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Prospect Theory Applied: Using Loss Aversion and Framing to Make Better Choices
How-ToMachine Learning

Prospect Theory Applied: Using Loss Aversion and Framing to Make Better Choices

via Dev.to王凯1mo ago

Daniel Kahneman and Amos Tversky's Prospect Theory is not just academic -- it is a practical tool for understanding why we make the choices we do and how to make better ones. Key Insights from Prospect Theory Loss aversion : Losses feel about twice as painful as equivalent gains feel good. Losing 100 dollars hurts more than finding 100 dollars feels good. This asymmetry drives many decision patterns. Reference dependence : We evaluate outcomes relative to a reference point, not in absolute terms. A salary of 80,000 dollars feels great if you expected 70,000 but terrible if you expected 90,000. Diminishing sensitivity : The difference between 100 and 200 dollars feels larger than the difference between 1,100 and 1,200 dollars, even though both are 100 dollars. Probability distortion : We overweight small probabilities (lottery tickets, unlikely disasters) and underweight moderate to high probabilities. Practical Applications Negotiation : Frame offers as gains from a lower reference poi

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