Transcript Behavioral Game Theory Networked Life CSE 112 Spring 2004 BGT lectures by Nick Montfort for Prof.

Behavioral Game Theory
Networked Life
CSE 112
Spring 2004
BGT lectures by Nick Montfort
for Prof. Michael Kearns
Another experiment…
6 people to the front, in 3 pairs,
for quick economic games…?
$$$
The rules
1. A coin flip determines who is player 1.
2. Player 1 chooses to take some number
of quarters between 0 and 10.
3. Player 2 gets the rest of the 10
quarters.
That’s it.
This is a one-shot game.
The 3 pairs at the front play 1 game each.
Behavioral Game Theory
and Game Practice
• Game theory:
how rational individuals should behave
• Who are these rational individuals?
• Behavioral game theory:
Look at how people actually behave
– experiment by setting up real economic situations
– account for people’s economic decisions
– don’t break game theory when it works
• Fit a model to observations, not “rationality”
Playing “Dictator”
Sit
there
Take
0
Take
1
Take
2
Take
3
Take
4
Take
5
Take
6
Take
7
Take
8
Take
9
Take
10
10,0
9,1
8,2
7,3
6,4
5,5
4,6
3,7
2,8
1,9
0,10
• The Dictator Game is a game
• It has a unique equilibrium:
– Player 1, the column player, takes everything
– No move player 2 can make makes player 2 better off
– No move player 1 can make makes player 1 better off
Formal, turn-based games can be considered as
matrix games…
Extensive and Matrix Form
• A formal game with players and moves can be written in
extensive form
X
10,9
Player 2
A
Y
Player 1
B
X
Player 2
Y
0,10
5,5
A
B
A:X
B:X
10,9
5,5
A:X
B:Y
10,9
5,5
A:Y
B:X
0,10
5,5
A:Y
B:Y
0,10
5,5
5,5matrix
to
• All such games can be converted
form…
• A “move” is a complete strategy for playing the game
Ultimatum Bargaining
From the first day of class…
• Player 1 chooses some division of a surplus (of size 10)
• Player 2 can either accept or reject the division
•
•
•
•
How many moves for Player 1?
How many for Player 2?
What does the matrix look like?
What equilibrium or equilibria exist?
How People Play Dictator
First experiment: Kahneman et al. 1986
Player 1 can only choose ($10,$10) or ($18,$2)
3/4 of dictators chose the equal split
People are generous!
…or maybe not. Only 10% were paid (?), choice was limited …
Forsyth et al. 1994
Mean allocation of dictators is only about 20%!
Frohlich and Oppenheimer 1997
Canadians gave 27%, Americans only 16%
Anonymity from experimenters = lower offers from dictators
How People Ultimatum-Bargain
Thousands of games have been played in experiments…
•
•
•
•
In different cultures around the world
With different stakes
With different mixes of men and women
By students of different majors
Pretty much always, two things prove true:
1.
2.
Player 1 offers close to, but less than, half (40% or so)
Player 2 rejects low offers (20% or less)
Ultimatum Bargaining across Cultures
Sharing norms differ in the industrialized world
Japan, Israel lowest (Roth et al. 1991)
Machiguenga farmers in Peru (Henrich 2000)
Offered 26% on average, accepted all but 1 offer
Very socially disconnected
Ache in Paraguay, Lamelara in Indonesia
Made hyperfair (more than 50%) offers
Headhunters (potlatch culture), whalers
Ultimatum Bargaining across Majors
Economics majors offer 7% less, accept 7% less
(Carter and Irons 1991)
They must have learned game theory!
… but this behavior is consistent across years of study
(freshman to seniors) … maybe their game-theoretic
nature made them want to study economics?
Other studies show no correlation, or that econ/business
students offer more.
Ultimatum Bargaining and Looks
70 University of Miami students, photographed and rated
for attractiveness (Schweitzer and Solnick 1999)
Man as player 1, attractive woman as player 2…
Doesn’t make much difference
Woman as player 1, attractive man as player 2…
Average offer is 50.7% (hyperfair!)
Small percentage (1 or 2?) offer almost everything
Stakes, Entitlement, Framing
Indonesia: from a day’s wages to a month’s wages
No difference…
Florida: answer questions to get $400 pie instead of $20
More low offers at $400 … but subjects earned it
Framing it as a buyer/seller exchange lowers offers 10%
Framing it as a resource competition raises them slightly
(Hoffman et al. 1994)
Two Problems with Game Theory
1. Doesn’t explain the dictator game
2. Doesn’t explain ultimatum bargaining
Can it still help us outsmart people who don’t
play game-theoretically?
Generally, no. It can only help us beat
“rational” opponents — not real people.
Does adding something non-strategic like
“altruism” fix these problems?
It had better fix the dictator game!
But it isn’t enough for ultimatum bargaining.
A New Theory…
We could create new per-game theories…
But this would be useless.
We could consider these as repeated games of some
sort…
But that complicates a lot of things.
Maybe we can make a small change to something
underlying…
What if people don’t only care about their own payoffs?
A New Theory of Utility
Consider that people still like their payoffs
They also dislike others having more money, with some coefficient .
And they dislike having more money than others, with coefficient .
U_1 is player 1’s utility; P_1 & P_2 are the players’ payoffs.
U_1 = P_1 - (max[P_2 - P_1, 0]) - (max[P_1 - P_2,0])
 is “envy”
 is “guilt”
0 <=  < 1
<
Different players can have different  and 
Inequality Aversion
U_1 = P_1 - _1(max[P_2 - P_1, 0]) - _1(max[P_1 - P_2,0])
(Fehr and Schmidt 1999)
Now, we can do classical game theory, but with U, not P
Player 2 should reject any offer < _2/(1 + 2_2)
If  = 1/3, player 2 should reject any offer less than 20%
Player 1 offers will depend on
Estimates of player 2 envy (_2) distribution
and Player 1 guilt (_1)
Inequality Aversion: Advantages
•
•
•
•
Model generalizes easily to more than 2 players
 = 1/3,  = 0 can explain a lot!
• Ultimatum bargaining
• Multi-player ultimatum bargaining (“Market game”)
• Even dictator games
Parameters can be tuned for cultures or individuals
Does not break most of the existing, correct
predictions of non-IA game theory
Inequality Aversion: A Problem…
Consider the following game:
Player 1 can give 5% or 90% (no other options)
Player 2 can accept or reject
Will Player 2 reject an offer of 5%?
What if Player 1 could give only 10% or 95%?
Still, we’re making progress.
Inequality Aversion on Graphs
Finally! We’re getting back to networks...
For games where IA game theory works,
we could put these games on graphs.
Do players care about global inequalities
or neighborhood inequalities?
Our guesses may agree, but it’s an open
question: no experiment has been done!
Next time…
• Experiment
• Discussion of different (more
interesting?) multi-player games…
• …and learning in games…
• …and attempts at randomness.
Another experiment…
5 people to the front
for quick economic games…?
$$$
The rules
1. All 5 people move at once by writing down a
number between 0 and 100.
2. The one with the number that is closest to
2/3 of the average is the winner, getting $1.
3. If several people all are closest, Kilian will
pick a winner randomly using random.org.
That’s it.
We will repeat this game.
First, to return to
Ultimatum Bargaining…
To clarify:
•
•
Player 1 offers 4
Player 2 accepts 4 or greater but rejects 3 or less
This is a Nash equilibrium.
So is any set of actions where player 1 offers N
and player 2 accepts N (no matter what
player 2 does in the other cases.)
But it isn’t a subgame perfect equilibrium.
Subgames
X
10,9
Player 2
A
Y
Player 1
B
X
0,10
5,5
Player 2
Y
5,5
This game has two (proper) subgames.
More interesting subgames
Player 2
High
Low
A
B
Player 2 High
Player 1
Low
C
Player 2
High
Low
3,5
3,0
2,5
2,0
3,5
3,0
A game with three proper subgames. (A; A:High, B:Low, C:Low) is a NE.
But doesn’t A:High, B: High, C:High seem like the only “right” thing for
Player 2 to do?
Subgame perfect equilibria
How many proper subgames are there in Ultimatum
Bargaining (10 unit surplus)?
What is the dominant strategy for player 2 in each one?
Which other games is this sensible for:
• Prisoner’s dilemma?
• Rock, paper, scissors?
• Dictator?
• The game we played at the beginning of class?
Only turn-based games that actually have proper subgames.
Beauty Contest
Some number of players try to guess a number that is 2/3
of the average guess.
The answer can’t be between 68 and 100 - no use guessing
in that interval. It is dominated.
But if no one guesses in that interval, the answer won’t be
greater than 44.
But if no one guesses more than 44, the answer won’t be
greater than 29…
Everyone should guess 0! And good game theorists would…
But they’d lose…
Iterated Dominance
People don’t instantly compute all the way to 0
The median subject uses 1 or 2 rounds of iteration (25, 35)
Guessing 0 on the first round (game theorist) is poor
Guessing 30 (behavioral game theory) is much better
But 30 isn’t a good guess the seventh time you play…
A Digression: Randomness
Another experiment. No money this time, but this is going
to help you win at RPS!
Everyone, count off into two groups…
Half the class (group 1):
Flip a coin 20 times and record the results.
The other half (group 2):
Write down a sequence that you think looks like 20
random coin flips.
Write the sequence on paper; write “1” or “2” on the back.
Subjective Randomization
People randomize better when they’re paid to be random
World RPS championship: probably a good entropy pool!
Binary random choices (simulating coin flips):
• Often come up exactly 50/50
• Have too few runs of identical choices: (n+1)/2 expected
• Have longest runs that are too short (5 or 6 per 20)
Alternating (“negative recency”) is a common artifact
Oddly, children seem to learn this around grade 5!
Back to Multiplayer Games:
Centipede
A four-turn game with 5 possible payoffs:
1.
2.
3.
4.
P1 can take (0.4,0.1) or pass
P2 can take (0.2,0.8) or pass
P1 can take (1.6,0.4) or pass
P2 can take (.8,3.2) or pass (6.4,1.6)
What should happen?
What does happen? Cond. prob of take:
0.06 0.32 0.57 0.75 in trials 1-5
0.08 0.49 0.75 0.82 in trials 6-10
Continental Divide
A group of 7 plays the game 15 times
Players pick numbers between 1-14; they win by
coordinating — choosing close to the median
Two “peaks” of payoff provide equilibira points, at
3 (payoff 60) and 12 (payoff 112)
Between 7 and 8 is the “divide”
A few people who have 7 as a lucky number can
drive the group to the low-payoff equilibrium
Or, a few choosing 8 can lead to the better one
Learning
How do people learn as they play economic games?
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•
•
Evolution: They don’t! (But can die off or prevail)
Reinforcement: Choices that pay off well (and maybe similar ones)
are made more likely
Belief learning: Build a model of the opponent
•
•
•
•
•
Fictitious play
Cournot best-response dynamics
Imitation: Do what the most successful player is doing
Rule Learning: Reinforce general rules, not actions
Experience-Weighted Attraction: Integrates RL & Belief
(And there are others…)
Evolution
Evolutionary approaches are actually the opposite of
learning
Individuals have fixed strategies they are born with
The population as a whole evolves, but individuals do not
learn — they either die or survive
Some machine learning researchers have considered
merging evolutionary approaches and learning:
let some individuals have the capability for learning
(Cervone et al. 2000)
Reinforcement Learning
In the simplest form:
• Make actions that result in high payoffs more likely
We could smooth the probabilities to neighboring actions
(if we know what “neighboring” means)
One problem with a pure RL approach: People learn actions
that are never reinforced
Belief Learning
Two extremes:
• Fictitious play
Build a model of what the other player does based on
past history (25% action A, 50% action B…)
•
•
•
Cumulate (1st action is as important as most recent)
Weight more recent actions more heavily/discount the past
Cournot best-response dynamics
Assume your opponent will repeat the last action, and
pick the best respose to that
Imitation
May explain a lot of business activity…
What looks like imitation may be a simplified form of other
learning, or one heuristic of many
Can’t explain innovation (or how the best player plays)
Rule Learning
Instead of learning strategies directly, learn decision rules
that allow you to choose strategies
Decisions are made using a mixture of experts
One way to model how different ways of learning are
combined…
Experience Weighted Attraction
Another way to integrate learning approaches:
(Camerer and Ho, 1999)
•
•
One parameter mixes between belief and reinforcement learning
One adjusts between cumulative beliefs and weighted beliefs
(Two other parameters…)
A functional version sets these four parameters from past
history and a single parameter.
A good fit, and suggests how to integrate different types
of learning.