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Decision Trees & Utility Theory
Michael C. Runge USGS Patuxent Wildlife Research Center
Advanced SDM Practicum
NCTC, 12-16 March 2012
Motivation: Risk
IsSJ
Manage
in situ
Captive
breeding
Introduce to
new island
Persist
Extinct
Ecol.
Damage
Persist
Extinct
Persist
Extinct
Works
Fails
Ecol.
Damage
New Isl.
Augment
Outline
Decision trees
Utility curves
Eliciting utility curves
Utility functions
Multi-attribute utility
Cognitive challenges
A few other thoughts…
Add new technology
to a hatchery?
Does
it
work
?
40,000
Fry
70,000
Fry
10,000
Fry
Decision Tree
Yes
Yes
No
No
p = 0.8
p = 0.2
Actions
Objectives
Model
Yes (0.1)
No (0.9)
Yes (0.7)
No (0.3)
Yes (0.4)
Yes (0.8)
No (0.6)
No (0.2)
Wild Fire? Wet Year? Control Burn?
Yes
Yes (0.1)
No
No (0.9)
No (0.9)
Yes (0.1)
Value
0.70
1.00
0.25
0.35
0.90
0.20
0.10
0.50
0.07
0.90
0.175
0.105
0.36
0.16
0.30
0.02
Wild Fire? Wet Year? Control Burn?
Yes
Yes (0.1)
No
No (0.9)
No (0.9)
Yes (0.1)
Control Burn?
Yes
No
0.349
0.228
Roll-back Method:
Start at right
EV at chance nodes
Best at choice nodes
Move left until done
Does EV capture values?
Choice
Game
1
$30
-$1
Game
2
$2000
-$1900 Expected values
Game 1: $14.50 Game 2: $50.00
Which do you choose?
Expected Value
The expected value criterion
• Assumes a long-run average
• Assumes a linear value function
• Focuses on only a single attribute
But maybe…
• We make repeated decisions in our
life…
Risk Attitude
Consider the following wager • Win $500 with prob 0.5, or lose $500 with prob 0.5
• Would you pay to get out of this wager? How much?
• Would you pay to get into this wager? How much?
A classic risk decision
$500
Win
? Yes
Yes
No
No
p=0.5
EV = ?
EV = $0
Bet?
p=0.5 -$500
-$?
Risk Attitude
Risk-averse • You would trade a gamble for a sure amount that is
less than the expected value of the gamble
• E.g., buying insurance
Risk-seeking • You would trade a sure amount for a gamble that
has a smaller expected value (but the chance of a larger payout)
• E.g., buying lottery tickets
Add new technology
to a hatchery?
Does
it
work
?
40,000
Fry
70,000
Fry
10,000
Fry
Decision Tree
Yes
Yes
No
No
p = 0.8
p = 0.2
EV = 40K
EV = 58K
Add new technology
to a hatchery?
Does
it
work
?
40,000
Fry
70,000
Fry
10,000
Fry
Risk-averse Utility
Yes
Yes
No
No
p = 0.8
p = 0.2
EU = 0.9
EU = 0.8
U = 1.0
U = 0.0
U = 0.9
Trade a gamble with
expected value of 58K
for a sure thing with a
value of 40K
Properties of Utility Functions
Monotonic vs. peaked
Risk tolerance
• Averse, neutral, seeking
• Mixed
Constant vs. declining aversion
Eliciting Utilities
Elicitation methods center around gamble choices • Notation: [x, , y] R w
• The choice is between a sure return of w or gamble that returns x with probability or y with probability 1
• R is the preference relation (, , or ~)
Lottery diagram
x
Choice
y
w
1
Methods of Elicitation
Preference comparison • [xi, i, yi] Ri wi
Probability equivalence • [xn+1, i, x0] ~ xi
Value equivalence
Certainty equivalence • [x*, 0.5, x0] ~ x1, [x1, x0] ~ x2, [x*, x1] ~ x3,…
Probability-equivalence
w -10,000 0 10,000 30,000 60,000
0.60 0.85
u(w) 0.0 1.0
60,000
Choice
-10,000
w
Probability-equivalence
w -10,000 0 10,000 30,000 60,000
0.35 0.60 0.85
u(w) 0.0 1.0
60,000
Choice
-10,000
w
𝑢 30,000 = 𝛼𝑢 60,000 + 1 − 𝛼 𝑢 −10,000
= 𝛼 1.0 + 1 − 𝛼 0.0 = 𝛼
Probability-equivalence
w -10,000 0 10,000 30,000 60,000
0.35 0.60 0.85
u(w) 0.0 0.35 0.60 0.85 1.0
60,000
Choice
-10,000
w
𝑢 30,000 = 𝛼𝑢 60,000 + 1 − 𝛼 𝑢 −10,000
= 𝛼 1.0 + 1 − 𝛼 0.0 = 𝛼
Utility Curve
0
0.2
0.4
0.6
0.8
1
1.2
-10000 0 10000 20000 30000 40000 50000 60000
Uti
lity
Payoff ($)
Certainty-equivalence
x 60,000
y -10,000
w w1
u(w)
x 0.5
Choice
y
w
𝑢 𝑤1 = 0.5𝑢 60,000 + 1 − 0.5 𝑢 −10,000
= 0.5 1.0 + 0.5 0.0 = 0.5
Certainty-equivalence
x 60,000 w1
y -10,000 -10,000
w w1 w2
u(w) 0.5
x 0.5
Choice
y
w
𝑢 𝑤2 = 0.5𝑢 𝑤1 + 1 − 0.5 𝑢 −10,000
= 0.5 0.5 + 0.5 0.0 = 0.25
Certainty-equivalence
x 60,000 w1 60,000 60,000 w3 w1 w2
y -10,000 -10,000 w1 w3 w1 w2 -10,000
w w1 w2 w3
u(w) 0.5 0.25 0.75
x 0.5
Choice
y
w
𝑢 𝑤3 = 0.5𝑢 60,000 + 1 − 0.5 𝑢 𝑤1
= 0.5 1.0 + 0.5 0.5 = 0.75
Certainty-equivalence
x 60,000 w1 60,000 60,000 w3 w1 w2
y -10,000 -10,000 w1 w3 w1 w2 -10,000
w w1 w2 w3
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Certainty-equivalence
x 60,000 8,000 60,000 60,000 w3 8,000 w2
y -10,000 -10,000 8,000 w3 8,000 w2 -10,000
w 8,000 w2 w3
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Certainty-equivalence
x 60,000 8,000 60,000 60,000 w3 8,000 2,000
y -10,000 -10,000 8,000 w3 8,000 2,000 -10,000
w 8,000 -2,000 w3
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Certainty-equivalence
x 60,000 8,000 60,000 60,000 20,000 8,000 2,000
y -10,000 -10,000 8,000 20,000 8,000 2,000 -10,000
w 8,000 -2,000 20,000
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Certainty-equivalence
x 60,000 8,000 60,000 60,000 20,000 8,000 2,000
y -10,000 -10,000 8,000 20,000 8,000 2,000 -10,000
w 8,000 -2,000 20,000 32,000
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Certainty-equivalence
x 60,000 8,000 60,000 60,000 20,000 8,000 2,000
y -10,000 -10,000 8,000 20,000 8,000 2,000 -10,000
w 8,000 -2,000 20,000 32,000 12,000 4,000 -5,000
u(w) 0.5 0.25 0.75 0.875 0.625 0.375 0.125
x 0.5
Choice
y
w
Utility Curve
0
0.2
0.4
0.6
0.8
1
1.2
-10000 0 10000 20000 30000 40000 50000 60000
Uti
lity
Payoff ($)
Methods of Elicitation
Preference comparison • [xi, i, yi] Ri wi
Probability equivalence • [xn+1, i, x0] ~ xi
Value equivalence
Certainty equivalence • [x*, 0.5, x0] ~ x1, [x1, x0] ~ x2, [x*, x1] ~ x3,…
Utility Functions
There are functions that describe smooth utility
curves
• Compact expressions
• These are often easier to elicit than a lot of
individual points
Common
• Linear
• Exponential
• Logarithmic
Exponential Utility
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1
Y-Values Kernel
• 𝑒−𝑐𝑥
Risk attitude
• c>0, risk averse
• c<0, risk seeking
• constant
Logarithmic Utility
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1
Y-Values Kernel
• log (𝑥 + 𝑏)
• x > b
Risk attitude
• risk averse
• declining
Scaling
Utility functions can be scaled to the
interval {0,1}
• Linear transformation
𝑢 𝑥 =𝑘 𝑥 −𝑘(𝑥0)
𝑘 𝑥1 −𝑘(𝑥0)
Multi-attribute Utility
What if there is more than one
objective?
Most commonly
• Assume mutual utility independence
• Develop utilities separately
• Combine into single expression
Goodwin & Wright (2004:123ff)
Cognitive Challenges
Lotteries are imaginary
Subtleties of elicitation
• Gift, purchase, sale, transfer
Strength of preference for sure
outcomes vs. attitudes toward risk
Recommendations
Pre-analysis preparation phase
• Motivate decision maker to think
carefully about responses
Use more than one assessment
procedure
Phrase utility questions in terms
closely related to original problem