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The Effect of Emotions on Economic Decision-Making MAS 630: Affective Computing Javier Hernandez...
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The Effect of Emotions on Economic Decision- Making MAS 630: Affective Computing Javier Hernandez Rivera [email protected]
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Transcript of The Effect of Emotions on Economic Decision-Making MAS 630: Affective Computing Javier Hernandez...
The Effect of Emotions on Economic Decision-Making
MAS 630: Affective ComputingJavier Hernandez Rivera
2
Contents• Motivation & Project Goals• Background• Experimental Setting• Data Synchronization & Visualization• Preliminary Data Analysis• Conclusions
Motivation&
Project Goals
3
4
Affect in Decision MakingEmotions have been long neglected in decision making (DM) in favor of a deliberative and reason-based decision making
Why? Affect can lead us to irrational decision making (ignoring the odds or negative consequences)
Playing the lottery
Smoking
Flying by plane
(Shafir, Simonson, & Tversky, 1993)
Happy
Relaxed
Fearful
Makes People
Feel
5
Project GoalsWhat?• Validate current economic DM theories (e.g.,Somatic
Marker Hypothesis) in different settings• Understand how negative emotions (fear and anger)
affect the DM process
How?• Emotion elicitation• Two-armed Bandit task• Electrodermal activity (EDA)
Why?• Understand the role of emotions in DM• Explore the benefits and limitations of most common
emotional responses to catastrophes
Background
6
7
Roles of Emotions in Decision Making
3) Encode and recall information
(Peters E., Vastfjall D., Garling T. & Slovic P, 2006)
1) Minimize negative emotions
2) Emotions as common currency
4) Motivator of information processing and behavior
vs
Positive Negative
8
Factors that Influence Decision Making
Uncertainty2,3
Sad8
Sexual Arousal5
Time1
Risk3,4
Ownership
Hunger6
Visceral States
Relaxed7
Disgusted8
6(Read & Leeuwen, 1998)8(Lerner, Small & Loewenstein, 2004)
Perc
eive
dva
lue
time
1 (Lowenstein, 1992)
7(Pham, Hung, Gorn, 2011)
2(Bar-Anan., Wilson & Gilbert , 2009) 4(MacGregor et al., 2005)
3(Lerner, & Tiedens, 2006)5(Ariely & Loewenstein, 2006)
9
Decision Making and PhysiologySomatic Marker Hypothesis (SMH)
A B C D
Disadvantageous decksLead to overall loss
Risky option (high variance)
Advantageous decksLead to overall gain
Safe option (low variance)
(Bechara A., Damasio H., & Tranel D. 1991, 1997)
Observation: Higher EDA responses before choosing risky and disadvantageous options, even before people could consciously identify the risky decks.
x 100 Trials
Theory: Physiological responses (a.k.a. somatic markers), learned in daily life activity, consciously or unconsciously influence the decision-making process.
Experiment: Iowa Gambling task
10
Anger and Fear
Anger FearUncertainty
UncontrolledCertaintyControl
Risk-seekingOptimistic assessments
Appraisal to negative events1
Influence on Decision Making1
1(Lerner and Keltner, 2000,2001) 2(Lerner, Dahl, Hariri & Taylor, 2006)
Risk-aversePessimistic assessments
PhysiologycalResponses2
Low High
Most common emotional reactions after catastrophic events such as the terrorist attacks of 9/11 or the economical crisis
Experimental Setting
11
Designed & conducted by Hyungil Ahn(Ahn, 2010)
Experimental Setting
Safe option (low variance)
is better
Option 1Option 2
Risky option (high variance)
is better
Fear
Anger
Bet Money
Neutral Gain
Loss
x 25 Trials
Bet Money
x 25 Trials
Option 1Option 2
Emotions Ownership Risk + Uncertainty
+- +-
Domain 1 Domain 2
Experimental Setting: 1 Trial
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2
3
4 5 6
EDA
Time
1 2 3 4 5
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Data Synchronization&
Visualization
14
15
Data Synchronization
EDA(20 Hz)
Surveys
Task Activity
20 participants were excluded because of missing information
15 participants were excluded because of corrupted signals
(artifacts, low response)
Number of Participants
Neutral Anger Fear
Gain 3 3 5
Loss 4 5 5
Neutral Anger Fear
Safe 7 8 10
Risky 7 8 10
Fram
es
Best
Opti
on
25 participants
2 sessions
x = 1250trials
FilteringLoss-pass
filter(0.16 Hz cutoff
frequency)
NormalizationScale each
subject between 0 and 1
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Data Visualization (Neutral)
Data in
Risky Option is Better Safe Option is BetterVideoVideo
GainFrame
(3 participants)
LossFrame
(4 participants)
Neutral
7 participants(350 trials)
N3)(N3N2)(N2N1)(N1
........................
...............................
.
B)(B S)(S
2
1 22 22 11 11 11 11 12 11 11 12 11 11 12 11 11 11 11 11 11 11 11 21 11 11
N3)(N3N2)(N2N1)(N1(N
...............
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B)(B S)(S1
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B)(B S)(S1
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..
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B)(B S)(S
1
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21 11 1
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N3)(N3N2)(N2N1)(N1(N.
...
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.
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B)(B S)(S
2 22
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N3)(N3N2)(N2N1)(N1(N
...........................
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..
.
.
..
.....
.
B)(B S)(S
21
12
2
11 22
12
22 12 21 11 21 11 11 11 11 11 12 12 12 12 12 12 11 21
21 2
N3)(N3N2)(N2N1)(N1(N
..................................................
.
..
.
.
.
B)(B S)(S
111 22 22 22 11
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1
Selected Options(‘1’ is always
the optimal selection)
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Data Visualization (Anger)
Data in
Risky Option is Better
Safe Option is Better VideoVideo
GainFrame
(3 participants)
LossFrame
(5 participants)
Anger8 participants
(400 trials)
.
...
.........
...
.........
........................
.....
.
.
A3)(A3A2)(A2A1)(A1B)(B S)(S
22
21 12 22 22 11 12 22 21
12
21 22
11
22
21
22 1
21
12
2
2
12
1
2
2
1
11
2
2
1
.........................
.........................
......
A3)(A3A2)(A2A1)(A1B)(B S)(S
1
2
11
11
2
1
1
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22
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111
22
2 11 12 12 22 11 12 11 11 22 11 11 12 21 1
........................
...............
..........
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..
A3)(A3A2)(A2A1)(A1B)(B S)(S
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1
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1
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1
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21 21 21 21 21 2
..................................................
.
. A3)(A3A2)(A2A1)(A1B)(B(S
2 11 21 11 22 11 12 21 11 12 12 21 12 12 21 12 22 11 11 11 22 12 11 22 11 1
..................
..........
.......
.
....
..
..
..
..
..
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..A3)(A3A2)(A2A1)(A1B)(B(S
1 12 22 11 1
1
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211 1
1 1
112 2
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1
1
1
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2
1
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22
.....
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.
A3)(A3A2)(A2A1)(A1B)(B(S
2
1112
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A3)(A3A2)(A2A1)(A1B)(B S)(S
12 11 11 12 11 11 12 12 11 11 12 11 11 11 12 11 11 12 11 11 12 11 12
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.
......
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A3)(A3A2)(A2A1)(A1B)(B S)(S
2
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Data Visualization (Fear)
Data in
F3)(F3F2)(F2F1)(F1
......
.......
.
.......
..
..................
.........
......
B)(B S)(S
1
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F3)(F3F2)(F2F1)(F1
..............................
..
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.
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.
B)(B S)(S
22 21 21 21
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F3)(F3F2)(F2F1)(F1
..................................................
....
.
. B)(B S)(S
2 11 12 11 11 12 21 22 22 12 11 11 11 11 11 12 11 11 11 12 12 11 11 11 11 1
F3)(F3F2)(F2F1)(F1
.
..
......
............
..
..
...............................
B)(B(S
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F3)(F3F2)(F2F1)(F1
.........................
...............................
B)(B S)(S
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2
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F3)(F3F2)(F2F1)(F1
..............................................
....
......
B)(B S)(S 12
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F3)(F3F2)(F2F1)(F1
..................................................
....
.. B)(B S)(S
112 21 21 11 11 21 21 22 12 21 11 11 11 11 11 11 12 11 11 12 11 11 11 11 1
F3)(F3F2)(F2F1)(F1
..................................................
......
B)(B S)(S
21 12 21 21 22 12 22 11 21 21 11 21
11 21 21 21 21 21 22 21 21 11 11 21 21
F3)(F3F2)(F2F1)(F1
.....................................
..................
.
B)(B S)(S
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F3)(F3F2)(F2F1)(F1
..................................................
......
B)(B S)(S
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Risky Option is BetterSafe Option is Better VideoVideo
GainFrame
(5 participants)
LossFrame
(5 participants)
Fear10 participants
(500 trials)
PreliminaryData Analysis
19
20
Behavioral Responses: Speed
Aver
age
Tria
l Re
spon
se T
ime
(sec
)Neutral
(N = 350)Anger
(N = 399)
0.5 1 1.5 2 2.5 3 3.50
5
10
15
20
25
Fear(N = 500)
People answer significantly faster in the negative emotional states, and fearful people are significantly faster than angry people.
Standard Error of the Mean (SEM)
Betting
Trial
EDA
Time
1 2 3 4 5 6
Surveys
* Statistically Significant (Two Sample T-Test)
* *
21
Advantageous Disadvantageous
Neutral FearAnger
Overall, people in the three emotional conditions perform similarly.
Negative states are slightly better when the safe option is the optimal one, but they are slightly worse when the risky option is the optimal one. Fearful people tend to perform slightly better than angry people
Neutral Anger Fear0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Emotions - Median Anticipatory Signal
Low High0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Start Outcome - Median Anticipatory Signal
D1 (Low risk is better) D2 (High risk is better)0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Domains - Median Anticipatory Signal
AdvantageousDisadvantageous
Neutral Anger Fear0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Emotions - Median Anticipatory Signal
Low High0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Start Outcome - Median Anticipatory Signal
D1 (Low risk is better) D2 (High risk is better)0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Domains - Median Anticipatory Signal
AdvantageousDisadvantageous
Safe Option
Is Better
Risky Option is
Better
Behavioral Responses: Performance%
of
Sele
ction
s
**
**** Statistically Significant (Two Sample T-Test)
22
Behavioral Responses: Risk Preference
Neutral Anger Fear0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Emotions - Median Anticipatory Signal
Low High0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
% R
isky
Start Outcome - Median Anticipatory Signal
D1 (Low risk is better) D2 (High risk is better)0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Domains - Median Anticipatory Signal
Non-RiskyRisky
Non-Risky Option Risky Option
Neutral Anger Fear0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Emotions - Median Anticipatory Signal
Low High0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
% R
isky
Start Outcome - Median Anticipatory Signal
D1 (Low risk is better) D2 (High risk is better)0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
% R
isky
Domains - Median Anticipatory Signal
Non-RiskyRisky
Neutral FearAnger
GainFrame
LossFrame
Although people in the neutral state significantly choose riskier options, people in the negative states prefer non-riskier options.
In the loss frame, people prefer the riskier options. The difference is significant for the neutral and fear settings.
% o
f Se
lecti
ons
* Statistically Significant (Two Sample T-Test)
*
*
*
*
N A F0
10
20
30
40
50
60
70
80
90
100
Pes
sim
isti
c vs
Op
timis
tic
N A F0
10
20
30
40
50
60
70
80
90
100
Pes
sim
isti
c vs
Op
timis
tic
N A F0
10
20
30
40
50
60
70
80
90
100
Pes
sim
isti
c vs
Op
timis
tic
N A F0
10
20
30
40
50
60
70
80
90
100
Pes
sim
isti
c vs
Op
timis
tic
Neutral FearAnger Neutral FearAngerAver
age
of P
leas
antn
ess
Ratin
gs o
n th
e O
utco
mes
Aver
age
of th
e %
of
Adva
ntag
eous
Sel
ectio
ns
Gain Frame Loss Frame
Angry people in the loss frame perform slightly better than angry people in the gain frame.
As expected, the overall pleasantness ratings on the outcomes are slightly lower in the loss frame. Moreover, angry people are surprisingly unpleased even though they obtained slightly higher outcomes.
Behavioral Responses: Pleasantness
24
Preprocessing for EDA Analysis
EDA
FilteringLoss-pass
filter(0.16 Hz cutoff
frequency)
NormalizationScale each
subject between 0 and 1*
Baseline RemovalSmoothed
Minimum Sliding Window over 10
minutes
*(Lykken, D.T., Venables, P.H, 1971)
5 10 15 20 25 30 35 400
0.1
0.2
5 10 15 20 25 30 35 400
0.1
0.2
0.3
5 10 15 20 25 30 35 400
0.1
0.2
5 10 15 20 25 30 35 400
0.1
0.2
0.3
Minutes
µS
Original SignalLow-pass filtered signalBaselineCorrected signal
Feature Extraction
Normalized Area under the
Curve
Anticipatory Responses: SMHIowa Gambling Task
AdvantageousDisadvantageous
Two-Armed Bandit Task
The SMH hypothesis (higher EDA responses before disadvantageous selections) seems plausible when the Safe Option is optimal and it might be delayed when the Risky Option is the optimal one.
Tota
l # S
elec
tions
Aver
age
Activ
ation
1-8 9-17 18-250
1
2
x 10-4 N (n: 625)
TrialsA
CT
IV
1-8 9-17 18-250
1
2
3
4
5
6
7
8
9N
Trials
Tota
l # o
f O
ptio
ns
1-8 9-17 18-250
1
2
x 10-4 N (n: 625)
Trials
AC
TIV
1-8 9-17 18-250
1
2
3
4
5
6
7
8
9N
Trials
Tot
al
# o
f O
ptio
ns
1-8 9-17 18-250
1
2
x 10-4 N (n: 625)
Trials
AC
TIV
1-8 9-17 18-250
1
2
3
4
5
6
7
8
9N
Trials
Tot
al
# o
f O
ptio
ns
Safe Option Is Better Risky Option is BetterSafe Option Is Better
Trials1-8 9-17 18-25 1-8 9-17 18-25
1-8 9-17 18-250
1
2
x 10-4 N (n: 625)
Trials
AC
TIV
1-8 9-17 18-250
1
2
3
4
5
6
7
8
9N
Trials
Tota
l # o
f O
ptio
ns
Pre-Punishment
Pre-Hunch Hunch Conceptual
Period *
* Statistically Significant
* * *
26
Main Limitations of the Analysis
0 5 10 15 20 25
InitTrial(0.00 sec)
BetClick(1.86 sec)
SelectOption(1.65 sec)
GetOutcome(0.64 sec)
AnswerExperience(6.28 sec)
AnswerConfidence(4.15 sec)
AnswerPrediction(5.66 sec)1 2 3 4 5 6
Betting~4 sec.
AnsweringSurveys~16 sec.
Average EDA response
(N: 1250 trials)
Too short to display
anticipatory responses?
Cognitive load of the first survey?
1) Reduced number of participants (35 part. were excluded)
2) Consecutive tasks distort EDA responses
27
Conclusions• People in the negative states bet faster than
people in the neutral state.• Fearful people bet faster and performed slightly
better than angry people.• Although most of the people preferred riskier
options, angry and fearful people in the gain frame preferred safer options.
• Angry people performed slightly better in the loss frame.
• Angry people were less pleased in the loss frame even though they obtained relatively higher outcomes.
• Although the SMH seemed plausible in the Two-armed Bandit Task, further analysis is required.
Readings
Data Synchronization
Deliverables
Data Analysis
Time Distribution
28
References IAhn, H.I. (2010). Modeling and Analysis of Affective Influences on Human Experience, Prediction,
Decision Making, and Behavior. MIT PhD Thesis. Ariely D., & Loewenstein G. (2006). The Heat of the Moment: The Effect of Sexual Arousal on Sexual
Decision Making. J. Behav. Dec. Making, (19), 87-98Bar-Anan Y., Wilson T & Gilbert (2009) . The Feeling of Uncertainty Intensities Affective Reactions.
Emotion 9, (1), 123-127Bechara A., Damasio H., & Tranel D. (1997). Deciding Advantageously Before Knowing the
Advantageous Strategy. Science.Damasio, A. R., Tranel, D., & Damasio, H. (1991). Somatic Markers and the Guidance of Behavior:
Theory and Preliminary Testing. Lerner, J. S., Dahl, R. E., Hariri, A. R., & Taylor, S. E. (2007). Facial Expressions of Emotion Reveal
Neuroendocrine and Cardiovascular Stress Responses. Biol Psychiatry; 61:,253-260Lerner, J. S., & Keltner, D. (2000). Beyond Valence: Toward a Model of Emotion-specific Influences
on Judgment and Choice. Cognition and Emotion, 14(4), 473–493.Lerner, J. S., & Keltner, D. (2001). Fear, Anger, and Risk. Journal of Personality and Social Psychology,
81(1), 146–159.Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart Strings and Purse Strings: Effects of
Emotions on Economic Transactions. Psychological Science, 15, 337–341.
29
References IILoewenstein, G. & Prelec, D. (1992). Anomalies in Intertemporal Choice: Evidence and an
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