Prospect Theory

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Prospect Theory is the "Psychology of Choice"—the Nobel Prize-winning discovery that humans don't make "Rational" decisions based on math, but "Emotional" decisions based on "Gains and Losses." Developed by Daniel Kahneman and Amos Tversky, it challenges the idea of the "Economic Man" who always picks the best option. Instead, it proves that we "Hate losing" much more than we "Love winning" (Loss Aversion) and that we think about "Changes" in our wealth rather than our "Total" wealth. From the way we "Hold onto bad stocks" to how we "Fear rare risks," prospect theory is the foundation of behavioral economics. It is the science of why we are "Predictably Irrational" when it comes to money and life.

Remembering

• Prospect Theory — A theory that describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known.
• Loss Aversion — The phenomenon where "Losses loom larger than gains"; the "Pain" of losing $100 is twice as strong as the "Joy" of gaining $100.
• Reference Point — The "Starting point" from which you judge a gain or a loss (e.g., your current bank balance or your expected salary).
• Diminishing Sensitivity — The idea that the difference between $0 and $100 feels "Huge," but the difference between $1,000 and $1,100 feels "Small."
• Probability Weighting — The tendency to "Over-estimate" small chances (like the Lottery or a Plane crash) and "Under-estimate" high chances (like the flu).
• Certainty Effect — The preference for "Sure things" over "Risks," even if the risk has a higher mathematical value.
• Framing Effect — How the "Way a choice is presented" changes the decision (e.g., "90% Fat-Free" vs "10% Fat").
• Daniel Kahneman — The psychologist who won the Nobel Prize in Economics for this theory (author of 'Thinking, Fast and Slow').
• Amos Tversky — Kahneman's brilliant partner who co-developed the theory.
• The Value Function — The "S-shaped" curve that shows how we perceive value differently for gains (concave) and losses (convex).

Understanding

Prospect theory is understood through Loss and Framing.

1. Loss Aversion (The "Pain" of Money): Humans are "Risk-Averse" for gains but "Risk-Seeking" for losses.

• **Gains**: If I offer you $50 for sure, or a 50% chance at $100, you will take the **Sure $50**.
• **Losses**: If I tell you that you will lose $50 for sure, or have a 50% chance to lose $100, you will **Take the Risk** to avoid the loss.
• This is why people "Keep gambling" when they are losing—they are "Seeking risk" to get back to zero.

2. The Reference Point (Context is Everything): You don't care about your "Total Net Worth."

• If you expect a $10,000 bonus and get $5,000, you feel "Poor" (A Loss).
• If you expect no bonus and get $1,000, you feel "Rich" (A Gain).
• Happiness is determined by "Changes," not by "Levels."

3. Probability Weighting (The Lottery Effect): We are "Bad" at understanding small numbers.

• A "1 in a Million" chance of winning a lottery feels "Possible."
• A "1 in a Million" chance of a side effect from a vaccine feels "Scary."
• We "Over-react" to the "Ends" of the distribution and "Under-react" to the "Middle."

The 'Diseases' Experiment': Kahneman and Tversky asked people to choose between two treatments for a disease. Group A was told: "200 people will be saved." Group B was told: "400 people will die." Even though the math was the same, people chose the first option because it was "Framed as a Gain." This proved that "Words matter more than Math" in human choice.

Applying

Modeling 'The Loss Aversion' (Predicting if a person will accept a bet): <syntaxhighlight lang="python"> def evaluate_gamble(win_amount, lose_amount, probability=0.5):

   """
   Shows that people need a 2x Gain to justify a 1x Loss.
   """
   # Rational Utility: (Win * P) - (Lose * (1-P))
   rational_utility = (win_amount * probability) - (lose_amount * (1 - probability))
   
   # Behavioral Utility: (Win * P) - (2 * Lose * (1-P))
   # Note: The '2' is the 'Loss Aversion Coefficient'
   psychological_utility = (win_amount * probability) - (2 * lose_amount * (1 - probability))
   
   if psychological_utility > 0:
       return f"Rational: {rational_utility} | Behavioral: {psychological_utility} | RESULT: TAKE THE BET."
   else:
       return f"Rational: {rational_utility} | Behavioral: {psychological_utility} | RESULT: REJECT THE BET."

1. A 'Fair' bet: Win 100, Lose 100.

print(evaluate_gamble(100, 100))

1. A 'Good' bet: Win 250, Lose 100.

print(evaluate_gamble(250, 100)) </syntaxhighlight>

Prospect Landmarks

The 'Endowment Effect' → Why you think your "Old Coffee Mug" is worth $10 when everyone else thinks it's worth $2. Because you "Own it," losing it feels like a "Loss," so you over-value it.

Sunk Cost Fallacy → Why we "Stay in a bad relationship" or "Finish a bad movie." We don't want the "Past time/money" to be a "Loss," so we "Waste more time" trying to save it.

Insurance Sales → How companies sell you "Extended Warranties." They frame a $50 payment as "Protection against a $500 Loss," hitting your "Loss Aversion" button.

The 'Status Quo' Bias → Why we "Don't change our bank" or "Our internet provider," even if we can save money. Changing feels like a "Risk," so we "Stick with what we know."

Analyzing

Rational Man vs. Behavioral Human

Feature	Rational (Expected Utility)	Behavioral (Prospect Theory)
Goal	Maximize Total Wealth	Avoid "Losses" relative to a point
Risk View	Consistent (Always the same)	Inconsistent (Depends on Gain/Loss)
Probabilities	Handled Linearly (0.5 = 0.5)	Weighted (0.01 feels like 0.05)
Analogy	A 'Calculator'	A 'Storyteller'

The Concept of "Reflection": Analyzing the "Mirror Effect." If you "Flip" a positive problem into a negative one, the human brain "Flips" its behavior. We move from "Playing it Safe" (Gains) to "Gambling Everything" (Losses). This explains why a "Losing Gambler" becomes "More and More Dangerous" as the night goes on.

Evaluating

Evaluating prospect theory:

1. The "Expert" Defense: Do "Experts" (like Professional Traders) still suffer from loss aversion, or can you "Train" the brain to be rational?
2. Culture: Does a "Culture of Honor" or "Scarcity" make loss aversion "Stronger"?
3. Happiness: If we only care about "Changes," can we ever be "Permanently Happy," or are we "Doomed" to the "Hedonic Treadmill" (needing more and more to feel a 'Gain')?
4. AI: Should we program AIs to be "Rational" (like a calculator) or "Behavioral" (to understand and help humans)?

Creating

Future Frontiers:

1. Loss-Aversion "Therapy": Using VR to "Practice" losing small amounts of money to "De-sensitize" the brain, allowing for more rational investing.
2. Hyper-Personalized Nudges: An app that "Knows your reference point" and "Frames" healthy choices (like Exercise) as a "Gain" for you specifically.
3. Policy Design: Governments that "Auto-Enroll" people in savings accounts, using the "Status Quo Bias" to help people save for retirement.
4. The 'Rationality' Assistant: A real-time AI that "Flags" your decisions: "Warning: You are only making this choice because you are afraid of a loss. The math says you should take the risk."