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18.Taming Intuitive Predictions - Coggle Diagram
18.Taming Intuitive Predictions
Kahneman's Prediction Protocol
đź§
Step 1: Base Case
:
Define all
base information
.
Estimate the
average scenario
assuming no additional data.
Step 2: Influence of Available Information
:
Estimate the
maximum influence
of available data.
Discard irrelevant information
immediately.
Step 3: Adjustment for Uncertainty
:
Adjust predictions cautiously.
Adjustments should be smaller if the correlation is
weak
or the data is
unreliable
.
Understanding Regression to the Mean
📉
Definition
:
Extreme outcomes are often followed by outcomes closer to the average.
How It Works
:
After extreme events, natural variability causes a return to average results.
Why It’s Hard to Grasp
:
People often attribute natural statistical effects to
causal factors
.
Implication for Predictions
:
Caution is needed when interpreting extreme results, as they often regress to the mean.
Practical Application of Prediction
⚖️
Causal vs. Statistical Thinking
:
People often mistakenly explain regression effects with causal reasoning.
Kahneman emphasizes
statistical reasoning
over intuitive causes.
Limiting Extreme Predictions
:
Kahneman’s approach encourages
avoiding predictions for rare events
.
Example: Predicting something like the
next unicorn startup
is statistically unwise.
Key Takeaways
đź“š
System 1's Influence
:
People often rely on
intuitive judgments
from System 1, which leads to
overconfidence
.
Adjusting Predictions
:
By following a structured approach, predictions become more grounded and realistic.
Regression to the Mean
:
Recognize that extreme events often return to average outcomes, reducing the bias in predictions.
Avoid Rare Predictions
:
Focus on
probable
events rather than highly
improbable
ones to improve accuracy.