Behavioral Game Theory
Behavioral Game Theory looks at how people really make choices.
It helps us understand:
- How real people act when they don't use perfect logic
- How fairness and trust change the game
In regular Game Theory, we pretend everyone is a math wizard. We assume players only care about winning. But in the real world, people have feelings! They learn from mistakes. They care about what others think.
The Ultimatum Game
Imagine two players. One person decides how to split a sum of money. The other person can either accept or reject the offer.
Here are the rules:
- If the partner says yes, both players get the cash
- If the partner says no, nobody gets anything
What's a fair offer?
... think about it for a bit ...
Old-school Game Theory says you should offer the smallest amount possible. Just one penny!
But real people don't act like computers. Behavioral Game Theory shows that players often reject unfair offers.
They would rather get nothing than let someone treat them poorly. They value fairness over pure cash.
Example: The Trust Game
In this game, Player 1 can send money to Player 2. Any money sent gets tripled. Then, Player 2 decides how much to send back.
- If Player 1 sends $10, it turns into $30 for Player 2
- Player 2 can be nice and send $15 back, or keep all $30
Strict logic says: "Don't send any money! Player 2 will just keep it." But guess what? Real studies show that many people do trust each other. And it pays off!
Loss Aversion (We Hate Losing!)
People hate losing more than they love winning. This is called Loss Aversion.
Losing $20 feels awful. Finding $20 feels good. But the bad feeling of losing is much stronger than the good feeling of winning! Because of this, players often pick a safe choice.
Example: Playing It Safe
Imagine you must choose:
- Safe choice: Get $5 for sure
- Risky choice: A coin flip. Heads you win $20, tails you lose $5
The risky choice looks better on paper since the math says it is worth $7.50 on average. Yet, many people still take the safe $5. They just really want to avoid losing.
Loss aversion: The pain of losing $5 hurts more than the joy of winning $20.
Example: A Game Show Choice
A player wins $1,000. The host offers one last gamble:
- 50% chance to walk away with $2,000 total
- 50% chance to drop down to $500
Many people say no. They don't want to risk losing $500, even though they could double their cash.
This is why big companies often stay stuck. They stick to old habits instead of trying new things. They fear the risk.
Real World Examples
Example: Group Habits
Sometimes people follow a system just because everyone else does it. It feels safe.
- Teaching only for a test instead of true learning
- Scrolling through phone feeds out of pure habit
- Staying late at work just to look busy
Example: Selling Too Soon
An investor buys stock for $50.
- If it goes up to $60, they sell fast to lock in the gain
- If it drops to $40, they hold onto it. They hope it goes back up
They hate admitting a loss. So they hold onto a bad stock for too long.
Loss aversion: Losing $10 feels twice as bad as making $10 feels good.
Example: Sunk Costs
A company spends $1 million on a new product. Then they discover the product will fail.
Stopping now saves money later. But managers often keep spending money anyway! They don't want to admit the $1 million is gone.
Loss aversion: People spend more cash just to avoid facing a loss.
This is called throwing good money after bad.
In math and economics, we call this the sunk cost fallacy. Money spent is gone. It should not affect your next choice.
Look forward, not backward!
When we know how humans really work, we can make better rules and systems.
Conclusion
Behavioral Game Theory teaches us that math is only half the puzzle. To win the game, you have to understand people!