PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Decision Theory II.
PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Normative Decision Theory A...
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Transcript of PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Normative Decision Theory A...
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Normative Decision Theory
A prescriptive theory for how decisions should be made to maximize the value of
decision outcomes for an individual
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Decision Theory
• Quantify preferences on outcomes s– U(s,a)
• Quantify Beliefs about outcomes of actions– P(s|O,A) where
• O are observations• A are actions
• Decision making principle:– Choose A that Maximizes Expected Utility
• Needs link between s & A, s’ = T(s,A)
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Utility Matrix
OUTCOMES
AC
TIO
NS
€
U(a,s)
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Can we boil all good down to a number?• Probably not.• Different kinds of utility (Kahneman):
– Experienced utility• E.g. Pain during treatment
– Remembered utility• E.g. Pain remembered after treatment
– Predicted utility• Do people know what will be good for them?
– Decision utility• Do people use their knowledge when making decisions?
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Fundamental EquationValue of a decision = Expected Utility of making an action A, where the expectation (average) is carried out over the possibleoutcomes of that action.
s : state of the worldO: observationA : action
€
E s U(A | s,obs)[ ] = P(s = Result(A) | obs,Do(A))U(s
∑ s = Result(A),A)
s = Result(A) is the sth possible outcome of action A
€
V = E U[A | s,O][ ] = P(s | O,A)Transition Matrix1 2 4 3 4 U(s,A)
Utility Function1 2 3
s
∑
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Preference Nomenclature
Lotteries:A lottery is a probabilistic mixture of outcomes
Ordering using lotteries
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Utility Theory Axioms 1
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Utility Axioms 2
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
What do the Axioms do?They Guarantee:
1) Utility principle
2) Maximum expected utility principle
Utility of a lottery is the expection of the utilities
There exists a monotonic function that numerically encodes preferences
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
An Example: You bet your what?
You just won $1,000,000
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
An Example: You bet your what?
You just won $1,000,000
BUTYou are offered a gamble:
Bet your $1,000,000.00 on a fair coin flip.
Heads: $3,000,000
Tails: $0.00
What should you do?
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Problem AnalysisExpected monetary gain = 0.5* $0 + 0.5* $ 3,000,000 =
$1,500,000
$1,500,000 > $ 1,000,000 !Will you take the bet now?How much do you need as a pay off?
Utility theory posits lotteries that result in indifference, and in taking the bet.
Let Sk be your current wealth. Let U(Sk) = 5; U(Sk +3,000,000) = 10; U(Sk+1,000,000) = 8;
€
EU(Accept) = 12 U(Sk ) + 1
2 U(Sk +3,000,000)
EU(Accept) = U(Sk +1,000,000)
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Bernoulli’s Game
Given a fair coin
I will toss this coin N timesuntil it comes up heads.
Your payoff = 2N
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Game Analysis
You should be willing to bet any finite amount?
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Measured Utility function
Utility function measured using lotteries for “Mr. Beard”Grayson, 1960
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Some ViolationsGame 1:
A: 80% chance winning $4000B: 100% chance winning $3000
Result B>ASo 0.8 U(x+$4000) < U(x+$3000)
Game 2:C: 20% chance winning $4000D: 25% chance winning $3000
Result C preferred to DSo (0.2/0.25) U(x+$4000) > U(x+$3000) 0.8 U(x+$4000) > U(x+$3000)
For people, preferences are sometimes a function of the probability
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Another Violation
Lack of Independence of Irrelevant alternatives
Salmon $12.50
Steak $25.00
If restaurant is first-rate, Steak > Salmon
Restaurant looks kind of seedy => salmon
Waiter comes back and says he forgot to say they have snails and frog’s legs
Man says “I’ll have the steak”
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Multi-attribute Utility
Hopefully fi(xi) are simply like addition
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Utility functions
Perception:
Utility measured by correctness of inference
Utility measured by perceived energy expenditure
Action
Utility end point accuracy
Utility measured by minimum energy expenditure
Social
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Utility functions for attractiveness?
But what’s the use in beauty?Money?What else is there?
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PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005
Rational Mate Choice?