 resources/

scout-vs-soldier.md - Mindset framework
cognitive-bias-catalog.md - Comprehensive bias reference
debiasing-techniques.md - Remediation strategies

Ready to start? Choose a number from the menu above.

📁 resources/

scout-vs-soldier.md - 思维框架
cognitive-bias-catalog.md - 全面的偏差参考
debiasing-techniques.md - 修正策略

scout-mindset-bias-check

Original

Translation

Scout Mindset & Bias Check

Scout Mindset与偏差检查

Table of Contents

目录

What is Scout Mindset?

什么是Scout Mindset？

When to Use This Skill

何时使用此工具

Interactive Menu

交互式菜单

Core Workflows

核心工作流

1. Run the Reversal Test

1. 运行反转测试

Step 1: State your current conclusion

步骤1：陈述当前结论

Step 2: Identify supporting evidence

步骤2：识别支持性证据

Step 3: Reverse the evidence

步骤3：反转证据

Step 4: Ask "Would I still accept it?"

步骤4：自问"我是否仍会接受它？"

Step 5: Adjust for double standards

步骤5：修正双重标准

2. Check Scope Sensitivity

2. 检查范围敏感性

Step 1: Identify the variable scale

步骤1：识别变量规模

Step 2: Test linear scaling

步骤2：测试线性缩放

Step 3: Check reference point calibration

步骤3：检查参考点校准

Step 4: Validate magnitude assessment

步骤4：验证量级评估

Step 5: Adjust for scope insensitivity

步骤5：修正范围不敏感性

3. Test Status Quo Bias

3. 测试现状偏差

Step 1: Identify status quo prediction

步骤1：识别现状预测

Step 2: Calculate energy to maintain status quo

步骤2：计算维持现状所需的能量

Step 3: Invert the default

步骤3：反转默认假设

Step 4: Apply entropy principle

步骤4：应用熵原理

Step 5: Adjust probabilities

步骤5：调整概率

4. Audit Confidence Intervals

4. 审核置信区间

Step 1: State current CI

步骤1：陈述当前置信区间

Step 2: Run surprise test

步骤2：运行意外测试

Step 3: Check historical calibration

步骤3：检查历史校准

Step 4: Compare to reference class variance

步骤4：与参考类别方差比较

Step 5: Adjust CI width

步骤5：调整置信区间宽度

5. Run Full Bias Audit

5. 运行全面偏差审计

1. Confirmation Bias

1. 确认偏差

2. Availability Bias

2. 可得性偏差

3. Anchoring Bias

3. 锚定偏差

4. Affect Heuristic

4. 情感启发式

5. Overconfidence

5. 过度自信

6. Fundamental Attribution Error

6. 基本归因错误

Step 7: Prioritize and remediate

步骤7：优先级排序与修正

6. Learn the Framework